The modernization of agriculture has motivated the development of advanced analytics and decision-support systems to improve resource utilization and reduce environmental impacts. Targeted Spraying and Fertilization (TSF) is a critical operation that enables farmers to apply inputs more precisely, optimizing resource use and promoting environmental sustainability. However, accurate TSF is a challenging problem, due to external factors such as crop type, fertilization phase, soil conditions, and weather dynamics. In this paper, we present TerrAI, a Neural Network-based solution for TSF, which considers the spatio-temporal variability across different parcels. Our experimental study over a real-world remote sensing dataset validates the soundness of TerrAI on data-driven agricultural practices.
@article{arxiv.2603.00139,
title = {Towards Data-driven Nitrogen Estimation in Wheat Fields using Multispectral Images},
author = {Andreas Tritsarolis and Tomaž Bokan and Matej Brumen and Domen Mongus and Yannis Theodoridis},
journal= {arXiv preprint arXiv:2603.00139},
year = {2026}
}