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Plant diseases pose a significant threat to global food security, with annual losses amounting to billions of dollars. Early detection and intervention are critical for mitigating these losses. Traditional methods of disease detection, such as visual inspection, are time-consuming, labor-intensive, and often inefficient. This paper presents an advanced approach using the YOLOv5 (You Only Look Once) model, a state-of-the-art object detection algorithm, to identify and classify plant diseases in real-time. The YOLOv5 model offers high accuracy and efficiency, particularly for tasks requiring simultaneous detection of multiple objects, making it ideal for agricultural applications. This paper explores using novel data collection methods, such as mounting cameras on Husky UGV (Unmanned Ground Vehicle), to capture diverse images of crops from various angles in the field. These autonomous systems enhance dataset variety, contributing to more accurate detection results. Our methodology focuses on training the YOLOv5 model using annotated datasets, which enables the model to identify diseases such as apple rust leaf, bell pepper leaf spot, and others. The experimental results demonstrate a detection accuracy of 92 %, showcasing the potential of YOLOv5 for realtime crop disease monitoring. Integrating robotics for field analysis is further discussed as a future direction, providing a scalable solution for continuous crop monitoring and disease management.
Tanveer et al. (Thu,) studied this question.