EARLY RECOGNITION OF MUNG LEAF DISEASES BASED ON SUPPORT VECTOR MACHINE AND CONVOLUTIONAL NEURAL NETWORK

Abstract

This paper proposed a model that Classifies a Mung (Vigna mungo L.) leaf to check if it is healthy or infected with a disease with the aid of Machine Learning and Deep Learning algorithms. The dataset is created in a controlled environment, where a controlled environment is a data item (image) that comprises only a single subject (leaf) and a white background collected from the south Gujarat Region in India. SVM and CNNs with different architectures have been trained and compared to each other. It aimed at detecting 3 mung leaf disease categories and a healthy leaf category. The model extracts complex features of various diseases. Comparative experiment results show that in the proposed work SVM overfit the data and CNN achieves 95.05% identification accuracy on the Mung leaf image dataset. Early detection will help farmers to improve their productivity. The main objective was to automate Mung Leaf disease identification using advanced deep learning approaches and image data.