Fine-Tuned EfficientNetB0 Transfer Learning Model for the Detection and Classification of Peach Leaf Diseases

Peach trees are vulnerable to numerous diseases impacting fruit production and quality. Early and accurate disease detection is essential for managing these diseases effectively. Traditional manual inspection methods are labor-intensive and prone to human error, underscoring the need for automated approaches. In this study, a fine-tuned EfficientNetB0 transfer learning model is developed for detecting and classifying peach leaf diseases. The proposed model leverages the strengths of the EfficientNetB0 architecture, which balances computational efficiency and accuracy, to classify peach leaf conditions, including healthy and diseased leaves. A dataset of 2,635 peach leaf images is used to train and validate the model. Results show that the model achieves high accuracy, precision, recall, and F1-scores, making it a reliable tool for automated disease detection. This research contributes to precision agriculture by providing a scalable and efficient solution for disease management in peach cultivation, potentially reducing the need for manual inspection and improving agricultural productivity.