IMPLEMENTING PLANT DISEASE DETECTION SYSTEM USING CONVOLUTIONAL NEURAL NETWORK (CNN)

Agriculture plays a crucial role in global food security, but in plant disease can be reduce crop yield and quality.Traditional method of detecting plant diseases depends on manual inspection, which can be slow. Process ofindustry that required a large amount of labor to produce its goods or services. And offer requires the expertiseof trained professional, In this project we propose a plant disease detection system using Convolutional NeuralNetworks (CNN). The system classifies plant diseases based on leaf images, providing a fast and accuratediagnosis. The model is trained on the Plant Village dataset, which contains images of various crops affected bydifferent diseases. The proposed CNN model consists of multiple convolutional and pooling layers to extractfeatures, followed by fully connected layers for classification. The system achieves high accuracy in identifyingplant diseases, making it a valuable tool for farmers and agricultural experts. Additionally, we trained themodel can be development on mobile applications, web platforms, or IoT devices, allowing real-time diseasedetection in the field. The study also explores transfer learning techniques to improve performance and reducetraining time. The proposed system helps in early disease detection, reducing crop losses, and promotingsustainable farming practices.