Towards Data-driven Nitrogen Estimation in Wheat Fields using Multispectral Images
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Abstract (arXiv)
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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References · 5
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2019Searching for MobileNetV3cited by 1,994
- 2018Attention Gated Networks: Learning to Leverage Salient Regions in Medical Imagescited by 272
- 2018Deep learning in agriculture: A surveycited by 160
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