Efficient Computation of Leaf Disease Classification Techniques using Deep Learning

Abstract

Being a major agricultural country, a considerable amount of development depends on the agriculture of Bangladesh. As agriculture stays one of the main areas of the Bangladeshi economy, Bangladesh is attempting to become independent in producing food by creating successful developing agronomy. At the same time, plant leaf disease is quite natural and sometimes uncontrollable that causes damage of crops, as well as causing significant damage in the agronomy of Bangladesh. To prevent the problem, this work aims to classify several plant leaf diseases, specifically corn, grape, mango, and pepper, to diagnose the leaf diseases for proper early action to cure. We have also been able to classify by means of disease classification as a multi-class classification of those four plant leaves. Therefore, We have used Convolutional Neural Network (CNN) based Deep Learning models to analyze the results, and we have compared the scores of four CNN models: VGG-16, VGG-19, GoogLeNet, and our proposed model. Finally, our proposed model imparted better computation and achieved 99.91% accuracy. Furthermore, we have found that deep learning could be an appropriate approach to classify ill leaves of the plants from the healthy.