A Review of Deep Learning Techniques for Identification and Diagnosis of Plant Leaf Disease

Abstract

This paper focuses on review of deep learning techniques on plant leaf disease detection and its diagnosis. As we know agricultural farming plays a crucial role in world economy but the sad part is, it is adversely affected by damage due to diseases to plants all around the world. It is observed, plants are one of important source of energy for human being as well as other animals, and hence it is prime to save not only plant but their leaves too. It is then important to identify infected leaves which are affected by disease; this will be great help for farmers to protect it from sowing till process of harvesting which can reflect in reduction in economic loss. But manual work in this regard will be absolute burden on labour hence Automatic identification of diseases and it’s diagnose will be add-on tool for agricultural yield and it will also help to maximize the production of crop. In this paper, comparative analysis between the performances of distinguish deep learning approaches for identification and diagnosis of various diseases on plant leaves with the help of different patterns of plant leaf images is discussed. In many experiments and evaluations, process of segmentation, feature extractions and classification methods are being done for quick diagnosis on selected plant leaf diseases. Here, we are trying to help farmers to identify and diagnose the disease on banana leaves by using deep convolutional neural network which can be treated at early stage.