DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Claim Objections
Claims 8-9 are objected to because of the following informalities:
In claim 8, line 2-3, the term “executed by the processor, wherein when the program” should be changed to “executed by the processor; wherein when the computer program” in order to avoid a sentence run-on and maintain consistency and clarity through the claims.
In claim 9, line 1-2, the term “sorting a computer program, wherein when the computer program” should be changed to “sorting a computer program; wherein when the computer program” in order to avoid typographical issue and sentence run-on.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim 7 recites limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 7; recites the limitation, “a data acquisition module configured to…..” [Line 2].
Claim 7; recites the limitation, “a plot segmentation module configured to…..” [Line 4].
Claim 7; recites the limitation, “a grid module configured to …...” [Line 6].
Claim 7; recites the limitation, “a uniformity module configured to……,” [Line 9].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claim 7;
(i) “data acquisition module” (Fig. 2, #210. Paragraph [0100-0101]- FIG. 2 is a schematic structural view of an apparatus for calculating a plant height uniformity of a crop population according to the present disclosure. As shown in FIG. 2, the apparatus for calculating a plant height uniformity of a crop population includes: a data acquisition module 210, a plot segmentation module 220, a grid module 230, and a uniformity module 240. The data acquisition module 210 is configured to acquire 3D point cloud data of a target crop population. The data acquisition module is illustrated in Fig. 2, as a black box #210, thus does not have sufficient structure or material associated with it.).
(ii) “plot segmentation module” (Fig. 2, #220. Paragraph [0100 and 0102]- FIG. 2 is a schematic structural view of an apparatus for calculating a plant height uniformity of a crop population according to the present disclosure. As shown in FIG. 2, the apparatus for calculating a plant height uniformity of a crop population includes: a data acquisition module 210, a plot segmentation module 220, a grid module 230, and a uniformity module 240. The plot segmentation module 220 is configured to segment the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot. The plot segmentation module is illustrated in Fig. 2, as a black box #220, thus does not have sufficient structure or material associated with it.).
(iii) “grid module” (Fig. 2, #230. Paragraph [0100 and 0103]- FIG. 2 is a schematic structural view of an apparatus for calculating a plant height uniformity of a crop population according to the present disclosure. As shown in FIG. 2, the apparatus for calculating a plant height uniformity of a crop population includes: a data acquisition module 210, a plot segmentation module 220, a grid module 230, and a uniformity module 240. The grid module 230 is configured to determine target crop grids according to the 3D point cloud data of each target crop plot, a number of target crop grids being the same as a number of target crop plants in each target crop plot. The sending module is illustrated in Fig. 2, as a black box #230, thus does not have sufficient structure or material associated with it.).
(iv) “uniformity module” (Fig. 2, #240. Paragraph [0100 and 0104]- FIG. 2 is a schematic structural view of an apparatus for calculating a plant height uniformity of a crop population according to the present disclosure. As shown in FIG. 2, the apparatus for calculating a plant height uniformity of a crop population includes: a data acquisition module 210, a plot segmentation module 220, a grid module 230, and a uniformity module 240. The uniformity module 240 is configured to determine a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid. The uniformity module is illustrated in Fig. 2, as a black box #240, thus does not have sufficient structure or material associated with it.).
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 7 along with its dependent claims are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 7 limitations:
Claim 7; recites the limitation, “a data acquisition module configured to…..” [Line 2].
Claim 7; recites the limitation, “a plot segmentation module configured to…..” [Line 4].
Claim 7; recites the limitation, “a grid module configured to …...” [Line 6].
Claim 7; recites the limitation, “a uniformity module configured to……,” [Line 9].
Claim 7 respectively invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification is devoid of adequate structure to perform the claimed functions. The specification does not provide sufficient details such that one of the ordinary skill in the art would understand which structure performed(s) the claimed function.
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 7 along with its dependent claims are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. As described above, the disclosure does not provide adequate structure to perform the claimed function in the recited limitation.
Claim 7; recites the limitation, “a data acquisition module configured to…..” [Line 2].
Claim 7; recites the limitation, “a plot segmentation module configured to…..” [Line 4].
Claim 7; recites the limitation, “a grid module configured to …...” [Line 6].
Claim 7; recites the limitation, “a uniformity module configured to……,” [Line 9].
The specification does not demonstrate that applicant has made an invention that achieves the claimed function because the invention is not described with sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor had possession of the claimed invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over BALDWIN et al. (US 20240032454 A1), hereinafter referenced as BALDWIN, in view of BAINBRIDGE et al. (US 20230292647 A1), hereinafter referenced as BAINBRIDGE.
Regarding claim 1, BALDWIN explicitly teaches a method for calculating a plant height uniformity of a crop population (Fig. 3. Paragraph [0020]-BALDWIN discloses the systems and methods herein leverage specific scan data to determine height models for plots (e.g., for crops growing in the plots, etc.).), comprising following steps:
acquiring three-dimensional (3D) point cloud data of a target crop population (Fig. 1, illustrates a target crop population (wherein Field #106 is the target crop population). Paragraph [0058]-BALDWIN discloses the method 300 includes capturing, at 302, by the scanning device 110, scan data from the field 106, where the scan data, in this embodiment, includes LiDAR data indicative of surfaces (and/or surface features) of the field 106 (e.g., ground and ground features, vegetation, etc.). Further in paragraph [0060]-BALDWIN discloses the scan data may include a series of point cloud data, which includes point data for the individual scans of the event.);
segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot (Fig. 1 and 4, illustrate target crop plots. Paragraph [0061-0062]-BALDWIN disclose it should be appreciated that the scan data, as described herein, may be processed for the field 106 (or multiple fields) or separately for the plots 108a-d (e.g., for a single plot, for multiple plots, etc.). Regardless, the plot specific data is leveraged as described in more detail below. In addition, at 307, the computing device 102 classifies the data in the composite data set, for example, where each of the points is classified as ground or other (e.g., vegetation hits, canopy hits, outliers, etc.) and, in some embodiments, further assessed for quality (as generally described above in the system 100) (wherein processing the data separately for the plots is segmenting the 3D point cloud data for each target crop plot).);
determining a plant height uniformity of each target crop plot (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106). Further in paragraph [0063]-BALDWIN discloses the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel. That said, it should be appreciated that the CHM may be derived from the composite data set in other ways in other embodiments, which may rely, for example, on row detection, planter data (e.g., row/plant location, etc.), etc. (wherein the plant height uniformity is the canopy height model).) according to 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0061]-BALDWIN discloses at 306, the computing device 102 aligns the scan data to form a composite data set (wherein the composite data set is 3D point cloud data of the target crop grid).).
BALDWIN fails to explicitly teach determining target crop grids according to the 3D point cloud data of each target crop plot, a number of target crop grids being the same as a number of target crop plants in each target crop plot; and.
However, BAINBRIDGE explicitly teaches determining target crop grids (Fig. 3. Paragraph [0124]-BAINBRIDGE discloses the crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes (wherein a target crop grid is a 3D point).) according to the 3D point cloud data of each target crop plot (Fig. 3. Paragraph [0108]-BAINBRIDGE discloses the AOI may be a whole field or a region of a field (wherein a region of a field is a target crop plot). Further in paragraph [0124]-BAINBRIDGE discloses the crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes (wherein a target crop grid is a 3D point).),
a number of target crop grids being the same as a number of target crop plants in each target crop plot (Fig. 3. Paragraph [0124]-BAINBRIDGE discloses in step 160, a spatially resolved model of the crops in the AOI is generated based on the identified crop features and attributes. The crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes (wherein a target crop grid is a 3D point).); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of BAINBRIDGE of determining target crop grids according to the 3D point cloud data of each target crop plot, a number of target crop grids being the same as a number of target crop plants in each target crop plot; and.
Wherein having BALDWIN’s method of determining crop height having determining target crop grids according to the 3D point cloud data of each target crop plot, a number of target crop grids being the same as a number of target crop plants in each target crop plot; and.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and BAINBRIDGE relate to measuring and monitoring crop fields to generate models based on crop data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while BAINBRIDGE there is therefore a need for an automated system and method of crop monitoring for more efficient crop monitoring, accurate crop yield predictions, and highly targeted interventions up to a plant-by-plant level. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and BAINBRIDGE et al. (US 20230292647 A1), Paragraph [0006].
Regarding claim 7, BALDWIN explicitly teaches an apparatus for calculating a plant height uniformity of a crop population (Fig. 2. Paragraph [0020]-BALDWIN discloses the systems and methods herein leverage specific scan data to determine height models for plots (e.g., for crops growing in the plots, etc.). Further in paragraph [0050]-BALDWIN discloses the example computing device 200 includes a processor 202 and a memory 204 coupled to (and in communication with) the processor 202.), comprising:
a data acquisition module configured to acquire three-dimensional (3D) point cloud data of a target crop population (Fig. 1, illustrates a target crop population (wherein Field #106 is the target crop population). Paragraph [0058]-BALDWIN discloses the method 300 includes capturing, at 302, by the scanning device 110, scan data from the field 106, where the scan data, in this embodiment, includes LiDAR data indicative of surfaces (and/or surface features) of the field 106 (e.g., ground and ground features, vegetation, etc.). Further in paragraph [0060]-BALDWIN discloses the scan data may include a series of point cloud data, which includes point data for the individual scans of the event.);
a plot segmentation module configured to segment the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot (Fig. 1 and 4, illustrate target crop plots. Paragraph [0061-0062]-BALDWIN disclose it should be appreciated that the scan data, as described herein, may be processed for the field 106 (or multiple fields) or separately for the plots 108a-d (e.g., for a single plot, for multiple plots, etc.). Regardless, the plot specific data is leveraged as described in more detail below. In addition, at 307, the computing device 102 classifies the data in the composite data set, for example, where each of the points is classified as ground or other (e.g., vegetation hits, canopy hits, outliers, etc.) and, in some embodiments, further assessed for quality (as generally described above in the system 100) (wherein processing the data separately for the plots is segmenting the 3D point cloud data for each target crop plot).);
a uniformity module configured to determine a plant height uniformity of each target crop plot (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106). Further in paragraph [0063]-BALDWIN discloses the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel. That said, it should be appreciated that the CHM may be derived from the composite data set in other ways in other embodiments, which may rely, for example, on row detection, planter data (e.g., row/plant location, etc.), etc. (wherein the plant height uniformity is the canopy height model).) according to 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0061]-BALDWIN discloses at 306, the computing device 102 aligns the scan data to form a composite data set (wherein the composite data set is 3D point cloud data of the target crop grid).).
