Secure and Decentralized Apple Leaf Disease Identification Using DL Integration Models

Smart agriculture makes use of advanced technologies to detect diseases and securely store gathered agricultural data. Apple farming depends on the climate, so farms can be affected easily. This proposed work is focused on apple leaf disease identification. Through a Blockchain platform with the capability of smart contracts, acquired datasets are passed into a Deep learning (DL) model for processing. DL and Blockchain technology are integrated to create a smart, secure, and decentralized platform. Convolutional neural network (CNN) models were trained, including VGG16, VGG19, ResNet50, ResNet101, and MobileNetV2. Integrate the better-performed CNN models for apple leaf disease classification. Analysis of the results reveals that ResNet50 and MobileNetV2 achieved 92.48 percent accuracy. Blockchain provides a trustworthy and decentralized environment for all of the collected data and processed DL models. Cloud infrastructure is used to store massive amounts of data for extended periods.