Mapping field anomalies using digital images and machine learning models

One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform:

• obtaining a shapefile that defines boundaries of an agricultural plot;
• based on the shapefile, obtaining a plurality of ground based plot images from one or more image capturing devices mounted at a fixed ground location or a ground vehicle at the agricultural plot;
• calibrating the plurality of ground based plot images;
• stitching the plurality of calibrated ground based plot images into a plot map of the agricultural plot at a plot level;
• generating a plot grid;
• based on the plot grid and the plot map, defining a plurality of plot tiles for the agricultural plot, each of the plurality of plot tiles including multiple pixels of the plurality of calibrated ground based plot images;
• classifying the plurality of plot tiles using a first machine learning model and a plurality of first image classifiers corresponding to one or more first anomalies, into a set of classified plot images that depicts at least one anomaly, wherein each of the plurality of plot tiles is classified into classifications at least corresponding to a crop, a weed, trees, and inter-row damage;
• determining, for each image in the set of classified plot images, a probability that the image is correctly classified, and further comparing the probability to an acceptable probability;
• based on the set of classified plot images, generating a plot anomaly map for the agricultural plot;
• and
• transmitting the plot anomaly map to one or more controllers that control one or more agricultural machines to perform agricultural functions on the agricultural plot.