MULTILAYER CONVOLUTION NEURAL NETWORK FOR THE CLASSIFICATION OF MANGO LEAVES INFECTED BY ANTHRACNOSE DISEASE

Abstract

Fungal diseases not only influence the economic importance of the plants and its products, but also abate their ecological prominence. Mango tree, specifically the fruits and the leaves are highly affected by the fungal disease named as Anthracnose. The main aim of this article is to develop an appropriate and effective method for diagnosis of the disease and its symptoms, therefore espousing a suitable system for an early and cost-effective solution of this problem. Over the last few years, due to their higher performance capability in terms of computation and accuracy, computer vision, and deep learning methodologies have gained popularity in assorted fungal diseases classification. Therefore, for this work a Multilayer Convolutional Neural Network (MCNN) is proposed for the classification of the Mango leaves infected by the Anthracnose fungal disease. This work is validated on a real-time dataset captured at the Shri Mata Vaishno Devi University, Katra, J&K, India consists 1070 images of the Mango tree leaves. Dataset contains both healthy and infected leaf images. Results envisage the higher classification accuracy of the proposed MCNN model when compared to the other state-of-the-art approaches.

Existing System

AlexNet and SqueezeNet, Deep Residual Neural Network

Proposed System

A Multilayer Convolutional Neural Network is proposed in this work for the classification of the Mango leaves infected with the fungal disease named as Anthracnose. Acquire the real-time images of the Mango tree containing both diseased and non-diseased leaves and also, images from plant Village dataset. Then Preprocess all the images for contrast enhancement using histogram equalization method and rescaling using central square crop method. Class labels to the images are assigned. Then categorize the images among training and testing dataset selecting from all the class labels. After that Train the CNN with the help of training images and also test the CNN with the help of testing images. Finally detect the disease of the mango using the neural network architecture.

Architecture

BLOCK DIAGRAM