The research uses cameras mounted to a vehicle to capture geotagged images while conducting a transect survey. The images from two capture dates were manually classified into different classes of previous crop, tillage systems, residue cover, and cover crop utilization. The raw data was compared against the Indiana Cropland Transect Survey and the USDA-NASS Cropland Data Layer. The symmetric Kullback-Liebler divergence method was used to compared the distributions looking for similarities. <div><br></div><div>The manually classified data was then used to build satellite segmentation models using artificial neural networks , decision trees, k nearest neighbors, random forests, and support vector machine methods. The models were compared using overall accuracy, kappa coefficient, specificity, sensitivity, positive prediction value, and negative prediction value. The best model for each category of previous crop, tillage system, residue cover, and cover crop was used to segment a Sentenial-2 imagery downloaded from Copernicus Open Access hub. The results of the segment were compared by looking at the agreement at individual pixel locations from the segmented raster to the manually classified data and the Indiana Cropland Transect Survey. </div><div><br></div><div>Finally, all the images captured were used to being the development of a automated image classifier using nested convolutional neural networks (CNN). A small set of images was used to build the CNN. That model when then make prediction on new unclassified images. The predictions were manually checked. The check images were used to the to build the training and validation pools for the models. The first network divided the images into field or not field.</div><div>The second branch was field images divided in to images containing green growing plants of brown dead plants or residues. The final branch was determining the amount of surface cover left on a field. The results from each run of the training process were saved and used to assess model performance looking at accuracy and loss.</div>