Paddy Crop Yield Prediction Using Sentinel-2 Satellite Imagery and AI

A model that predicts paddy crop yield from Sentinel-2 satellite imagery using AI.

A model that predicts paddy crop yield from Sentinel-2 satellite imagery using AI. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to work with images or video: building a dataset, training or fine-tuning a vision model, measuring its performance and wrapping it in a usable tool.

The main parts are dataset preparation and annotation, model training or fine-tuning, evaluation with standard metrics, plus visual examples, and an interface that works on uploaded images or live video. The reference build uses Rasterio, Python, OpenCV, PyTorch, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.