An Extensive Approach to Detect Apple Fruit Diseases using Neural Convolutional Learning with Classification Principles

Apple fruit diseases significantly impact agricultural productivity and quality, making early and accurate disease detection critical. This study proposes a novel neural convolutional learning approach to detect apple fruit diseases, leveraging the Apple Disease Detection Dataset comprising images of apple leaves affected by diseases such as Apple Scab, Black Rot, Cedar Apple Rust, and Healthy samples. The methodology integrates pre-trained convolutional neural networks (CNNs) with transfer learning to enhance detection efficiency. Rigorous preprocessing techniques, including image resizing, normalization, and augmentation, were applied to improve data quality and model robustness. The model was trained and evaluated using a dataset split into training, validation, and test sets, achieving an exceptional accuracy of 98.69% on the test data. Metrics such as precision, recall, F 1-score, and ROC-AUC further validated the model's performance, with average precision, recall, and F 1-scores exceeding 98%. Grad-CAM heatmaps provided interpretability by highlighting disease-specific regions, ensuring reliable predictions. The proposed approach demonstrates high potential for real-world deployment, offering farmers an effective tool for early disease detection. Future work includes integrating this model into mobile applications and expanding its capabilities to identify other plant diseases.