This repository contains the code for a machine learning model developed to classify rice leaf diseases. The model has been trained on a dataset obtained from Kaggle, consisting of 2,627 images representing six different rice leaf diseases, along with a category for healthy rice leaves. The trained model can be used to classify new rice leaf images and identify the presence of diseases.
The rice leaf disease dataset includes the following diseases and categories:
- Bacterial Leaf Blight
- Brown Spot
- Healthy
- Leaf Blast
- Leaf Scald
- Narrow Brown Spot
The dataset contains images in JPEG format (.jpg) and has been used to train and evaluate the machine learning model.
Dataset Source: Rice Leaf Disease Dataset on Kaggle
The machine learning model used in this repository is based on Convolutional Neural Networks (CNN), a powerful architecture for image classification tasks. The specific implementation details of the CNN model can be found in the code provided.
If you use this model or the dataset in your research work or any other projects, please consider citing the original dataset source or the repository where you obtained the code.
Please refer to the license information provided within the repository for details regarding the usage and distribution of the code and model.
We would like to acknowledge the efforts of the individuals or organizations responsible for collecting and curating the rice leaf disease dataset. Their contributions enable researchers and enthusiasts to develop models and algorithms for rice plant disease detection and classification.
If you have any questions or issues regarding the code or model, please reach out to the repository owner for assistance.