BALDWIN fails to explicitly teach a grid module configured to determine target crop grids according to the 3D point cloud data of each target crop plot, a number of target crop grids being the same as a number of target crop plants in each target crop plot; and
However, BAINBRIDGE explicitly teaches a grid module configured to determine target crop grids (Fig. 3. Paragraph [0124]-BAINBRIDGE discloses the crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes (wherein a target crop grid is a 3D point).) according to the 3D point cloud data of each target crop plot (Fig. 3. Paragraph [0108]-BAINBRIDGE discloses the AOI may be a whole field or a region of a field (wherein a region of a field is a target crop plot). Further in paragraph [0124]-BAINBRIDGE discloses the crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes (wherein a target crop grid is a 3D point).),
a number of target crop grids being the same as a number of target crop plants in each target crop plot (Fig. 3. Paragraph [0124]-BAINBRIDGE discloses in step 160, a spatially resolved model of the crops in the AOI is generated based on the identified crop features and attributes. The crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes (wherein a target crop grid is a 3D point).); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN of an apparatus for calculating a plant height uniformity of a crop population, comprising: a data acquisition module configured to acquire three-dimensional (3D) point cloud data of a target crop population; a plot segmentation module configured to segment the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; a uniformity module configured to determine a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of BAINBRIDGE of a grid module configured to determine target crop grids according to the 3D point cloud data of each target crop plot, a number of target crop grids being the same as a number of target crop plants in each target crop plot; and.
Wherein having BALDWIN’s apparatus for determining crop height having a grid module configured to determine target crop grids according to the 3D point cloud data of each target crop plot, a number of target crop grids being the same as a number of target crop plants in each target crop plot; and
The motivation behind the modification would have been to obtain apparatus for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and BAINBRIDGE relate to measuring and monitoring crop fields to generate models based on crop data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while BAINBRIDGE there is therefore a need for an automated system and method of crop monitoring for more efficient crop monitoring, accurate crop yield predictions, and highly targeted interventions up to a plant-by-plant level. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and BAINBRIDGE et al. (US 20230292647 A1), Paragraph [0006].
Regarding claim 8, BALDWIN in view of BAINBRIDGE explicitly teach the method for calculating a plant height uniformity of a crop population according to claim 1 is implemented.
BALDWIN further explicitly teaches an electronic device (Fig. 2, #200 called computing device. Paragraph [0050]-BALDWIN discloses the example computing device 200 includes a processor 202 and a memory 204 coupled to (and in communication with) the processor 202.), comprising
a memory (Fig. 2, #202 called processor. Paragraph [0050]-BALDWIN discloses the example computing device 200 includes a processor 202 and a memory 204 coupled to (and in communication with) the processor 202.),
a processor (Fig. 2, #204 called memory. Paragraph [0050]-BALDWIN discloses the example computing device 200 includes a processor 202 and a memory 204 coupled to (and in communication with) the processor 202.), and
a computer program stored in the memory and executable on the processor (Fig. 2. Paragraph [0077]-BALDWIN discloses it should be appreciated that the functions described herein, in some embodiments, may be described in computer executable instructions stored on a computer readable media, and executable by one or more processors. The computer readable media is a non-transitory computer readable media. By way of example, and not limitation, such computer readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.),
wherein when the program is executed by the processor (Fig. 2. Paragraph [0077]-BALDWIN discloses it should be appreciated that the functions described herein, in some embodiments, may be described in computer executable instructions stored on a computer readable media, and executable by one or more processors.),
Regarding claim 9, BALDWIN in view of BAINBRIDGE explicitly teach the method for calculating a plant height uniformity of a crop population according to claim 1 is implemented.
BALDWIN further explicitly teaches a non-transitory computer-readable storage medium (Fig. 2, #204 called memory. Paragraph [0077]-BALDWIN discloses it should be appreciated that the functions described herein, in some embodiments, may be described in computer executable instructions stored on a computer readable media, and executable by one or more processors. The computer readable media is a non-transitory computer readable media.), storing a computer program (Fig. 2. Paragraph [0077]-BALDWIN discloses by way of example, and not limitation, such computer readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.),
wherein when the computer program is executed by a processor (Fig. 2. Paragraph [0077]-BALDWIN discloses it should be appreciated that the functions described herein, in some embodiments, may be described in computer executable instructions stored on a computer readable media, and executable by one or more processors.),
Claims 2, 11, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over BALDWIN et al. (US 20240032454 A1), hereinafter referenced as BALDWIN, in view of BAINBRIDGE et al. (US 20230292647 A1), hereinafter referenced as BAINBRIDGE, and further in view of SELVIAH et al. (US 20200043186 A1), hereinafter referenced as SELVIAH, and further in view of VARMA BHUPATIRAJU et al. (US 20230094371 A1), hereinafter referenced as VARMA.
Regarding claim 2, BALDWIN in view of BAINBRIDGE explicitly teach the method for calculating a plant height uniformity of a crop population according to claim 1,
BALDWIN further explicitly teaches after the acquiring 3D point cloud data of a target crop population (Fig. 3, #302 called capture scan data. Paragraph [0058]-BALDWIN discloses the method 300 includes capturing, at 302, by the scanning device 110, scan data from the field 106, where the scan data, in this embodiment, includes LiDAR data indicative of surfaces (and/or surface features) of the field 106 (e.g., ground and ground features, vegetation, etc.). Further in paragraph [0060]-BALDWIN discloses the scan data may include a series of point cloud data, which includes point data for the individual scans of the event.), further comprising a preprocessing step (Fig. 3, #307 called classify aligned data. Paragraph [0062]), specifically:
determining a ground point cloud according to the 3D point cloud data of the target crop population (Fig. 3, #307 called classify aligned data. Paragraph [0062]-BALDWIN discloses at 307, the computing device 102 classifies the data in the composite data set, for example, where each of the points is classified as ground (wherein points classified as ground are a ground point cloud).);
BALDWIN fails to explicitly teach determining a normal direction of the ground point cloud as a reference direction, and.
However, BAINBRIDGE explicitly teaches determining a normal direction of the ground point cloud as a reference direction (Fig. 3. Paragraph [0124]-BAINBRIDGE discloses the crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes. The Z-component of each 3D point may be relative. Relative Z can be determined via the drone using an on-board range sensor (e.g. LIDAR) or altitude sensor (e.g. barometer), if available (wherein the Z-component is a reference direction).), and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of BAINBRIDGE of determining a normal direction of the ground point cloud as a reference direction, and.
Wherein having BALDWIN’s method of determining crop height having determining a normal direction of the ground point cloud as a reference direction, and.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and BAINBRIDGE relate to measuring and monitoring crop fields to generate models based on crop data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while BAINBRIDGE there is therefore a need for an automated system and method of crop monitoring for more efficient crop monitoring, accurate crop yield predictions, and highly targeted interventions up to a plant-by-plant level. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and BAINBRIDGE et al. (US 20230292647 A1), Paragraph [0006].
Although BALDWIN in view of BAINBRIDGE explicitly teach the 3D point cloud data of the target population, BALDWIN in view of BAINBRIDGE fail to explicitly teach rotating the 3D point cloud data of the target crop population, such that a direction of the 3D point cloud data of the target crop population is the same as the reference direction; and.
However, SELVIAH explicitly teaches rotating the 3D point cloud data of the target crop population (Fig. 3-4. Paragraph [0171]-SELVIAH discloses rotate the second point cloud by the stored angle of rotation about the respective one or more axes of rotation among the three orthogonal axes of rotation in the common coordinate system (wherein the second point cloud is the 3D point cloud data of the target crop population).), such that a direction of the 3D point cloud data of the target crop population is the same as the reference direction (Fig. 3. Paragraph [0173]-SELVIAH discloses the rotational alignment processor is configured to find at which angle of rotation the second set of vectors exhibits the best match with the first set of vectors (wherein the angle of rotation is the reference direction). Paragraph [0176]-SELVIAH discloses it may be that both the first 3D dataset and the rotated second 3D dataset are output, the output being the two datasets defined in a coordinate system in which they are rotationally aligned (wherein being rotationally aligned is having a point cloud in the same direction as the reference direction).); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of SELVIAH of rotating the 3D point cloud data of the target crop population, such that a direction of the 3D point cloud data of the target crop population is the same as the reference direction; and.
Wherein having BALDWIN’s method of determining crop height having rotating the 3D point cloud data of the target crop population, such that a direction of the 3D point cloud data of the target crop population is the same as the reference direction; and.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and SELVIAH relate to processing three-dimensional point clouds obtained by LIDAR, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while SELVIAH it is desirable to provide a reliable automated technique for the rotational alignment of overlapping 3D datasets. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and SELVIAH et al. (US 20200043186 A1), Paragraph [0005].
BALDWIN in view of BAINBRIDGE and further in view of SELVIAH fail to explicitly teach removing the ground point cloud in the 3D point cloud data of the target crop population.
