Abstract
Early and accurate diagnosis of apple leaf disease is a prerequisite for maintaining crop health and for enhancing agricultural productivity. Conventional methods, which largely relied on human inspection or naive machine learning algorithms, were not capable of handling the complexity of patterns, the class imbalance, and the real-world challenges such as conflated symptoms or poor lighting. The present study develops a completely new model design by integrating a ConvNeXt model along with a modified rime optimization algorithm (MRIME) used for hyperparameter tuning as well as complementing through the Convolutional Block Attention Module (CBAM) to ensure better feature extraction. CBAM extends the power of the model in focusing on critical discriminative regions, while MRIME gives optimal values for relevant hyperparameters for generalization while avoiding overfitting. Evaluated by the Apple Leaf Disease Symptoms Dataset, the proposed approach attained an accuracy of 92.7%, precision of 92.5%, recall of 92.6%, F1-score of 92.5%, and mAP of 92.3%, surpassing most baselines including ResNet50 and EfficientNet-B0. Compared to the aforementioned baselines, ablation experiments demonstrated that CBAM led to about 1.5% enhancement in accuracy, while MRIME could boost performance by another 1.2% via hyperparameter tuning. These results confirm the complementary benefit of attention mechanisms and metaheuristic optimization in producing state-of-the-art results.