AFS-PLDCNet: An Advanced Computational Tool for the Classification of Apple Leaf Diseases

Abstract

Background: Accurate and timely diagnosis of foliar diseases is the most crucial factor in efforts to maximize crop yield and ensure sustainability. Existing deep learning models, especially single-backbone CNNs, have achieved promising results; however, they often fail to generalize well in different orchard conditions. Methods: In this study, the AFS-PLDCNet framework has been proposed for robust leaf disease classification. This framework uses an attention-based feature-fusion approach to combine deep representations extracted from EfficientNetV2S, MobileNetV2 and ResNet18. A learnable attention mechanism assigns adaptive weights dynamically to each feature and a lightweight meta-learner is used for classification. A new dataset of 9000 apple leaf images was captured in Himachal Pradesh’s orchards, encompassing Alternaria leaf blotch, Marssonina blotch and healthy leaves. Result: The experimental results demonstrate that AFS-PLDCNet achieved superior classification accuracy compared to existing single-backbone CNNs. The proposed model is well-suited for real-time, field-level leaf disease classification and precision agriculture systems.