mohith-bacha/Rice-leaf-disease-detection

Detecting rice leaf diseases using Python involves collecting a labeled dataset of leaf images, preprocessing them through resizing, normalization, and augmentation, and training a convolutional neural network (CNN) to classify healthy and diseased leaves. # Rice Leaf Disease Detection Using CNN ## Objective The primary objective of this project is to detect diseases in rice leaves through image analysis utilizing Convolutional Neural Networks (CNN) with Keras and TensorFlow. ## Overview The "Rice Leaf Disease Detection Using CNN" project focuses on the accurate identification of diseases in rice leaves by leveraging CNN models built with Keras and TensorFlow. By analyzing images of rice leaves, this system aims to classify various leaf diseases. ### Project Highlights - 🌾 Detects rice leaf diseases through image analysis - 🚀 Utilizes CNN with Keras and TensorFlow for accurate detection - 📊 Implements OpenCV for image preprocessing ## Project Details The project workflow is divided into key stages: 1. **Data Loading**: Importing the necessary datasets for analysis. 2. **Dataset Preparation**: Preparing the dataset for model training. 3. **Label Mapping using JSON**: Mapping labels for classification using JSON. 4. **Data Preprocessing**: Preprocessing images using OpenCV. 5. **Model Building**: Implementing CNN models using Keras. 6. **Training**: Training the model for disease classification. ### Project Images ### Tags - Image Classification - CNN - TensorFlow - Keras - OpenCV