Image Based Plant Disease Detection Model Using Convolution Neural Network
E. Anupriya, Manyam Thaile, P. Chinnasamy, Papani Muni Yasaswini
Abstract
Agriculture is the prime sector of India’s economy and stands as one among the top two crop producers in the whole world. Farm production is very important and plays a primary part in agribusiness sector as it contributes around 18.1 percent to the country’s GDP. The less production of annual food rate is mainly dependent to infertile farmland and infected crops. The identification of crop infections in the area of agricultural sector is exceptionally demanding. When the detection is not accurate, the crop production may decrease and the market value of that product may affect a drastic loss. This indicates the need of new techniques which help in the detection of crop infections and its growth. Hence proposing a new model by the use of convolution networks by classification of infected leaves. This proposed system can detect thirteen different and complex kinds of crop infections by differentiating them from healthy leaves. This proposed model of detection and prediction of plant diseases is mainly introduced to focus on the agricultural and agro-based business. This helps in the early detection and identification of crop diseases which helps the farmers to take necessary precautions or measures that in turn can help with the crop productivity.