Plant Disease Classification and Segmentation Using a Hybrid Computer-Aided Model Using GAN and Transfer Learning

Plants are essential for life on earth, providing various resources and are helpful in maintaining ecosystem balance. Plant diseases result in reduced crop productivity and yield. Manual detection and classification of plants diseases is a crucial task. This research presents a hybrid computer aided model for plant disease classification and segmentation. In this research work we have utilized PlantVillage dataset with 8 classes of plant diseases. The dataset was annotated using a Generative Adversarial Network (GAN), four transfer learning models were used for classification, and a hybrid model is proposed based on the pretrained deep learning models. Instance and semantic segmentation were used for localizing disease areas in plants, using a hybrid algorithm. The use of GAN and transfer learning models, as well as the hybrid approach for classification and segmentation, resulted in a robust and accurate model for plant disease detection and management in agriculture. This research could also serve as a model for other image classification and segmentation tasks in different domains. Proposed hybrid model achieved the promising accuracy of 98.78% as compared to the state-of-the-art techniques.