CropSentinel: AI-Based Crop Health Monitoring and Early Warning System

CropSentinel, an AI-based crop health monitoring and early warning system.

CropSentinel, an AI-based crop health monitoring and early warning system. 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 hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are alerts when a threshold or risk is reached, data preparation and exploration, model training and comparison of algorithms, evaluation with standard metrics, and a simple interface or API to try predictions. The reference build uses Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.