Cotton pests classification in field-based images using deep residual networks

Abstract

Pests in agriculture are a major cause of losses in crops worldwide. Cotton is an important source of textile fiber, and insect pests and their management are the highest variable cost in cotton production. Identifying the most harmful cotton pests in field conditions helps acting for environmentally acceptable and yet economically effective strategies. This research proposes a classification system for major cotton pests (primary and secondary). A new ground-truth dataset containing RGB cotton field images is presented, along with a novel deep residual learning design to classify major pests automatically from given images. Performance is evaluated against other convolutional neural networks and the proposed ResNet34∗ model achieved the highest accuracy with f-score of 0.98.