However, VARMA explicitly teaches removing the ground point cloud in the 3D point cloud data of the target crop population (Fig. 11. Paragraph [0075]-VARMA FIG. 11 illustrates the 3D point cloud 552 after processing unit 50 has applied a ground filter. For example, a ground filter process may be applied to separate the ground and non-ground points such that the non-ground points are retained.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE and further in view of SELVIAH of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of VARMA of removing the ground point cloud in the 3D point cloud data of the target crop population.
Wherein having BALDWIN’s method of determining crop height having removing the ground point cloud in the 3D point cloud data of the target crop population.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and VARMA relate to detecting crop rows through the use of LIDAR, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while VARMA the example vehicle row follow systems, methods and mediums facilitate navigation of the vehicle along a centerline between consecutive plant rows. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and VARMA BHUPATIRAJU et al. (US 20230094371 A1), Paragraph [0021].
Regarding claim 11, BALDWIN in view of BAINBRIDGE explicitly teach the electronic device according to claim 8,
BALDWIN further explicitly teaches after the acquiring 3D point cloud data of a target crop population (Fig. 3, #302 called capture scan data. Paragraph [0058]-BALDWIN discloses the method 300 includes capturing, at 302, by the scanning device 110, scan data from the field 106, where the scan data, in this embodiment, includes LiDAR data indicative of surfaces (and/or surface features) of the field 106 (e.g., ground and ground features, vegetation, etc.). Further in paragraph [0060]-BALDWIN discloses the scan data may include a series of point cloud data, which includes point data for the individual scans of the event.), further comprising a preprocessing step (Fig. 3, #307 called classify aligned data. Paragraph [0062]), specifically:
determining a ground point cloud according to the 3D point cloud data of the target crop population (Fig. 3, #307 called classify aligned data. Paragraph [0062]-BALDWIN discloses at 307, the computing device 102 classifies the data in the composite data set, for example, where each of the points is classified as ground (wherein points classified as ground are a ground point cloud).);
BALDWIN fails to explicitly teach determining a normal direction of the ground point cloud as a reference direction, and.
However, BAINBRIDGE explicitly teaches determining a normal direction of the ground point cloud as a reference direction (Fig. 3. Paragraph [0124]-BAINBRIDGE discloses the crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes. The Z-component of each 3D point may be relative. Relative Z can be determined via the drone using an on-board range sensor (e.g. LIDAR) or altitude sensor (e.g. barometer), if available (wherein the Z-component is a reference direction).), and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of BAINBRIDGE of determining a normal direction of the ground point cloud as a reference direction, and.
Wherein having BALDWIN’s method of determining crop height having determining a normal direction of the ground point cloud as a reference direction, and.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and BAINBRIDGE relate to measuring and monitoring crop fields to generate models based on crop data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while BAINBRIDGE there is therefore a need for an automated system and method of crop monitoring for more efficient crop monitoring, accurate crop yield predictions, and highly targeted interventions up to a plant-by-plant level. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and BAINBRIDGE et al. (US 20230292647 A1), Paragraph [0006].
Although BALDWIN in view of BAINBRIDGE explicitly teach the 3D point cloud data of the target population, BALDWIN in view of BAINBRIDGE fail to explicitly teach rotating the 3D point cloud data of the target crop population, such that a direction of the 3D point cloud data of the target crop population is the same as the reference direction; and.
However, SELVIAH explicitly teaches rotating the 3D point cloud data of the target crop population (Fig. 3-4. Paragraph [0171]-SELVIAH discloses rotate the second point cloud by the stored angle of rotation about the respective one or more axes of rotation among the three orthogonal axes of rotation in the common coordinate system (wherein the second point cloud is the 3D point cloud data of the target crop population).), such that a direction of the 3D point cloud data of the target crop population is the same as the reference direction (Fig. 3. Paragraph [0173]-SELVIAH discloses the rotational alignment processor is configured to find at which angle of rotation the second set of vectors exhibits the best match with the first set of vectors (wherein the angle of rotation is the reference direction). Paragraph [0176]-SELVIAH discloses it may be that both the first 3D dataset and the rotated second 3D dataset are output, the output being the two datasets defined in a coordinate system in which they are rotationally aligned (wherein being rotationally aligned is having a point cloud in the same direction as the reference direction).); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of SELVIAH of rotating the 3D point cloud data of the target crop population, such that a direction of the 3D point cloud data of the target crop population is the same as the reference direction; and.
Wherein having BALDWIN’s method of determining crop height having rotating the 3D point cloud data of the target crop population, such that a direction of the 3D point cloud data of the target crop population is the same as the reference direction; and.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and SELVIAH relate to processing three-dimensional point clouds obtained by LIDAR, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while SELVIAH it is desirable to provide a reliable automated technique for the rotational alignment of overlapping 3D datasets. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and SELVIAH et al. (US 20200043186 A1), Paragraph [0005].
BALDWIN in view of BAINBRIDGE and further in view of SELVIAH fail to explicitly teach removing the ground point cloud in the 3D point cloud data of the target crop population.
However, VARMA explicitly teaches removing the ground point cloud in the 3D point cloud data of the target crop population (Fig. 11. Paragraph [0075]-VARMA FIG. 11 illustrates the 3D point cloud 552 after processing unit 50 has applied a ground filter. For example, a ground filter process may be applied to separate the ground and non-ground points such that the non-ground points are retained.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE and further in view of SELVIAH of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of VARMA of removing the ground point cloud in the 3D point cloud data of the target crop population.
Wherein having BALDWIN’s method of determining crop height having removing the ground point cloud in the 3D point cloud data of the target crop population.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and VARMA relate to detecting crop rows through the use of LIDAR, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while VARMA the example vehicle row follow systems, methods and mediums facilitate navigation of the vehicle along a centerline between consecutive plant rows. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and VARMA BHUPATIRAJU et al. (US 20230094371 A1), Paragraph [0021].
Regarding claim 16, BALDWIN in view of BAINBRIDGE explicitly teach the non-transitory computer-readable storage medium according to claim 9,
BALDWIN further explicitly teaches after the acquiring 3D point cloud data of a target crop population (Fig. 3, #302 called capture scan data. Paragraph [0058]-BALDWIN discloses the method 300 includes capturing, at 302, by the scanning device 110, scan data from the field 106, where the scan data, in this embodiment, includes LiDAR data indicative of surfaces (and/or surface features) of the field 106 (e.g., ground and ground features, vegetation, etc.). Further in paragraph [0060]-BALDWIN discloses the scan data may include a series of point cloud data, which includes point data for the individual scans of the event.), further comprising a preprocessing step (Fig. 3, #307 called classify aligned data. Paragraph [0062]), specifically:
determining a ground point cloud according to the 3D point cloud data of the target crop population (Fig. 3, #307 called classify aligned data. Paragraph [0062]-BALDWIN discloses at 307, the computing device 102 classifies the data in the composite data set, for example, where each of the points is classified as ground (wherein points classified as ground are a ground point cloud).);
BALDWIN fails to explicitly teach determining a normal direction of the ground point cloud as a reference direction, and.
However, BAINBRIDGE explicitly teaches determining a normal direction of the ground point cloud as a reference direction (Fig. 3. Paragraph [0124]-BAINBRIDGE discloses the crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes. The Z-component of each 3D point may be relative. Relative Z can be determined via the drone using an on-board range sensor (e.g. LIDAR) or altitude sensor (e.g. barometer), if available (wherein the Z-component is a reference direction).), and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of BAINBRIDGE of determining a normal direction of the ground point cloud as a reference direction, and.
Wherein having BALDWIN’s method of determining crop height having determining a normal direction of the ground point cloud as a reference direction, and.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and BAINBRIDGE relate to measuring and monitoring crop fields to generate models based on crop data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while BAINBRIDGE there is therefore a need for an automated system and method of crop monitoring for more efficient crop monitoring, accurate crop yield predictions, and highly targeted interventions up to a plant-by-plant level. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and BAINBRIDGE et al. (US 20230292647 A1), Paragraph [0006].
Although BALDWIN in view of BAINBRIDGE explicitly teach the 3D point cloud data of the target population, BALDWIN in view of BAINBRIDGE fail to explicitly teach rotating the 3D point cloud data of the target crop population, such that a direction of the 3D point cloud data of the target crop population is the same as the reference direction; and.
However, SELVIAH explicitly teaches rotating the 3D point cloud data of the target crop population (Fig. 3-4. Paragraph [0171]-SELVIAH discloses rotate the second point cloud by the stored angle of rotation about the respective one or more axes of rotation among the three orthogonal axes of rotation in the common coordinate system (wherein the second point cloud is the 3D point cloud data of the target crop population).), such that a direction of the 3D point cloud data of the target crop population is the same as the reference direction (Fig. 3. Paragraph [0173]-SELVIAH discloses the rotational alignment processor is configured to find at which angle of rotation the second set of vectors exhibits the best match with the first set of vectors (wherein the angle of rotation is the reference direction). Paragraph [0176]-SELVIAH discloses it may be that both the first 3D dataset and the rotated second 3D dataset are output, the output being the two datasets defined in a coordinate system in which they are rotationally aligned (wherein being rotationally aligned is having a point cloud in the same direction as the reference direction).); and
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of SELVIAH of rotating the 3D point cloud data of the target crop population, such that a direction of the 3D point cloud data of the target crop population is the same as the reference direction; and.
Wherein having BALDWIN’s method of determining crop height having rotating the 3D point cloud data of the target crop population, such that a direction of the 3D point cloud data of the target crop population is the same as the reference direction; and.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and SELVIAH relate to processing three-dimensional point clouds obtained by LIDAR, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while SELVIAH it is desirable to provide a reliable automated technique for the rotational alignment of overlapping 3D datasets. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and SELVIAH et al. (US 20200043186 A1), Paragraph [0005].
BALDWIN in view of BAINBRIDGE and further in view of SELVIAH fail to explicitly teach removing the ground point cloud in the 3D point cloud data of the target crop population.
