Cotton Crop Disease Detection on Remotely Collected Aerial Images with Deep Learning

Cotton root rot (CRR) is a serious cotton disease primarily found in southwestern U.S., causing an average annual loss of about 29 million USD in Texas alone. Therefore, management of CRR infected cotton fields is crucial to the U.S. and Texas cotton industry. CRR usually appears at similar regions of the cotton fields each year, so detecting the locations of infected regions can make the management practices efficient for multiple growing seasons. Previous methods of mapping the regions of CRR involved classical image processing techniques like unsupervised machine learning methods, which are not viable for real-time detection. In this preliminary study, we present a deep-learning (DL) based method using YOLOv5 to detect the CRR infected regions of a cotton field, and then we demonstrate its ability for real-time detection by deploying it on an edge-computing platform (Pascal GPU of NVIDIA Jetson TX2 development board). In the end, we also show how the locations of detected CRR regions can be used to generate an optimal path for efficient management practices with the ant colony optimization (ACO) algorithm. Our preliminary results showed a moderate level of detection accuracy at a promising average inference speed of 11 frames per second (FPS). The total distance covered based on the optimal path of four detected regions of CRR was 160 m. Hence, through this study we were able to demonstrate that a DL based approach with the ACO algorithm has the potential to speed up management practices of CRR infected cotton fields with multispectral aerial imagery.