Analysis of Crop Disease Using Deep Learning based Convolutional Neural Network

Crop cultivation is crucial to the agriculture sector. To fulfill the demands of the nation's expanding population and to match the economic environment at the moment, crop output must be increased. The Convolutional Neural Network (CNN) a way of identifying agricultural diseases is presented in this research study. A major danger to agricultural production and food security is posed by crop diseases. Farmers can take prompt action to lessen the burden of diseases by having early and accurate identification of diseases. The suggested CNN-based method takes advantage of deep learning's abilities to automatically learn distinguishing features from cropped photos, facilitating accurate disease identification. The CNN model is good at reliably diagnosing a variety of crop diseases, according to experimental results, offering a useful tool for crop health monitoring and management. Therefore, productivity will increase, and the economy will benefit.