However, VARMA explicitly teaches removing the ground point cloud in the 3D point cloud data of the target crop population (Fig. 11. Paragraph [0075]-VARMA FIG. 11 illustrates the 3D point cloud 552 after processing unit 50 has applied a ground filter. For example, a ground filter process may be applied to separate the ground and non-ground points such that the non-ground points are retained.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE and further in view of SELVIAH of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of VARMA of removing the ground point cloud in the 3D point cloud data of the target crop population.
Wherein having BALDWIN’s method of determining crop height having removing the ground point cloud in the 3D point cloud data of the target crop population.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and VARMA relate to detecting crop rows through the use of LIDAR, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while VARMA the example vehicle row follow systems, methods and mediums facilitate navigation of the vehicle along a centerline between consecutive plant rows. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and VARMA BHUPATIRAJU et al. (US 20230094371 A1), Paragraph [0021].
Claims 3-4, 6, 12-13, 15, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over BALDWIN et al. (US 20240032454 A1), hereinafter referenced as BALDWIN, in view of BAINBRIDGE et al. (US 20230292647 A1), hereinafter referenced as BAINBRIDGE, and further in view of YAO et al. (US 20220189053 A1), hereinafter referenced as YAO.
Regarding claim 3, BALDWIN in view of BAINBRIDGE explicitly teach the method for calculating a plant height uniformity of a crop population according to claim 1,
BALDWIN further explicitly teaches acquiring a row spacing and a plant spacing of each target crop plot (Fig. 1. Paragraph [0043]-BALDWIN discloses the computing device 102 is configured to determine the plant heights of each of the plots 108a-d in the field 106. In doing so, the computing device 102 is configured to detect the number of rows in the given plot (and check the number of rows against planter data for the plot). The computing device 102 is configured to then select canopy hits in the CHM for the rows in each plot, in general, based on detected rows, plant spacing, etc., or other assessment of the CHM, etc.);
BALDWIN fails to explicitly teach wherein the determining target crop grids according to the 3D point cloud data of each target crop plot comprises:
However, BAINBRIDGE explicitly teaches wherein the determining target crop grids (Fig. 3. Paragraph [0124]-BAINBRIDGE discloses the crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes (wherein a target crop grid is a 3D point).) according to the 3D point cloud data of each target crop plot (Fig. 3. Paragraph [0108]-BAINBRIDGE discloses the AOI may be a whole field or a region of a field (wherein a region of a field is a target crop plot). Further in paragraph [0124]-BAINBRIDGE discloses the crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes (wherein a target crop grid is a 3D point).) comprises:
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of BAINBRIDGE of wherein the determining target crop grids according to the 3D point cloud data of each target crop plot comprises:
Wherein having BALDWIN’s method of determining crop height having wherein the determining target crop grids according to the 3D point cloud data of each target crop plot comprises:
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and BAINBRIDGE relate to measuring and monitoring crop fields to generate models based on crop data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while BAINBRIDGE there is therefore a need for an automated system and method of crop monitoring for more efficient crop monitoring, accurate crop yield predictions, and highly targeted interventions up to a plant-by-plant level. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and BAINBRIDGE et al. (US 20230292647 A1), Paragraph [0006].
BALDWIN in view of BAINBRIDGE fail to explicitly teach dividing the 3D point cloud data of each target crop plot into multiple rows according to the row spacing to obtain a row divided result; and dividing the 3D point cloud data of each target crop plot into multiple columns according to the plant spacing to obtain a column divided result; and determining the target crop grids according to the row divided result and the column divided result for the 3D point cloud data of each target crop plot, the target crop grid comprising a length taken as the row spacing, and a width taken as the plant spacing.
However, YAO explicitly teaches dividing the 3D point cloud data of each target crop plot into multiple rows according to the row spacing to obtain a row divided result (Fig. 2A-2C. Paragraph [0062]-YAO discloses after point cloud registration and denoising processing, point clouds of a row of wheat (FIG. 2b) were randomly acquired from a wheat point cloud image (FIG. 2a) of an area; the row of wheat point clouds was expressed into a simple cuboid, a row length was defined as X, a row width was defined as Y, and a plant height was defined as Z (FIG. 2c) (wherein randomly acquiring a row of wheat is dividing the 3D point cloud data into rows).); and
dividing the 3D point cloud data of each target crop plot into multiple columns according to the plant spacing to obtain a column divided result (Fig. 3 and 5. Paragraph [0066]-YAO discloses one row of wheat was divided into segments at intervals in a direction of the X axis according to a step length of 0.1 m (wherein the segments divided in a direction of the X axis form columns and wherein 0.1m is the plant spacing).); and
determining the target crop grids according to the row divided result and the column divided result for the 3D point cloud data of each target crop plot (Fig. 5B, illustrates the target crop grids given the row divided result and the column divided result. Paragraph [0069]-YAO discloses FIG. 5b, the black showed the leaf layer of one row of wheat, and the gray showed the stem layer of one row of wheat.),
the target crop grid comprising a length taken as the row spacing (Fig. 2A-2C, illustrate an aspect of the target crop grid wherein the length of the row is the row spacing. Paragraph [0062]-YAO discloses after point cloud registration and denoising processing, point clouds of a row of wheat (FIG. 2b) were randomly acquired from a wheat point cloud image (FIG. 2a) of an area; the row of wheat point clouds was expressed into a simple cuboid, a row length was defined as X, a row width was defined as Y, and a plant height was defined as Z (FIG. 2c).), and a width taken as the plant spacing (Fig. 3 and 5. Paragraph [0066]-YAO discloses one row of wheat was divided into segments at intervals in a direction of the X axis according to a step length of 0.1 m (wherein 0.1m is the plant spacing).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of YAO of dividing the 3D point cloud data of each target crop plot into multiple rows according to the row spacing to obtain a row divided result; and dividing the 3D point cloud data of each target crop plot into multiple columns according to the plant spacing to obtain a column divided result; and determining the target crop grids according to the row divided result and the column divided result for the 3D point cloud data of each target crop plot, the target crop grid comprising a length taken as the row spacing, and a width taken as the plant spacing.
Wherein having BALDWIN’s method of determining crop height having dividing the 3D point cloud data of each target crop plot into multiple rows according to the row spacing to obtain a row divided result; and dividing the 3D point cloud data of each target crop plot into multiple columns according to the plant spacing to obtain a column divided result; and determining the target crop grids according to the row divided result and the column divided result for the 3D point cloud data of each target crop plot, the target crop grid comprising a length taken as the row spacing, and a width taken as the plant spacing.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and YAO relate to measuring and monitoring crop fields through the use of LIDAR point cloud data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while YAO the method of the present invention is simple, is convenient to operate, and provides theoretical basis and technical support for the rapid and nondestructive extraction of a field wheat stem tillering number. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and YAO et al. (US 20220189053 A1), Paragraph [0036].
Regarding claim 4, BALDWIN in view of BAINBRIDGE and further in view of YAO explicitly teach the method for calculating a plant height uniformity of a crop population according to claim 3,
BALDWIN further explicitly teaches wherein the determining a plant height uniformity of each target crop plot (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106). Further in paragraph [0063]-BALDWIN discloses the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel. That said, it should be appreciated that the CHM may be derived from the composite data set in other ways in other embodiments, which may rely, for example, on row detection, planter data (e.g., row/plant location, etc.), etc. (wherein the plant height uniformity is the canopy height model).) according to 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0061]-BALDWIN discloses at 306, the computing device 102 aligns the scan data to form a composite data set (wherein the composite data set is 3D point cloud data of the target crop grid).) comprises:
determining a height characteristic index of the target crop grid (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index).) according to the 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the target crop grid is the composite data set).),
the height characteristic index being used for describing a target crop height in the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses the CHM is then derived by the computing device 102 as the height of the composite data for the vegetation above the ground/surface elevation (Equation 1). As such, the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel (wherein a target crop height is the height of the composite data for the vegetation above the ground/surface elevation).); and
determining the plant height uniformity of each target crop plot (Fig. 1 and 3-4. Paragraph [0063]-BALDWIN discloses the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the plant height uniformity is the canopy height model).) according to the height characteristic index of each target crop grid in each target crop plot (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index). Further in paragraph [0064]-BALDWIN discloses the CHM includes orange colored regions indicative of the height of the vegetation in the field and then dark colored regions indicative of the ground, both of which are bounded by the plot geometries (e.g., plots as indicated by the generally rectangular boundary lines/grid, etc.) of the field (e.g., as defined by the planter during planting, etc.).).
Regarding claim 6, BALDWIN in view of BAINBRIDGE and further in view of YAO explicitly teach the method for calculating a plant height uniformity of a crop population according to claim 4,
BALDWIN further explicitly teaches wherein the height characteristic index of the target crop grid (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index).) according to the 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the target crop grid is the composite data set).) comprises:
sorting the 3D point cloud data of the target crop grid according to heights (Fig. 3. Paragraph [0066]-BALDWIN discloses at 310 in the method 300, the computing device 102 determines the plant height for each plot (in the field 106 or multiple fields) based on the CHM for the given plot (or for the field 106, etc.). More specifically, the CHM, for a given plot, is aggregated to one height for the plot. For example, where a CHM for a plot includes hundreds or thousands of pixels, and corresponding height values, the computing device 102 may determine the aggregate of at least a portion of the pixels (e.g., a top 25%, 50% or 75% of the pixel heights, etc.) (wherein in order to determine a top 25%, 50% or 75% of the pixel heights the heights must first be sorted).); and
determining the height characteristic index as a height value of a preset quantile according to a height sorted result (Fig. 3. Paragraph [0066]-BALDWIN discloses at 310 in the method 300, the computing device 102 determines the plant height for each plot (in the field 106 or multiple fields) based on the CHM for the given plot (or for the field 106, etc.). More specifically, the CHM, for a given plot, is aggregated to one height for the plot. For example, where a CHM for a plot includes hundreds or thousands of pixels, and corresponding height values, the computing device 102 may determine the aggregate of at least a portion of the pixels (e.g., a top 25%, 50% or 75% of the pixel heights, etc.) (wherein a preset quantile is a top 25%, 50% or 75% of the pixel heights).).
