UAS and UGV-Based Disease Management System for Diagnosing Corn Diseases Above and Below the Canopy Using Deep Learning

Abstract. Early disease management following the onset of disease symptoms is crucial for controlling their spread. Heterogenous collaboration between unmanned aerial systems (UAS) and unmanned ground vehicles (UGV) for field scouting and disease diagnosis is being viewed as a potential approach for developing automated disease management solutions. However, automation of crop-specific disease identification requires the use of above and below-canopy sensors and properly trained deep learning (DL) models. This research proposes to develop a novel disease management system for diagnosing corn diseases from above and below the canopy by collaboratively using edge devices mounted on UAS and UGV, respectively. Three separate datasets were first acquired using UAS above the canopy, UGV below the canopy, and handheld imaging platforms within diseased corn fields. DL-based image classification models were first trained for identifying common corn diseases under field conditions resulting in testing accuracies of up to 95.04% using the DenseNet169 architecture. After creating bounding box annotations for disease images, You Only Look Once (YOLO)v7 DL-based object detection models were trained to identify diseases from each platform separately. After training multiple YOLOv7 models, the highest mAP@IoU=0.5 of 37.6%, 46.4%, and 72.2% were achieved for locating and identifying diseases above the canopy using UAS, below the canopy using UGV, and handheld sensors, respectively. A client/server architecture was developed to establish communication between the UAS, UGV, and Google Spreadsheets via Wi-Fi communication protocol. The coordinates of diseased regions and distinct disease types were recorded on Google Spreadsheets using the client/server architecture. A web application was developed to utilize the data from the Google Spreadsheet to help users diagnose diseases in real-time and provide them with recommendations for implementing appropriate disease management practices. Overall, this study reports findings of a collaborative UAS and UGV-based corn disease management system will help control disease spread and overcome yield losses.