CNN-Based Crop Disease Prediction for Enhanced Productivity

For the purpose of maintaining economic stability and providing food security, agriculture is absolutely necessary. Agricultural diseases, on the other hand, constitute a substantial obstacle, as they result in decreased yields and economic losses. This study aims to predict agricultural diseases by using Convolutional Neural Networks (CNN) and Deep Learning (DL) methods to identify them accurately and efficiently. For the purpose of disease detection, the system examines the visual patterns included in crop leaf images. We utilize an extensive collection of images that depict both healthy and damaged leaves for training purposes. This approach allows the model to accurately differentiate between the various diseases. The CNN model achieves outstanding classification accuracy while simultaneously optimizing feature extraction without the need for human involvement. Through the use of mobile or internet applications, it makes an effort to assist farmers and agriculture professionals by providing real-time disease detection. It is possible for the identification to be exact and at the appropriate time, which enables prompt action to be taken to reduce losses and promote sustainable agricultural practices.