Regarding claim 12, BALDWIN in view of BAINBRIDGE explicitly teach the electronic device according to claim 8,
BALDWIN further explicitly teaches acquiring a row spacing and a plant spacing of each target crop plot (Fig. 1. Paragraph [0043]-BALDWIN discloses the computing device 102 is configured to determine the plant heights of each of the plots 108a-d in the field 106. In doing so, the computing device 102 is configured to detect the number of rows in the given plot (and check the number of rows against planter data for the plot). The computing device 102 is configured to then select canopy hits in the CHM for the rows in each plot, in general, based on detected rows, plant spacing, etc., or other assessment of the CHM, etc.);
BALDWIN fails to explicitly teach wherein the determining target crop grids according to the 3D point cloud data of each target crop plot comprises:
However, BAINBRIDGE explicitly teaches wherein the determining target crop grids (Fig. 3. Paragraph [0124]-BAINBRIDGE discloses the crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes (wherein a target crop grid is a 3D point).) according to the 3D point cloud data of each target crop plot (Fig. 3. Paragraph [0108]-BAINBRIDGE discloses the AOI may be a whole field or a region of a field (wherein a region of a field is a target crop plot). Further in paragraph [0124]-BAINBRIDGE discloses the crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes (wherein a target crop grid is a 3D point).) comprises:
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of BAINBRIDGE of wherein the determining target crop grids according to the 3D point cloud data of each target crop plot comprises:
Wherein having BALDWIN’s method of determining crop height having wherein the determining target crop grids according to the 3D point cloud data of each target crop plot comprises:
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and BAINBRIDGE relate to measuring and monitoring crop fields to generate models based on crop data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while BAINBRIDGE there is therefore a need for an automated system and method of crop monitoring for more efficient crop monitoring, accurate crop yield predictions, and highly targeted interventions up to a plant-by-plant level. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and BAINBRIDGE et al. (US 20230292647 A1), Paragraph [0006].
BALDWIN in view of BAINBRIDGE fail to explicitly teach dividing the 3D point cloud data of each target crop plot into multiple rows according to the row spacing to obtain a row divided result; and dividing the 3D point cloud data of each target crop plot into multiple columns according to the plant spacing to obtain a column divided result; and determining the target crop grids according to the row divided result and the column divided result for the 3D point cloud data of each target crop plot, the target crop grid comprising a length taken as the row spacing, and a width taken as the plant spacing.
However, YAO explicitly teaches dividing the 3D point cloud data of each target crop plot into multiple rows according to the row spacing to obtain a row divided result (Fig. 2A-2C. Paragraph [0062]-YAO discloses after point cloud registration and denoising processing, point clouds of a row of wheat (FIG. 2b) were randomly acquired from a wheat point cloud image (FIG. 2a) of an area; the row of wheat point clouds was expressed into a simple cuboid, a row length was defined as X, a row width was defined as Y, and a plant height was defined as Z (FIG. 2c) (wherein randomly acquiring a row of wheat is dividing the 3D point cloud data into rows).); and
dividing the 3D point cloud data of each target crop plot into multiple columns according to the plant spacing to obtain a column divided result (Fig. 3 and 5. Paragraph [0066]-YAO discloses one row of wheat was divided into segments at intervals in a direction of the X axis according to a step length of 0.1 m (wherein the segments divided in a direction of the X axis form columns and wherein 0.1m is the plant spacing).); and
determining the target crop grids according to the row divided result and the column divided result for the 3D point cloud data of each target crop plot (Fig. 5B, illustrates the target crop grids given the row divided result and the column divided result. Paragraph [0069]-YAO discloses FIG. 5b, the black showed the leaf layer of one row of wheat, and the gray showed the stem layer of one row of wheat.),
the target crop grid comprising a length taken as the row spacing (Fig. 2A-2C, illustrate an aspect of the target crop grid wherein the length of the row is the row spacing. Paragraph [0062]-YAO discloses after point cloud registration and denoising processing, point clouds of a row of wheat (FIG. 2b) were randomly acquired from a wheat point cloud image (FIG. 2a) of an area; the row of wheat point clouds was expressed into a simple cuboid, a row length was defined as X, a row width was defined as Y, and a plant height was defined as Z (FIG. 2c).), and a width taken as the plant spacing (Fig. 3 and 5. Paragraph [0066]-YAO discloses one row of wheat was divided into segments at intervals in a direction of the X axis according to a step length of 0.1 m (wherein 0.1m is the plant spacing).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of YAO of dividing the 3D point cloud data of each target crop plot into multiple rows according to the row spacing to obtain a row divided result; and dividing the 3D point cloud data of each target crop plot into multiple columns according to the plant spacing to obtain a column divided result; and determining the target crop grids according to the row divided result and the column divided result for the 3D point cloud data of each target crop plot, the target crop grid comprising a length taken as the row spacing, and a width taken as the plant spacing.
Wherein having BALDWIN’s method of determining crop height having dividing the 3D point cloud data of each target crop plot into multiple rows according to the row spacing to obtain a row divided result; and dividing the 3D point cloud data of each target crop plot into multiple columns according to the plant spacing to obtain a column divided result; and determining the target crop grids according to the row divided result and the column divided result for the 3D point cloud data of each target crop plot, the target crop grid comprising a length taken as the row spacing, and a width taken as the plant spacing.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and YAO relate to measuring and monitoring crop fields through the use of LIDAR point cloud data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while YAO the method of the present invention is simple, is convenient to operate, and provides theoretical basis and technical support for the rapid and nondestructive extraction of a field wheat stem tillering number. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and YAO et al. (US 20220189053 A1), Paragraph [0036].
Regarding claim 13, BALDWIN in view of BAINBRIDGE and further in view of YAO explicitly teach the electronic device according to claim 12,
BALDWIN further explicitly teaches wherein the determining a plant height uniformity of each target crop plot (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106). Further in paragraph [0063]-BALDWIN discloses the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel. That said, it should be appreciated that the CHM may be derived from the composite data set in other ways in other embodiments, which may rely, for example, on row detection, planter data (e.g., row/plant location, etc.), etc. (wherein the plant height uniformity is the canopy height model).) according to 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0061]-BALDWIN discloses at 306, the computing device 102 aligns the scan data to form a composite data set (wherein the composite data set is 3D point cloud data of the target crop grid).) comprises:
determining a height characteristic index of the target crop grid (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index).) according to the 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the target crop grid is the composite data set).),
the height characteristic index being used for describing a target crop height in the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses the CHM is then derived by the computing device 102 as the height of the composite data for the vegetation above the ground/surface elevation (Equation 1). As such, the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel (wherein a target crop height is the height of the composite data for the vegetation above the ground/surface elevation).); and
determining the plant height uniformity of each target crop plot (Fig. 1 and 3-4. Paragraph [0063]-BALDWIN discloses the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the plant height uniformity is the canopy height model).) according to the height characteristic index of each target crop grid in each target crop plot (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index). Further in paragraph [0064]-BALDWIN discloses the CHM includes orange colored regions indicative of the height of the vegetation in the field and then dark colored regions indicative of the ground, both of which are bounded by the plot geometries (e.g., plots as indicated by the generally rectangular boundary lines/grid, etc.) of the field (e.g., as defined by the planter during planting, etc.).).
Regarding claim 15, BALDWIN in view of BAINBRIDGE and further in view of YAO explicitly teach the electronic device according to claim 13,
BALDWIN further explicitly teaches wherein the height characteristic index of the target crop grid (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index).) according to the 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the target crop grid is the composite data set).) comprises:
sorting the 3D point cloud data of the target crop grid according to heights (Fig. 3. Paragraph [0066]-BALDWIN discloses at 310 in the method 300, the computing device 102 determines the plant height for each plot (in the field 106 or multiple fields) based on the CHM for the given plot (or for the field 106, etc.). More specifically, the CHM, for a given plot, is aggregated to one height for the plot. For example, where a CHM for a plot includes hundreds or thousands of pixels, and corresponding height values, the computing device 102 may determine the aggregate of at least a portion of the pixels (e.g., a top 25%, 50% or 75% of the pixel heights, etc.) (wherein in order to determine a top 25%, 50% or 75% of the pixel heights the heights must first be sorted).); and
determining the height characteristic index as a height value of a preset quantile according to a height sorted result (Fig. 3. Paragraph [0066]-BALDWIN discloses at 310 in the method 300, the computing device 102 determines the plant height for each plot (in the field 106 or multiple fields) based on the CHM for the given plot (or for the field 106, etc.). More specifically, the CHM, for a given plot, is aggregated to one height for the plot. For example, where a CHM for a plot includes hundreds or thousands of pixels, and corresponding height values, the computing device 102 may determine the aggregate of at least a portion of the pixels (e.g., a top 25%, 50% or 75% of the pixel heights, etc.) (wherein a preset quantile is a top 25%, 50% or 75% of the pixel heights).).
