Detecting and counting sorghum aphid alates using smart computer vision models

Abstract

Sorghum aphid [Melanaphis sorghi (Theobald)] is considered an economic pest causing significant yield losses in susceptible sorghum in the southern U.S. Infestations start with the migration of alates (i.e., winged adults) to sorghum and establishing aphid colonies. In favorable conditions, sorghum aphid can exponentially reproduce via asexual reproduction. A suggested strategy is to monitor alates to determine initial infestations and take preventive strategies, which can result in more efficient pest monitoring and management. To reduce the time of monitoring and better understand of alate establishment under field conditions, we propose using computer vision models, specifically deep learning, to detect and count alates using field-collected images. During pest monitoring, we captured 2527 images and assessed the performance of five models within the YOLOv5 architecture family using two different image sizes, including input resolutions of 640 × 640, and 1280 × 1280 pixels. We trained models to detect and count individual alates, which ranged between 1 and 100 alates/leaf. Among models, the YOLOv5l Pytorch detection model had the best overall performance at 1280 × 1280 input pixel resolution. The YOLOv5l model is a candidate model for quantifying alates on sorghum leaves using deep learning with a precision of 83.80%, 85.60% recall, and 89% mAP@0.5 with a lower mean percent error of misdetection. To enable the use of our best deep learning model by the research community, we developed a web-based application that is freely available to the public. Using this application, users can upload images to detect and count alates with a low error of misdetection.