Crop Disease Prediction Using Web Application

Abstract

Abstract: Agriculture plays a very vital role in our life. Without agriculture, the existence of human beings is not possible as it is the main source of our food supply to sustain on the earth and it also helps to grow our economy across the world. Plant disease detection is one of the most important aspects of maintaining an agriculturally developed nation. The timely and efficient detection of plant diseases is essential for a healthy and productive agricultural sector. Various diseases like Common Rust, Bacterial Spot, Leaf Mold, Mosaic Virus, Powdery Mildew and others that could affect a plant and cause farmer to lose a substantial sum yearly. Deep Learning (DL) can play a crucial role in helping farmers to prevent crop failure by early disease detection in plant leaves. In the experiment, examination conducted by Convolution Neural Network (CNN) model on dataset (which consists of images of healthy and infected leaves) to detect plant diseases and Web Application is used for real-life crop disease prediction. The proposed Web Application aims to assist farmers in detection of plant diseases by analyzing images of the plant leaves. The proposed application uses the CNN model to distinguish healthy and infected leaves. The goal is to help farmers and prevent economic loss by detecting plant diseases early