Crop Disease Prediction using Deep Convolution Neural Network

Abstract

Harvest ailment has been a significant danger to ranchers thereby decreasing the harvest yield and bargaining its quality. One of the huge difficulties is a precise determination of illnesses. Generally, recognizable proof of yield infections has depended on human explanation by visual review. These days different current advancements have developed to limit post reap preparing, to strengthen horticultural manageability and to boost the profitability. In this work utilizes Deep Convolution Neural Network in distinguishing among sound and unhealthy leaf from the informational collections made. Our proposed paper incorporates different periods of execution specifically dataset obtaining, include extraction, preparing the classifier and grouping. The gathered datasets of both unhealthy and solid leaves are prepared under CNN to arrange the sick and sound pictures. For removing highlights of a picture by utilizing GLCM. So as to assess the exhibition of CNN model is done through confusion matrix which shows 82.39% the exactness of the model by utilizing TP and TN tests.