Deep Learning for Plant Leaf Disease Detection and Diagnosis Using Deep Convolution Neural Network

Abstract

ABSTRACT: Farming industry is the back-bone of any economy and it depends upon the individual farmers yield. This yield is often impacted due to micro-level diseases which occur during the growth of a particular fruit-bearing plant. For example, Fusarium oxysporum, Mycosphaerella musicola, Gloeosporium musae, Erwinia Carotovora, Pseudomonas Solanaceanim, Pentalonia nigronervosa, Erionota thrax, Banana Streak Virus and Banana Bract Mosaic are some pathogens influence the cultivation of bananas only by viruses. These viruses are innumerable, and therefore progresses in processing the images and other informative resources are needed in order to classify and put forward therapies for such viruses. A major part of the study in this area focuses on unseparated strategies for segmenting, extracting features and classing to diagnose visually visible conditions. However, these methods are not appropriate in the case of bigger and deeper databases, so that in all components of the computing layers machine learning and artificial intelligent approaches like Q-learning, re-enforcement learning and so on detects diseases not apparent to a human eye. Because of such different diseases and so many processing algorithms, machine designers are sometimes uncertain about how to identify what kind of diseases by combining algorithms. Moreover, leaf-based diseases are an indicative of the yield quality for many farming sectors. These sectors include but are not limited to banana farming, mango farming, and many others. To be able to design to facilitate, improve the quality of yield, detection and prevention of these diseases is of utmost importance. Thus, various image processing solutions have been proposed over the years for effectively detecting leaf-based diseases. After detecting the leaf-based diseases it is observed that yield prediction from leaf imagery is an important computer vision research issue. An efficient yield prediction system is helpful on many fronts, which include the agriculture industry, the cattle farming