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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.00139 |
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| _version_ | 1866917300387446784 |
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| author | Tritsarolis, Andreas Bokan, Tomaž Brumen, Matej Mongus, Domen Theodoridis, Yannis |
| author_facet | Tritsarolis, Andreas Bokan, Tomaž Brumen, Matej Mongus, Domen Theodoridis, Yannis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_00139 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards Data-driven Nitrogen Estimation in Wheat Fields using Multispectral Images Tritsarolis, Andreas Bokan, Tomaž Brumen, Matej Mongus, Domen Theodoridis, Yannis Computer Vision and Pattern Recognition 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. |
| title | Towards Data-driven Nitrogen Estimation in Wheat Fields using Multispectral Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.00139 |