Rice Plant Disease Identification

A Convolutional Neural Network (CNN) model for identifying rice diseases is a powerful tool in agriculture. By analyzing images of rice plants, CNNs can automatically detect and classify diseases, aiding farmers in timely intervention to protect crop yield and quality. The process involves collecting a diverse dataset of rice plant images, preprocessing them, designing a CNN architecture, and training the model. After validation, the model can be deployed for real-world use, offering automated disease diagnosis to farmers. Continuous improvement is essential, as the model needs to adapt to new data and changing disease patterns for effective disease management.