An Extensive Survey on Deep Learning Approach for Crop Diseases Management

Abstract

Agriculture is backbone of India and major revenue producing sector. But change in climate and its unpredictability directly affect the production and maintenance of crops. Furthermore, condition becomes even worst when the crops are infected by any disease which leads to crop failure. Farmer Suicidal Rate is 11.2% of all suicides in India. Increasing population and decreasing natural resources will make it a challenge to provide adequate food security to the coming generation of human beings. According to the Global Nutrition Report more than 190 million people in India are undernourished and India ranks 102nd out of 117 countries in Global Hunger Index which is in serious category. In such a scenario, there is need to make use of available resources to optimize the production and the quality of agricultural products. Deep Learning has recently entered the agriculture domain to solve various agricultural problems of classification or prediction. Crop disease management will be very useful to the farming community and the Indian society. In this paper there is complete survey of various deep learning methods for crop disease management which include crop disease identification, classification and prediction using data driven approach as well as image data sets have discussed. Early prediction of crop diseases will improve the crop production and prevent the spread of diseases in the surroundings of the infected areas. Several performance metrics are used to evaluate the crop disease classification or prediction models. In addition, some agriculture research questions, and their solutions are provided.