AI-Based Crop Stress Detection Using Satellite Imagery for Early Warning and Explainable Analysis

An AI-based crop stress detection system that uses satellite imagery for early warning and explainable analysis.

An AI-based crop stress detection system that uses satellite imagery for early warning and explainable analysis. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend 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 explanations of each result (feature importance or visual evidence), alerts when a threshold or risk is reached, dataset preparation and annotation, model training or fine-tuning, and evaluation with standard metrics, plus visual examples. The reference build uses SHAP, Rasterio, Python, OpenCV, and PyTorch, with a clear project structure, setup instructions and sample data so that it can be run and extended.