Modeling Convolutional Neural Network for Detection of Plant Leaf Spot Diseases

Abstract

Farming has come a long way further than directly a system to provide food to an ever-increasing population. The seventieth population of Asian countries depends on agriculture. The identification of plant leaf diseases is typically carried out with the inspection by masters and organic tests. Both techniques are manual, costly and time-consuming. Detecting plant leaf diseases is a challenging task since it involves numerous variables such as genotype, environment, and their interplay. Accurate detection of plant leaf diseases involves a fundamental understanding of functional relativity and the collaborative factors. Deep learning helps in recognition of plant diseases and it provides earlier and timely detection of plant diseases for efficient disease management. It expresses a different infusion that gives systematic and automated detection of diseases. A convolutional neural network model is designed to use for detection of leaf spot diseases in plants. A tomato plant disease dataset has been generated from the local farms of Chittoor District, Andhra Pradesh, India. The tomato leaf spot diseases considered for training are Septoria leaf spot, bacterial spot, target spot and leaf mold which are most popular diseases in tomato plants. The average classification accuracy of the proposed model is determined to be 90.6%. For comparative analysis, the proposed CNN model has been experimentally evaluated with Multilayer perceptron classifier model. The proposed model produced an effective average MSE and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values of 0.016 and 0.991. The MLP model produced less optimal results than the proposed model thus confirming the efficacy of the proposed model in classifying the plant leaf diseases.