| CPC G06T 7/001 (2013.01) [G05D 1/0033 (2013.01); G06F 18/214 (2023.01); G06F 18/285 (2023.01); G06N 3/0418 (2013.01); G06N 3/08 (2013.01); G06V 10/255 (2022.01); G06V 10/74 (2022.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 20/188 (2022.01); G06V 20/20 (2022.01)] | 20 Claims |
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1. A method implemented using one or more processors, comprising:
obtaining a first digital image that captures a depiction of a first individual plant of a plurality of plants growing in an agricultural parcel;
processing the first digital image to generate a bounding shape that encloses at least a portion of the first individual plant;
determining one or more spatial dimensions of the bounding shape;
applying the first digital image and the one or more spatial dimensions of the bounding shape as inputs across one or more machine learning models to generate output, wherein one or more of the machine learning models is trained using a plurality of training instances, and wherein each training instance includes one or more training digital images of a particular plant and one or more spatial dimensions of a bounding shape that encloses at least a portion of the particular plant in the one or more training digital images;
comparing the output to data indicative of a plurality of digital images captured previously to the first digital image in the agricultural parcel;
based on the comparing, matching the depiction of the first individual plant captured in the first digital image to one or more depictions of the same first plant captured in a subset of one or more of the previously-captured digital images; and
based on the matching, storing in memory an association between the first digital image that captures the depiction of the first individual plant and the subset of one or more previously-acquired digital images that capture the one or more matching depictions of the first individual plant.
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