用数字孪生整合传感器与模型，实现精准农业推荐
Precision Agriculture Revolution: Integrating Digital Twins and Advanced Crop Recommendation for Optimal Yield
- 构建数字孪生架构融合土壤、气象、地理数据
- 实时预测作物生长并优化水肥农药使用
- 适合智慧农业、智能农机研发人员参考
借助数字孪生结构，融合天气API、GPS模块、NPK（氮磷钾）土壤传感器及机器学习推荐模型，本研究旨在通过该概念推动农业4.0的变革。除了提供精确的作物生长预测外，结合土壤成分、气象动态和地理坐标等实时数据，支持作物推荐模型，并模拟预测场景，以实现更优的水资源与农药管理。
原文摘要 · Abstract (English)
With the help of a digital twin structure, Agriculture 4.0 technologies like weather APIs (Application programming interface), GPS (Global Positioning System) modules, and NPK (Nitrogen, Phosphorus and Potassium) soil sensors and machine learning recommendation models, we seek to revolutionize agricultural production through this concept. In addition to providing precise crop growth forecasts, the combination of real-time data on soil composition, meteorological dynamics, and geographic coordinates aims to support crop recommendation models and simulate predictive scenarios for improved water and pesticide management.
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。