Artificial Intelligence in Agriculture for Leaf Disease Detection and Prediction: A Review

Abstract

Gross Domestic Product (GDP) of nations mostly depends on agriculture sector. Use of engineering technology for farmers and rural development playing important role in 21^th century. Technology such as Deep Learning, Machine Learning and Artificial Intelligence are preferred for leaf disease detection. In the growing stages of crops control on leaf diseases are most important step. The disease detection and analysis at early stage for unhealthy leafs with solution always support of agriculture development. Our survey largely focus on related tools, technology, and different deep learning model. The improvement in percentage of accuracy is target and main goal for leaf diseases detection. The main approach is predict accurate type of disease occurred on tomato, soyabean and grapes leaf at early stage. Our study aim to address the issue using the Deep Learning (DL) techniques. Real time gathering of diseased leaf images and process using Convolution Neural Network (CNN) are the crucial step in proposed research. Convolution Neural Networks detect the diseased leaf and provide the analysis over it with better accuracy on real datasets. An agriculture surveying for disease detection playing unique role for farmer’s community. Ultimately all this supports and helps for increase the profit in the agriculture which always motivate the farmers for farming.