Explainable AI for Crop Disease Detection and Severity Estimation

An explainable AI system for crop disease detection and severity estimation.

An explainable AI system for crop disease detection and severity estimation. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to work with neural networks end to end: preparing datasets, designing and training a model, tuning it, evaluating it properly and explaining the outcome.

The main parts are explanations of each result (feature importance or visual evidence), dataset preparation and augmentation, model design and training, evaluation with standard metrics, plus error analysis, and a simple interface or API to try the model. The reference build uses SHAP, Python, PyTorch, TensorFlow, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.