Resnet Based Blockchain Architecture for The Detection of Plant Leaf Disease in Agriculture Field

Abstract

The only way to get better crop yields is to find and treat crop diseases quickly. Deep learning models diagnoses the plant diseases by looking at the leaves. A residual neural network is developed for the detection of disease in maize leaf. The leaves are collected from the available dataset, where the detection architecture is decentralized using blockchain architecture. The residual neural network with decentralized blockchain enables an optimal classification of instances. The model is implemented with improved disease detection accuracy with reduced training time in a python simulator with keras library. The results of simulation show an improved rate of classification accuracy, precision, recall land f-measure in detecting the leaf disease than the existing convolutional neural network models.