MaizeFolioID

MaizeFolioID is a cutting-edge image classification model focused on detecting foliar diseases in maize leaves. This project uses a pre-trained model from ImageNet, fine-tuned to classify specific leaf conditions, aiding in early detection and prevention of crop losses.

The model was trained on a comprehensive dataset from Kaggle, designed to distinguish between various foliar diseases. It’s an invaluable tool for farmers and agronomists, providing an early warning system to safeguard maize crops.

As a contributor to this project, I played a pivotal role in adapting the ImageNet model to our specific requirements, ensuring its accuracy and reliability in identifying maize leaf diseases.

The project includes a detailed guide on getting started, prerequisites, dependencies, and how to use the model. Additionally, we have outlined our future roadmap, including the integration of a VisionTransformer model and expanding the dataset for further training.

Here is a snippet of Python code used in the model:

# Python code snippet here

For more information, explore the MaizeFolioID on GitHub or try the model on our Streamlit App. You can also find the model on the Huggingface Model Hub and Project Documentation