Methods and systems for use in processing images related to crop phenology

A system for use in processing image data associated with one or more plots, the system comprising a computing device including a processor, which is configured, by executable instructions, to:

• access a data set, the data set including images associated with one or more plots, the images having a spatial resolution of better than or equal to one foot by one foot per pixel;
• for each of the one or more plots:
• compare, for each pixel in the images, a first normalized difference vegetation index (NDVI) value of a first image of the plot at time n from the data set to a second NDVI value of a second image of the plot at time n+1 from the data set;
• determine differences of the second NDVI values relative to the first NDVI values;
• in response to a first threshold percentage of the differences being positive, flag at least part of the second image;
• and
• modify the data set by removing the at least part of the second image, based on the second image being flagged, from the data set;
• prepare the modified data set as a series of feature vectors having a length of at least m*n, where m is a number of spectral values included in each of said images in the modified data set, and n is number of image dates which includes said images for the one or more plots in the modified data set, the spectral values including spectral color data from said images and NDVI values;
• access phenotypic data for a crop included in the one or more plots, at a time consistent with the images associated with the one or more plots, the phenotypic data including relative maturity;
• train a model, based on data including the series of feature vectors for the one or more plots, and the accessed phenotypic data, to identify relative maturity of a target crop in a target plot from images of the target plot, whereby weights of the model are fitted to said data;
• and
• store the trained model in memory.

A non-transitory computer-readable storage medium including executable instructions for processing image data, which when executed by at least one processor, cause the at least one processor to:

• access a data set, the data set including images associated with one or more plots, the images having a spatial resolution of better than or equal to one foot by one foot per pixel;
• for each of the one or more plots:
• compare, for each pixel in the images, a first normalized difference vegetation index (NDVI) value of a first image of the plot at time n from the data set to a second NDVI value of a second image of the plot at time n+1 from the data set;
• determine differences of the second NDVI values relative to the first NDVI values;
• in response to a first threshold percentage of the differences being positive, flag at least part of the second image;
• and
• modify the data set by removing the at least part of the second image, based on the second image being flagged, from the data set;
• prepare the modified data set as a series of feature vectors having a length of at least m*n, where m is a number of spectral values included in each of said images in the modified data set, and n is number of image dates which includes said images for the one or more plots in the modified data set, the spectral values including spectral color data from said images and NDVI values;
• access phenotypic data for a crop included in the one or more plots, at a time consistent with the images associated with the one or more plots, the phenotypic data including relative maturity;
• train a model, based on data including the series of feature vectors for the one or more plots, and the accessed phenotypic data, to identify relative maturity of a target crop in a target plot from images of the target plot, whereby weights of the model are fitted to said data;
• and
• store the trained model in memory.