用多光谱图像实现小麦田氮素精准估算，助力智能施肥
Towards Data-driven Nitrogen Estimation in Wheat Fields using Multispectral Images
- 基于神经网络建模农田时空变异特性
- 在真实遥感数据集上验证了估算精度
- 适合农业遥感与智慧种植研究者
现代农业的推进催生了先进的分析与决策支持系统，以提高资源利用效率并减少环境影响。精准喷洒与施肥（TSF）是关键环节，能实现投入品的精确施用，优化资源使用并促进环境可持续性。然而，受作物类型、施肥阶段、土壤条件和天气变化等外部因素影响，准确实施TSF仍具挑战。本文提出TerrAI，一种基于神经网络的TSF解决方案，充分考虑不同地块间的时空变异特性。通过在真实世界遥感数据集上的实验研究，验证了TerrAI在数据驱动农业实践中的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。