Deep learning, image classification, crop stubble, ground cover, canopy cover


In agricultural fields, knowledge about the proportion of the soil surface covered with crop residue and vegetation canopy is key for improving soil and water conservation practices. In this study we trained a deep convolutional neural network to automate the classification of bare soil, crop stubble, and live vegetation from downward-facing images of agricultural fields. A comprehensive generic dataset, consisting of 3300 training and 645 test images, was collected from agricultural fields across Kansas State University Agricultural Experiment Stations and the Natural Resources Conservation Service Plant Material Center located near Manhattan, KS. Despite the intricate patterns and color textures resulting from different combinations of soil, canopy, and stubble the trained network showed good performance for automating the classification of land cover from images. The network achieved 87% accuracy over the training dataset and 84% accuracy over the test set.


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