Disease Recognition in Sugarcane Crop Using Deep Learning

Abstract

Crop diseases recognition is one of the considerable concerns faced by the agricultural industry. However, recent progress in visual computing with improved computational hardware has cleared way for automated disease recognition. Results on publicly available datasets using convolutional neural network (CNN) architectures have demonstrated its viability. To investigate how current state-of-the-art classification models would perform in uncontrolled conditions, as would be faced on site, we acquired a dataset of five diseases of sugarcane plant taken from fields across different regions of Karnataka, India, captured by camera devices under different resolutions and lighting conditions. Models trained on our sugarcane dataset achieved a top accuracy of 93.40% (on test set) and 76.40% on images collected from different trusted online sources, demonstrating the robustness of this approach in identifying complex patterns and variations found in realistic scenarios. Furthermore, to accurately localize the infected regions, we used two different types of object-detection algorithms—YOLO and Faster R-CNN. Both networks were evaluated on our dataset, achieving a top mean average precision score of 58.13% on the test set. Taking everything into account, the approach of using CNN’s on a considerably diverse dataset would pave the way for automated disease recognition systems.