Regarding claim 17, BALDWIN in view of BAINBRIDGE explicitly teach the non-transitory computer-readable storage medium according to claim 9,
BALDWIN further explicitly teaches acquiring a row spacing and a plant spacing of each target crop plot (Fig. 1. Paragraph [0043]-BALDWIN discloses the computing device 102 is configured to determine the plant heights of each of the plots 108a-d in the field 106. In doing so, the computing device 102 is configured to detect the number of rows in the given plot (and check the number of rows against planter data for the plot). The computing device 102 is configured to then select canopy hits in the CHM for the rows in each plot, in general, based on detected rows, plant spacing, etc., or other assessment of the CHM, etc.);
BALDWIN fails to explicitly teach wherein the determining target crop grids according to the 3D point cloud data of each target crop plot comprises:
However, BAINBRIDGE explicitly teaches wherein the determining target crop grids (Fig. 3. Paragraph [0124]-BAINBRIDGE discloses the crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes (wherein a target crop grid is a 3D point).) according to the 3D point cloud data of each target crop plot (Fig. 3. Paragraph [0108]-BAINBRIDGE discloses the AOI may be a whole field or a region of a field (wherein a region of a field is a target crop plot). Further in paragraph [0124]-BAINBRIDGE discloses the crop model is a three-dimensional (3D) point cloud model, where each 3D point represents the location of a crop feature in the AOI, which is tagged or associated with a respective attribute vector containing all the determined attributes (wherein a target crop grid is a 3D point).) comprises:
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of BAINBRIDGE of wherein the determining target crop grids according to the 3D point cloud data of each target crop plot comprises:
Wherein having BALDWIN’s method of determining crop height having wherein the determining target crop grids according to the 3D point cloud data of each target crop plot comprises:
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and BAINBRIDGE relate to measuring and monitoring crop fields to generate models based on crop data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while BAINBRIDGE there is therefore a need for an automated system and method of crop monitoring for more efficient crop monitoring, accurate crop yield predictions, and highly targeted interventions up to a plant-by-plant level. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and BAINBRIDGE et al. (US 20230292647 A1), Paragraph [0006].
BALDWIN in view of BAINBRIDGE fail to explicitly teach dividing the 3D point cloud data of each target crop plot into multiple rows according to the row spacing to obtain a row divided result; and dividing the 3D point cloud data of each target crop plot into multiple columns according to the plant spacing to obtain a column divided result; and determining the target crop grids according to the row divided result and the column divided result for the 3D point cloud data of each target crop plot, the target crop grid comprising a length taken as the row spacing, and a width taken as the plant spacing.
However, YAO explicitly teaches dividing the 3D point cloud data of each target crop plot into multiple rows according to the row spacing to obtain a row divided result (Fig. 2A-2C. Paragraph [0062]-YAO discloses after point cloud registration and denoising processing, point clouds of a row of wheat (FIG. 2b) were randomly acquired from a wheat point cloud image (FIG. 2a) of an area; the row of wheat point clouds was expressed into a simple cuboid, a row length was defined as X, a row width was defined as Y, and a plant height was defined as Z (FIG. 2c) (wherein randomly acquiring a row of wheat is dividing the 3D point cloud data into rows).); and
dividing the 3D point cloud data of each target crop plot into multiple columns according to the plant spacing to obtain a column divided result (Fig. 3 and 5. Paragraph [0066]-YAO discloses one row of wheat was divided into segments at intervals in a direction of the X axis according to a step length of 0.1 m (wherein the segments divided in a direction of the X axis form columns and wherein 0.1m is the plant spacing).); and
determining the target crop grids according to the row divided result and the column divided result for the 3D point cloud data of each target crop plot (Fig. 5B, illustrates the target crop grids given the row divided result and the column divided result. Paragraph [0069]-YAO discloses FIG. 5b, the black showed the leaf layer of one row of wheat, and the gray showed the stem layer of one row of wheat.),
the target crop grid comprising a length taken as the row spacing (Fig. 2A-2C, illustrate an aspect of the target crop grid wherein the length of the row is the row spacing. Paragraph [0062]-YAO discloses after point cloud registration and denoising processing, point clouds of a row of wheat (FIG. 2b) were randomly acquired from a wheat point cloud image (FIG. 2a) of an area; the row of wheat point clouds was expressed into a simple cuboid, a row length was defined as X, a row width was defined as Y, and a plant height was defined as Z (FIG. 2c).), and a width taken as the plant spacing (Fig. 3 and 5. Paragraph [0066]-YAO discloses one row of wheat was divided into segments at intervals in a direction of the X axis according to a step length of 0.1 m (wherein 0.1m is the plant spacing).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of YAO of dividing the 3D point cloud data of each target crop plot into multiple rows according to the row spacing to obtain a row divided result; and dividing the 3D point cloud data of each target crop plot into multiple columns according to the plant spacing to obtain a column divided result; and determining the target crop grids according to the row divided result and the column divided result for the 3D point cloud data of each target crop plot, the target crop grid comprising a length taken as the row spacing, and a width taken as the plant spacing.
Wherein having BALDWIN’s method of determining crop height having dividing the 3D point cloud data of each target crop plot into multiple rows according to the row spacing to obtain a row divided result; and dividing the 3D point cloud data of each target crop plot into multiple columns according to the plant spacing to obtain a column divided result; and determining the target crop grids according to the row divided result and the column divided result for the 3D point cloud data of each target crop plot, the target crop grid comprising a length taken as the row spacing, and a width taken as the plant spacing.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and YAO relate to measuring and monitoring crop fields through the use of LIDAR point cloud data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while YAO the method of the present invention is simple, is convenient to operate, and provides theoretical basis and technical support for the rapid and nondestructive extraction of a field wheat stem tillering number. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and YAO et al. (US 20220189053 A1), Paragraph [0036].
Regarding claim 18, BALDWIN in view of BAINBRIDGE and further in view of YAO explicitly teach the non-transitory computer-readable storage medium according to claim 17,
BALDWIN further explicitly teaches wherein the determining a plant height uniformity of each target crop plot (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106). Further in paragraph [0063]-BALDWIN discloses the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel. That said, it should be appreciated that the CHM may be derived from the composite data set in other ways in other embodiments, which may rely, for example, on row detection, planter data (e.g., row/plant location, etc.), etc. (wherein the plant height uniformity is the canopy height model).) according to 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0061]-BALDWIN discloses at 306, the computing device 102 aligns the scan data to form a composite data set (wherein the composite data set is 3D point cloud data of the target crop grid).) comprises:
determining a height characteristic index of the target crop grid (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index).) according to the 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the target crop grid is the composite data set).),
the height characteristic index being used for describing a target crop height in the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses the CHM is then derived by the computing device 102 as the height of the composite data for the vegetation above the ground/surface elevation (Equation 1). As such, the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel (wherein a target crop height is the height of the composite data for the vegetation above the ground/surface elevation).); and
determining the plant height uniformity of each target crop plot (Fig. 1 and 3-4. Paragraph [0063]-BALDWIN discloses the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the plant height uniformity is the canopy height model).) according to the height characteristic index of each target crop grid in each target crop plot (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index). Further in paragraph [0064]-BALDWIN discloses the CHM includes orange colored regions indicative of the height of the vegetation in the field and then dark colored regions indicative of the ground, both of which are bounded by the plot geometries (e.g., plots as indicated by the generally rectangular boundary lines/grid, etc.) of the field (e.g., as defined by the planter during planting, etc.).).
Regarding claim 20, BALDWIN in view of BAINBRIDGE and further in view of YAO explicitly teach the non-transitory computer-readable storage medium according to claim 18,
BALDWIN further explicitly teaches wherein the height characteristic index of the target crop grid (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index).) according to the 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the target crop grid is the composite data set).) comprises:
sorting the 3D point cloud data of the target crop grid according to heights (Fig. 3. Paragraph [0066]-BALDWIN discloses at 310 in the method 300, the computing device 102 determines the plant height for each plot (in the field 106 or multiple fields) based on the CHM for the given plot (or for the field 106, etc.). More specifically, the CHM, for a given plot, is aggregated to one height for the plot. For example, where a CHM for a plot includes hundreds or thousands of pixels, and corresponding height values, the computing device 102 may determine the aggregate of at least a portion of the pixels (e.g., a top 25%, 50% or 75% of the pixel heights, etc.) (wherein in order to determine a top 25%, 50% or 75% of the pixel heights the heights must first be sorted).); and
determining the height characteristic index as a height value of a preset quantile according to a height sorted result (Fig. 3. Paragraph [0066]-BALDWIN discloses at 310 in the method 300, the computing device 102 determines the plant height for each plot (in the field 106 or multiple fields) based on the CHM for the given plot (or for the field 106, etc.). More specifically, the CHM, for a given plot, is aggregated to one height for the plot. For example, where a CHM for a plot includes hundreds or thousands of pixels, and corresponding height values, the computing device 102 may determine the aggregate of at least a portion of the pixels (e.g., a top 25%, 50% or 75% of the pixel heights, etc.) (wherein a preset quantile is a top 25%, 50% or 75% of the pixel heights).).
Claims 5, 10, 14, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over BALDWIN et al. (US 20240032454 A1), hereinafter referenced as BALDWIN, in view of BAINBRIDGE et al. (US 20230292647 A1), hereinafter referenced as BAINBRIDGE, and further in view of YAO et al. (US 20220189053 A1), hereinafter referenced as YAO, and further in view of ROJAS (US 20140107927 A1), hereinafter referenced as ROJAS.
