An Optimized Approach to Identify and Classify Disease in Corn Leaf Using Deep Learning

Abstract

Agriculture is a fundamental pillar of the Indian economy and a primary newlinesource of income for many people. However, agricultural production needs help to meet newlinethe exponentially growing demand for food and environmental challenges. But the newlinedemand for crops is diminished due to various climatic changes and low productivity. newlineHence the farmers and experts are making more efforts to enhance the production of crops newlineall over the world. newlineCorn is considered a significant food crop, and it is the most important crop newlinethat enhances the income of the Indian economy. Compared to other crops, corn provides newlinehuge income to farmers and increases productivity. However, staple food and proper newlineyielding of crops played a challenging role for the farmers. Corn is more vulnerable to newlinedisease than other crops. The most prevalent disease affects the corn leaf, which provides newlinea huge economic loss and also reduces the nutritional value. Also, it gets affected due to newlineclimatic changes and variation in illumination that create a hard situation and cannot newlinepredict the target area of the affected leaf. The treatment of this disease is obtained in a newlinechallenging way and cannot be predicted with the naked eye. This affects the harvest of newlinecorn and the economy of the farmers, so a model is required to produce an efficient output newlinefor the detection of corn leaf diseases. For this purpose, a number of methods are newlineintroduced but these methods focus on the evaluation of the yield, and these methods do newlinenot perform the detection of uncertainties among crops newline