A

agronomy

YieldStack is an open-source agricultural intelligence platform that helps farmers and researchers optimize water usage, nitrogen application, and crop performance through modular AI systems, real-time sensor data, and agronomic modeling. Built under the AGPL-3.0+ license, it emphasizes transparency, explainability, and interoperability, enabling sustainable farming decisions that reduce waste, improve yields, and support long-term soil health.

This repository contains all Python scripts and notebooks used to generate the figures presented in my PhD thesis manuscript. The code is organized by thesis chapter, allowing for reproducible analyses and clear tracking of the computational workflow. This project serves as a reference for the methods implemented and the data visualizations included in the thesis.