Regarding claim 5, BALDWIN in view of BAINBRIDGE and further in view of YAO explicitly teach the method for calculating a plant height uniformity of a crop population according to claim 3,
BALDWIN further explicitly teaches wherein the determining a plant height uniformity of each target crop plot (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106). Further in paragraph [0063]-BALDWIN discloses the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel. That said, it should be appreciated that the CHM may be derived from the composite data set in other ways in other embodiments, which may rely, for example, on row detection, planter data (e.g., row/plant location, etc.), etc. (wherein the plant height uniformity is the canopy height model).) according to 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0061]-BALDWIN discloses at 306, the computing device 102 aligns the scan data to form a composite data set (wherein the composite data set is 3D point cloud data of the target crop grid).) comprises:
determining a height characteristic index of the target crop grid (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index).) according to the 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the target crop grid is the composite data set).),
the height characteristic index being used for describing a target crop height in the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses the CHM is then derived by the computing device 102 as the height of the composite data for the vegetation above the ground/surface elevation (Equation 1). As such, the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel (wherein a target crop height is the height of the composite data for the vegetation above the ground/surface elevation).); and
determining the plant height uniformity of each target crop plot (Fig. 1 and 3-4. Paragraph [0063]-BALDWIN discloses the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the plant height uniformity is the canopy height model).) according to a height characteristic index of a first grid in each target crop plot (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index). Further in paragraph [0064]-BALDWIN discloses the CHM includes orange colored regions indicative of the height of the vegetation in the field and then dark colored regions indicative of the ground, both of which are bounded by the plot geometries (e.g., plots as indicated by the generally rectangular boundary lines/grid, etc.) of the field (e.g., as defined by the planter during planting, etc.) (wherein a pixel is a first grid).),
BALDWIN in view of BAINBRIDGE fail to explicitly teach removing at least one head column of target crop grids and at least one tail column of target crop grids in the column divided result.
However, YAO explicitly teaches removing at least one head column of target crop grids and at least one tail column of target crop grids in the column divided result (Paragraph [0068]-YAO discloses a lowest layer (10.sup.th layer, soil layer) and a highest layer (1.sup.st layer, top layer) were removed, and the point clouds of a top layer (2.sup.nd layer) and a bottom layer (9.sup.th layer) of the 8 remaining layers (10−2=8) were respectively marked by attribute fields: the leaf layer (2.sup.nd layer) was marked as “1”, and the stem layer (9.sup.th layer) was marked as “0” (wherein the highest layer is the head column and the lowest layer is the tail column).),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of YAO of removing at least one head column of target crop grids and at least one tail column of target crop grids in the column divided result.
Wherein having BALDWIN’s method of determining crop height having removing at least one head column of target crop grids and at least one tail column of target crop grids in the column divided result.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and YAO relate to measuring and monitoring crop fields through the use of LIDAR point cloud data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while YAO the method of the present invention is simple, is convenient to operate, and provides theoretical basis and technical support for the rapid and nondestructive extraction of a field wheat stem tillering number. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and YAO et al. (US 20220189053 A1), Paragraph [0036].
BALDWIN in view of BAINBRIDGE and further in view of YAO fail to explicitly teach wherein the first grid is determined as follows: removing at least one head row of target crop grids and at least one tail row of target crop grids in the row divided result, thereby obtaining the first grid.
However, ROJAS explicitly teaches wherein the first grid is determined as follows (Fig. 4. Paragraph [0022]-ROJAS discloses FIG. 4 illustrates a further filtering of the LiDAR return data by determining those LiDAR data points that are both within the buffers zones around the planting lines and are within the boundaries of the plot 50 to be analyzed (wherein plot 50 is the first grid).):
removing at least one head row of target crop grids and at least one tail row of target crop grids in the row divided result (Fig. 4. Paragraph [0022]-ROJAS discloses FIG. 4 illustrates a further filtering of the LiDAR return data by determining those LiDAR data points that are both within the buffers zones around the planting lines and are within the boundaries of the plot 50 to be analyzed (wherein filtering is removing and rows 160a, 160b and 160i are head rows and tail rows that are removed).), and
thereby obtaining the first grid (Fig. 4. Paragraph [0022]-ROJAS discloses FIG. 4 illustrates a further filtering of the LiDAR return data by determining those LiDAR data points that are both within the buffers zones around the planting lines and are within the boundaries of the plot 50 to be analyzed (wherein plot 50 is the first grid).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE and further in view of YAO of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of ROJAS of wherein the first grid is determined as follows: removing at least one head row of target crop grids and at least one tail row of target crop grids in the row divided result, thereby obtaining the first grid.
Wherein having BALDWIN’s method of determining crop height having wherein the first grid is determined as follows: removing at least one head row of target crop grids and at least one tail row of target crop grids in the row divided result, thereby obtaining the first grid.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and ROJAS relate to measuring and monitoring plants to generate models based on LIDAR data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while ROJAS there is need for a technique that can better identify young trees from surrounding vegetation with remotely sensed data. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and ROJAS (US 20140107927 A1), Paragraph [0003].
Regarding claim 10, BALDWIN in view of BAINBRIDGE and further in view of YAO and further in view of ROJAS explicitly teach the method for calculating a plant height uniformity of a crop population according to claim 5,
BALDWIN further explicitly teaches wherein the height characteristic index of the target crop grid (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index).) according to the 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the target crop grid is the composite data set).) comprises:
sorting the 3D point cloud data of the target crop grid according to heights (Fig. 3. Paragraph [0066]-BALDWIN discloses at 310 in the method 300, the computing device 102 determines the plant height for each plot (in the field 106 or multiple fields) based on the CHM for the given plot (or for the field 106, etc.). More specifically, the CHM, for a given plot, is aggregated to one height for the plot. For example, where a CHM for a plot includes hundreds or thousands of pixels, and corresponding height values, the computing device 102 may determine the aggregate of at least a portion of the pixels (e.g., a top 25%, 50% or 75% of the pixel heights, etc.) (wherein in order to determine a top 25%, 50% or 75% of the pixel heights the heights must first be sorted).); and
determining the height characteristic index as a height value of a preset quantile according to a height sorted result (Fig. 3. Paragraph [0066]-BALDWIN discloses at 310 in the method 300, the computing device 102 determines the plant height for each plot (in the field 106 or multiple fields) based on the CHM for the given plot (or for the field 106, etc.). More specifically, the CHM, for a given plot, is aggregated to one height for the plot. For example, where a CHM for a plot includes hundreds or thousands of pixels, and corresponding height values, the computing device 102 may determine the aggregate of at least a portion of the pixels (e.g., a top 25%, 50% or 75% of the pixel heights, etc.) (wherein a preset quantile is a top 25%, 50% or 75% of the pixel heights).).
Regarding claim 14, BALDWIN in view of BAINBRIDGE and further in view of YAO explicitly teach the electronic device according to claim 12,
BALDWIN further explicitly teaches wherein the determining a plant height uniformity of each target crop plot (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106). Further in paragraph [0063]-BALDWIN discloses the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel. That said, it should be appreciated that the CHM may be derived from the composite data set in other ways in other embodiments, which may rely, for example, on row detection, planter data (e.g., row/plant location, etc.), etc. (wherein the plant height uniformity is the canopy height model).) according to 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0061]-BALDWIN discloses at 306, the computing device 102 aligns the scan data to form a composite data set (wherein the composite data set is 3D point cloud data of the target crop grid).) comprises:
determining a height characteristic index of the target crop grid (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index).) according to the 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the target crop grid is the composite data set).),
the height characteristic index being used for describing a target crop height in the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses the CHM is then derived by the computing device 102 as the height of the composite data for the vegetation above the ground/surface elevation (Equation 1). As such, the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel (wherein a target crop height is the height of the composite data for the vegetation above the ground/surface elevation).); and
determining the plant height uniformity of each target crop plot (Fig. 1 and 3-4. Paragraph [0063]-BALDWIN discloses the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the plant height uniformity is the canopy height model).) according to a height characteristic index of a first grid in each target crop plot (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index). Further in paragraph [0064]-BALDWIN discloses the CHM includes orange colored regions indicative of the height of the vegetation in the field and then dark colored regions indicative of the ground, both of which are bounded by the plot geometries (e.g., plots as indicated by the generally rectangular boundary lines/grid, etc.) of the field (e.g., as defined by the planter during planting, etc.) (wherein a pixel is a first grid).),
BALDWIN in view of BAINBRIDGE fail to explicitly teach removing at least one head column of target crop grids and at least one tail column of target crop grids in the column divided result.
However, YAO explicitly teaches removing at least one head column of target crop grids and at least one tail column of target crop grids in the column divided result (Paragraph [0068]-YAO discloses a lowest layer (10.sup.th layer, soil layer) and a highest layer (1.sup.st layer, top layer) were removed, and the point clouds of a top layer (2.sup.nd layer) and a bottom layer (9.sup.th layer) of the 8 remaining layers (10−2=8) were respectively marked by attribute fields: the leaf layer (2.sup.nd layer) was marked as “1”, and the stem layer (9.sup.th layer) was marked as “0” (wherein the highest layer is the head column and the lowest layer is the tail column).),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of YAO of removing at least one head column of target crop grids and at least one tail column of target crop grids in the column divided result.
Wherein having BALDWIN’s method of determining crop height having removing at least one head column of target crop grids and at least one tail column of target crop grids in the column divided result.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and YAO relate to measuring and monitoring crop fields through the use of LIDAR point cloud data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while YAO the method of the present invention is simple, is convenient to operate, and provides theoretical basis and technical support for the rapid and nondestructive extraction of a field wheat stem tillering number. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and YAO et al. (US 20220189053 A1), Paragraph [0036].
BALDWIN in view of BAINBRIDGE and further in view of YAO fail to explicitly teach wherein the first grid is determined as follows: removing at least one head row of target crop grids and at least one tail row of target crop grids in the row divided result, thereby obtaining the first grid.
However, ROJAS explicitly teaches wherein the first grid is determined as follows (Fig. 4. Paragraph [0022]-ROJAS discloses FIG. 4 illustrates a further filtering of the LiDAR return data by determining those LiDAR data points that are both within the buffers zones around the planting lines and are within the boundaries of the plot 50 to be analyzed (wherein plot 50 is the first grid).):
removing at least one head row of target crop grids and at least one tail row of target crop grids in the row divided result (Fig. 4. Paragraph [0022]-ROJAS discloses FIG. 4 illustrates a further filtering of the LiDAR return data by determining those LiDAR data points that are both within the buffers zones around the planting lines and are within the boundaries of the plot 50 to be analyzed (wherein filtering is removing and rows 160a, 160b and 160i are head rows and tail rows that are removed).), and
thereby obtaining the first grid (Fig. 4. Paragraph [0022]-ROJAS discloses FIG. 4 illustrates a further filtering of the LiDAR return data by determining those LiDAR data points that are both within the buffers zones around the planting lines and are within the boundaries of the plot 50 to be analyzed (wherein plot 50 is the first grid).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE and further in view of YAO of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of ROJAS of wherein the first grid is determined as follows: removing at least one head row of target crop grids and at least one tail row of target crop grids in the row divided result, thereby obtaining the first grid.
