Implementation of an Efficient Low Weighted Network Development in Paddy Disease Detection Prediction, Remedy Guider using Live Mobile Application

Abstract

The agriculture industry is the most important industry for society as it serves the most important need of life. But the plant diseases in agriculture lead to a decrease in productivity and hence it is very important to prevent, detect, and get rid of the diseases. Image processing and deep learning are nowadays the buzzwords in the IT industry and their applications in the agriculture industry can enhance decision making in various aspects of the agriculture industry. Paddy crop is one of the most demanding crops especially in South Asia. This paper proposes a predictive model using CNN for classification and prediction of disease in paddy crop. Paddy crop diseases are very fatal and can affect the crops severely if it is not taken care in the initial stages. The proposed model will improve the decision making using CNN in case of various diseases in paddy crop for prediction of diseases in initial stages and prevention of mass loss in productivity of the whole yield.