A Hybrid CNN-CapsNet Pipelined Approach For Disease Diagnostic With Severity Estimation

This research presents a novel and robust approach for the disease classification of leaves by integrating YOLOv9 with attention-enhanced CNNs such as VGG-19, EfficientNetV2B0, and ResNet101, along with a hybrid Capsule Network (CapsNet) built on a ResNet50 backbone with a lightweight attention mechanism. YOLOv9 is trained to detect and segment individual leaves from input images, each of which is then passed to the enhanced CNNs or the hybrid CapsNet for disease classification and severity estimation. The proposed hybrid approach emphasizes local hierarchical features while preserving base CNN-extracted features, representing a significant advancement over existing methods, as such a pipelined integration of YOLOv9 with CapsNets has not been considerably explored in prior literature. The enhanced CNN models achieved high validation accuracies, with VGG-19 reaching 98.69%, EfficientNetV2B0 achieving 99.68%, and ResNet101 scoring 99.55%, while the hybrid CapsNet outperformed all with an accuracy of 99.81%. The disease classification was conducted using the publicly available PlantVillage dataset consisting of 67,000 images, while a custom dataset for leaf detection was curated from Kaggle and Roboflow sources. The proposed method not only improves classification accuracy but also contributes to practical agricultural benefits by enabling early disease detection and severity analysis, potentially reducing pesticide usage and enhancing crop management.