Wherein having BALDWIN’s method of determining crop height having wherein the first grid is determined as follows: removing at least one head row of target crop grids and at least one tail row of target crop grids in the row divided result, thereby obtaining the first grid.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and ROJAS relate to measuring and monitoring plants to generate models based on LIDAR data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while ROJAS there is need for a technique that can better identify young trees from surrounding vegetation with remotely sensed data. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and ROJAS (US 20140107927 A1), Paragraph [0003].
Regarding claim 19, BALDWIN in view of BAINBRIDGE and further in view of YAO explicitly teach the non-transitory computer-readable storage medium according to claim 17,
BALDWIN further explicitly teaches wherein the determining a plant height uniformity of each target crop plot (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106). Further in paragraph [0063]-BALDWIN discloses the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel. That said, it should be appreciated that the CHM may be derived from the composite data set in other ways in other embodiments, which may rely, for example, on row detection, planter data (e.g., row/plant location, etc.), etc. (wherein the plant height uniformity is the canopy height model).) according to 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0061]-BALDWIN discloses at 306, the computing device 102 aligns the scan data to form a composite data set (wherein the composite data set is 3D point cloud data of the target crop grid).) comprises:
determining a height characteristic index of the target crop grid (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index).) according to the 3D point cloud data of the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses at 308, the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the target crop grid is the composite data set).),
the height characteristic index being used for describing a target crop height in the target crop grid (Fig. 3. Paragraph [0063]-BALDWIN discloses the CHM is then derived by the computing device 102 as the height of the composite data for the vegetation above the ground/surface elevation (Equation 1). As such, the CHM is a two-dimensional image of canopy height, with specific pixels representing locations and the values of the pixel representing the height of the canopy above the ground at the location of the pixel (wherein a target crop height is the height of the composite data for the vegetation above the ground/surface elevation).); and
determining the plant height uniformity of each target crop plot (Fig. 1 and 3-4. Paragraph [0063]-BALDWIN discloses the computing device 102 generates a canopy height model (CHM) from the composite data set for a given one or more of the plots 108a-d (or for the field 106) (wherein the plant height uniformity is the canopy height model).) according to a height characteristic index of a first grid in each target crop plot (Fig. 4. Paragraph [0040]-BALDWIN discloses the CHM raster includes the height above ground (HAG) values at each pixel (i.e., height per pixel), representative of each location of the given plot, for instance, plot 108a in this example (and also similarly for the other plots 108b-d) (wherein the CHM is the height characteristic index). Further in paragraph [0064]-BALDWIN discloses the CHM includes orange colored regions indicative of the height of the vegetation in the field and then dark colored regions indicative of the ground, both of which are bounded by the plot geometries (e.g., plots as indicated by the generally rectangular boundary lines/grid, etc.) of the field (e.g., as defined by the planter during planting, etc.) (wherein a pixel is a first grid).),
BALDWIN in view of BAINBRIDGE fail to explicitly teach removing at least one head column of target crop grids and at least one tail column of target crop grids in the column divided result.
However, YAO explicitly teaches removing at least one head column of target crop grids and at least one tail column of target crop grids in the column divided result (Paragraph [0068]-YAO discloses a lowest layer (10.sup.th layer, soil layer) and a highest layer (1.sup.st layer, top layer) were removed, and the point clouds of a top layer (2.sup.nd layer) and a bottom layer (9.sup.th layer) of the 8 remaining layers (10−2=8) were respectively marked by attribute fields: the leaf layer (2.sup.nd layer) was marked as “1”, and the stem layer (9.sup.th layer) was marked as “0” (wherein the highest layer is the head column and the lowest layer is the tail column).),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of YAO of removing at least one head column of target crop grids and at least one tail column of target crop grids in the column divided result.
Wherein having BALDWIN’s method of determining crop height having removing at least one head column of target crop grids and at least one tail column of target crop grids in the column divided result.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and YAO relate to measuring and monitoring crop fields through the use of LIDAR point cloud data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while YAO the method of the present invention is simple, is convenient to operate, and provides theoretical basis and technical support for the rapid and nondestructive extraction of a field wheat stem tillering number. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and YAO et al. (US 20220189053 A1), Paragraph [0036].
BALDWIN in view of BAINBRIDGE and further in view of YAO fail to explicitly teach wherein the first grid is determined as follows: removing at least one head row of target crop grids and at least one tail row of target crop grids in the row divided result, thereby obtaining the first grid.
However, ROJAS explicitly teaches wherein the first grid is determined as follows (Fig. 4. Paragraph [0022]-ROJAS discloses FIG. 4 illustrates a further filtering of the LiDAR return data by determining those LiDAR data points that are both within the buffers zones around the planting lines and are within the boundaries of the plot 50 to be analyzed (wherein plot 50 is the first grid).):
removing at least one head row of target crop grids and at least one tail row of target crop grids in the row divided result (Fig. 4. Paragraph [0022]-ROJAS discloses FIG. 4 illustrates a further filtering of the LiDAR return data by determining those LiDAR data points that are both within the buffers zones around the planting lines and are within the boundaries of the plot 50 to be analyzed (wherein filtering is removing and rows 160a, 160b and 160i are head rows and tail rows that are removed).), and
thereby obtaining the first grid (Fig. 4. Paragraph [0022]-ROJAS discloses FIG. 4 illustrates a further filtering of the LiDAR return data by determining those LiDAR data points that are both within the buffers zones around the planting lines and are within the boundaries of the plot 50 to be analyzed (wherein plot 50 is the first grid).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of BALDWIN in view of BAINBRIDGE and further in view of YAO of a method for calculating a plant height uniformity of a crop population, comprising following steps: acquiring three-dimensional (3D) point cloud data of a target crop population; segmenting the 3D point cloud data of the target crop population to obtain 3D point cloud data of each target crop plot; determining a plant height uniformity of each target crop plot according to 3D point cloud data of the target crop grid with the teachings of ROJAS of wherein the first grid is determined as follows: removing at least one head row of target crop grids and at least one tail row of target crop grids in the row divided result, thereby obtaining the first grid.
Wherein having BALDWIN’s method of determining crop height having wherein the first grid is determined as follows: removing at least one head row of target crop grids and at least one tail row of target crop grids in the row divided result, thereby obtaining the first grid.
The motivation behind the modification would have been to obtain method of/system for determining crop height that enhances the accuracy in crop height measurements, provides a user with the necessary data to improve crop field output, and reduces the human labor required to manage a crop field. Since both BALDWIN and ROJAS relate to measuring and monitoring plants to generate models based on LIDAR data, wherein BALDWIN the canopy height models may then be used as a basis to create yield adjustments for the fields and/or between different fields (e.g., neighboring fields, adjacent fields, etc.) to provide for more accurate comparisons of the crops in the different fields (e.g., impact of neighboring crop height on yield in the fields, etc.). In this way, the updated and/or adjusted height data for the crops may be incorporated into subsequent crop-based decisions to obtain more accurate results, while ROJAS there is need for a technique that can better identify young trees from surrounding vegetation with remotely sensed data. Please see BALDWIN et al. (US 20240032454 A1), Paragraph [0019-0020], and ROJAS (US 20140107927 A1), Paragraph [0003].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
ANDERSON et al. (US 20220138925 A1) - An electronic data processor is configured to estimate a spatial region of interest of plant pixels of one or more target plants in the obtained image data for a harvestable plant component and its associated harvestable plant component pixels of the harvestable plant component. The electronic data processor is configured to identify the component pixels of a harvestable plant component within the obtained image data of plant pixels of the one or more target plants. An edge, boundary or outline of the component pixels is determined. The data processor is configured to detect a size of the harvestable plant component based on the determined edge, boundary or outline of the identified component pixels. A user interface is configured to provide the detected size of the harvestable plant component for the one or more target plants as an indicator of yield of the one or more plants or standing crop in the field…Abstract, Fig. 1A-1D.
PAPANIKOLOPOULOS et al. (US 20190274257 A1) - Systems, techniques, and devices for detecting plant biometrics, for example, plants in a crop field. An imaging device of an unmanned vehicle may be used to generate a plurality of images of the plants, and the plurality of images may be used to generate a 3D model of the plants. The 3D model may define locations and orientations of leaves and stems of plants. The 3D model may be used to determine at least one biometric parameter of at least one plant in the crop. Such detection of plant biometrics may facilitate the automation of crop monitoring and treatment…Abstract, Fig. 1 and 3.
HEARST et al. (US 11334986 B2) – A method for processing images of an agricultural field is disclosed that enables accurate phenotype measurements for crops planted in each research plot of the agricultural field. The method comprises receiving a plurality of input images of the agricultural field, calculating and refining object space coordinates for matched key points in the input images and an object space camera pose for each input image, calculating and refining object space center points for the research plots based on a user-defined plot layout, and generating output images of individual research plots that are centered, cropped, orthorectified, and oriented in alignment with planted rows of crops. Based on the output images, accurate phenotype measurements for crops planted in each research plot can be determined. The method advantageously minimizes row-offset errors, variations in canopy cover and color between images, geometric and radiometric distortion, and computational memory requirements, while facilitating parallelized image processing and analysis…Abstract, Fig. 1.
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/ETHAN N WOLFSON/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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