The image data of rice leaf disease total 120jpg images with 3 classes [Brown spot, leaf smut, bacterial blight] and each class contain 40jpg images.

This dataset contains 120 jpg images of disease infected rice leaves. The images are grouped into 3 classes based on the type of disease. There are 40 images in each class.

The image data of rice leaf disease total 120jpg images with 3 classes [Brown spot, leaf smut, bacterial blight] and each class contain 40jpg images.

This project aims to detect diseases on the leaves of rice plants in Indonesia using the Convolutional Neural Network (CNN) Inception V3 method to design a classification model and produce a high level of accuracy.

The model is used to detect rice leaf diseases and analyze their percentage

This project implements a convolutional neural network (CNN) for detecting rice leaf diseases. Users can upload images, and the model provides real-time predictions of the disease class along with raw prediction scores. Built using TensorFlow and Streamlit, it aims to assist farmers and agricultural specialists in managing plant health.

The open source code for the paper "A Hybrid Multi-stage Model based on YOLO and Modified Inception Network for Rice Leaf Disease Analysis" Hybrid_Multi_Stage_DeepLearing_Approach