Deep Learning for Leaf Disease Detection using CNN

Abstract

Agriculture is the backbone of the Indian economy. About 70% of people rely on it and share a large portion of GDP. Diseases in plants especially in the leaves affect the reduction of both quality and quantity of agricultural products. The human eye is not so powerful to detect minute differences in the infected part of the leaf. In this paper, we offer a software solution to automatically detect and diagnose plant leaf diseases. In this we use image processing techniques for the diagnosis and early diagnosis can be done as elsewhere. This approach will improve crop production. It involves several steps. Picture detection, pre-image processing, percentage separation, extraction and neural features. Recently, many researchers have advocated since the success of in-depth computer literacy the idea of improving the effectiveness of diagnostic programs. Unfortunately, most of these studies did not use the latest in-depth formats and were based on AlexNet, GoogleNet or similar properties. Moreover, deep-seated mechanisms do not exist to take advantage of it, making these deep divisions invisible and suitable as black people boxes. In this project, we tested the many technological approaches of the Convolutional Neural Network (CNN) buildings using various learning strategies in the public database of plant diseases separation. These new structures exceed the high-quality effects of plant diseases in stages with very high accuracy. In addition, we have suggested the use of saliency maps as a way to visualize and interpret CNN classification.