Advanced Potato Leaf Disease Classification Using YOLOv9: A Deep Learning Approach

Although potato is an important global crop, it suffers from a range of diseases that can cause substantial yield losses. Despite being a significant crop worldwide, potatoes are vulnerable to various diseases that can lead to significant decreases in production. The timely and accurate detection of diseases is crucial for managing crops effectively. This paper outlines a fresh approach for real-time potato disease classification using the YOLOv9 deep learning model. The dataset used to train the model contained both healthy and diseased potato plants, enabling it to identify the main diseases found in potato plants: late blight, early blight, and black leg. The process involved data preprocessing, model training, and evaluation to enhance the model's performance. The findings demonstrate that the YOLOv9 model attains high accuracy and quick inference time, making it suitable for agricultural applications. By utilizing advanced deep learning and substantial computational resources, this study offers a valuable tool to assist farmers and professionals.