Performance Analysis of Transfer Learning Model and Prediction of Corn Leaf Diseases

Abstract: Agriculture is one in all the formost significant roles within growth and development of our nation economy. The identification of diseases is that the key of forestall the losses within the yield and quantity of the agriculture product. Diseases detection on the plant is incredibly critical for sustainable agriculture. It’s challenging to watch the plant the plant manually especially people who are new to farming. It requires excessive time interval. Therefore a correct prediction and detection of disease will reduce the utilization of fertilizer within the field, which helps from soil impurities. In this present paper we have explained how we train our model with normal dataset and augmented dataset and achieved accuracy greater than 95%