Artificial Intelligence based Precise Disease Detection in Soybean using Real Time Object Detectors

Background: Soybean, a crucial oilseed crop in India, is susceptible to various diseases, including Yellow Mosaic Disease caused by Mungbean Yellow Mosaic Virus (MYMV). Early detection of plant leaf diseases is a major necessity for controlling the spread of infections and enhancing the quality of food crop. Therefore, this study aims to evaluate the efficacy of YOLOv8 for detecting Mungbean Yellow Mosaic Virus in soybean crops. Methods: Images were captured using 48 MP mobile camera from the experimental fields of Main Agricultural Research Station, University of Agricultural Sciences, Dharwad during 2024 summer season. The captured images were annotated, augmented using various transformations, resized and normalized. As a result, a dataset of 4480 annotated images of MYMV-infected soybean plants was created. The dataset was randomly split into training and validation set. Different variants of YOLOv8 models were trained and evaluated based on precision, recall, mean Average Precision (mAP) and inference time. Result: Among the variants, YOLOv8x demonstrated superior performance, with a precision of 98.1%. Whereas the recall (78.6%), [email protected] (85.3%) and F1-score (86.9%) were higher in YOLOv8s. YOLOv8n boasted the lowest inference time of 3.6 milliseconds and the least number of model parameters (3.1 millions). YOLOv8s shows promise as an efficient and accurate tool for MYMV detection in soybean crop. Its low inference time and high precision make it suitable for real-world deployment, aiding in early disease detection and precise management of the disease. This study contributes to advancing disease detection techniques in agriculture, facilitating early intervention and minimizing crop losses for sustainable agricultural practices.