Confidence-Driven Hierarchical Classification of Cultivated Plant Stresses

Logan Frank, Christopher Wiegman, Jim Davis, Scott Shearer; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2021, pp. 2503-2512

The application of convolutional neural networks (CNNs) and deep learning to different domains has become increasingly popular in the last several years. In particular, such models have been used in the agriculture domain to identify plant species, identify plant stresses, and estimate crop yields. Although there has been much success in applying these techniques to the agriculture domain, these works contain many shortcomings that are hindering their chance for adoption in practice (e.g., lack of domain knowledge, predicting only specific stress types, etc.). We address issues of previous works for the task of plant stress identification by applying a hierarchical classification approach employing confidence as a means to determine the specificity of a classification. This work is a collaboration between computer science and agricultural engineering experts.