Segmentation-Aware Hybrid CNN-ViT for Plant Leaf Disease Classification

Abstract

Diseases that affect plant leaves have a big effect on food supply and agricultural output. This makes it very important to find accurate and automatic ways to diagnose these diseases. This paper presents a segmentation-aware hybrid deep learning (DL) approach that integrates both Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for the effective classification of plant leaf diseases. An unsupervised K-means clustering method is used first to make rough segmentation masks that don’t need manual labels. Then, a U-Net model is used to make them better. The combination of CNN and ViT is used to get at the segmented leaf images and get both main disease-related features and a broader context. Gradient-weighted Class Activation Mapping is used to show which parts of the leaf affect the diagnosis. This makes it easier to understand the model’s decisions. We test the framework on the PlantVillage dataset to see how well it works and then again on the Plant Disease Detection dataset to see how it works with different types of this data. The results show that the disease classification is better, that the system works better when the data sources change, and that the visual insights are very clear. These results show that the suggested approach is a dependable and comprehensible solution for the automated plant disease identification and can facilitate precision agriculture.

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