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arXiv · 2603.00139

Towards Data-driven Nitrogen Estimation in Wheat Fields using Multispectral Images

Andreas Tritsarolis·Tomaž Bokan·Matej Brumen·Domen Mongus·Yannis Theodoridis
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Abstract

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.

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Andreas Tritsarolis, Tomaž Bokan, Matej Brumen, Domen Mongus, Yannis Theodoridis. 2026-02-24. Towards Data-driven Nitrogen Estimation in Wheat Fields using Multispectral Images. https://arxiv.org/abs/2603.00139

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