Pepper Leaf Disease Detection Using Deep Learning Techniques

Early diagnosis of pests and plant diseases is crucial for preventing significant crop losses. This study proposes a leaf disease detection system using MobileNetV2 integrated with multiple optimization techniques (Adam and learning rate scheduling). Evaluated on the Pepper PlantVillage dataset, the MobileNetV2 model employs patch embedding and attention mechanisms for feature extraction, with SoftMax used for final classification. The model was further validated on a multi-class Apple PlantVillage dataset. Results demonstrate high accuracy: 97.03% for pepper and 94.63% for apple classification. Comparative analysis with CNN architectures shows superior efficiency and faster convergence for our model, outperforming Inception v3 (96.81%) and VGG-19 (94.93%) on the pepper dataset.