DNN Based Plant Diseases Recognition Using Classification of Leaf Images

Abstract

Throughout the area of image processing, the new generation of convolutional neural networks CNNs hasproduced remarkable performances. The paper reflects on a new approach to the production of utilizingdeep convolutionary networks of a model of the identification of plants centered on the picture classificationof the surface. Throughout reality, a modern model of teaching and technique allows it simple and fast tointroduce the program. With the ability to differentiate the plant leaves from the environment, the engineeredmodel will recognise 13 specific forms of plant diseases from safe leaves. This approach for identifyingplant disease was introduced for the first time, according to our understanding. The entire paper outlinesall important measures possible for the introduction of this model for disease identification, beginning withthe picture collection to establish a database reviewed by agricultural experts. The comprehensive CNNpreparation was carried out by Caffe, a fundamental research system developed by Berkley Vision andLearning Center. On average, the experimental results of the built model were 91 to 98 percent reliable forseparate class research.