Deep learning based apple leaf disease detection using spatially modulated continuouslayer

Early and accurate detection of apple leaf diseases is critical for sustainable agriculture, yet manual diagnosis remains time-consuming and error-prone. This study introduces a novel deep learning framework centered on a custom ContinuousLayer, a spatially adaptive convolutional layer designed to overcome the limitations of standard CNNs. This architecture automates the classification of apple leaf diseases Black rot, rust, scab, and healthy leaves with high precision. The model addresses dataset imbalance through strategic resampling, achieving uniform class distribution. The ContinuousLayer introduces spatial feature modulation using trainable Gaussian basis functions, enhancing feature extraction while penalising kernel irregularities through a hybrid composite loss function. Trained on a dataset of 3,164 images balanced via bicubic up-sampling, and evaluated on a held-out test set of 10% of the data, the model attains a 98.63% test accuracy, with F1-scores ranging from 0.98 to 1.00 across classes. Visual analysis of the confusion matrix reveals minimal misclassification, predominantly between rust and scab. Comparative evaluation against baseline architectures demonstrates the efficacy of the ContinuousLayer in capturing disease-specific spatial patterns. These results underscore the potential of integrating mathematically inspired layers into CNNs for plant pathology applications, offering a highly accurate tool for precision agriculture in controlled environments.