Watermelon leaf disease detection and classification using Yolo architecture

Abstract

Watermelon is a fruit that people all over the world enjoy. However, eating watermelon is mostly in the summertime because the juicy and cooling effect to the body it has is highly appreciated. In countries like Bangladesh and other countries with long, hot summers, the demand for watermelon fruit is more or less similar. The yield quality is compromised due to several leaf diseases affecting watermelon production and causing a decrease in value of financial returns. Thus, very soon and accurate diagnosis of these diseases becomes very critical in order to minimize the losses and ensure sustainable agriculture. The deep learning-based approach of the study uses the YOLOv11 model for real-time detection and classification of watermelon leaf diseases. For that, a balanced dataset of healthy and diseased watermelon leaf images was collected and further added to for improvement of model performance. The specifications are that its detection speed and accuracy is very good while being lightweight in its design; because of that, the YOLOv8 architecture was selected. The model's robust precision under challenging field conditions in identifying multiple classes of diseases was attained with optimal learning parameters. Precision was achieved to be 93.3%; Recall 87.4%, mAP50, and mAP50-95 were 96.4% and 81.5% respectively. A web- based application was developed for realtime disease detection in uploaded leaf images to ensure easy reach and use for farmers and agricultural experts. The system presented in this integrated approach contributes promise toward smart agriculture through enhanced crop monitoring in favor of food security and economic stability.