用数字孪生技术解决农业气象数据不准问题，提升决策可靠性
Weather-Driven Agricultural Decision-Making Using Digital Twins Under Imperfect Conditions
- 构建可检测气象数据异常的模块化框架Cerealia
- 在真实果园和公开数据集上验证了异常检测有效性
- 适合农业自动化与智慧农场开发者使用
数字孪生技术通过提供物理系统的动态实时虚拟映射，可提升数字农业中的数据驱动决策能力。本研究展示数字孪生在识别农业气象数据测量不一致方面的价值，而这些不一致是各类农业决策与自动化任务的关键挑战。我们提出模块化框架Cerealia，当缺乏理想气象数据时，帮助终端用户检测数据异常。Cerealia采用神经网络模型进行异常检测，支持用户做出更明智的决策。我们基于NVIDIA Jetson Orin平台开发了原型，并在商业化果园的实际气象网络及公开气象数据集上进行了测试。
原文摘要 · Abstract (English)
By offering a dynamic, real-time virtual representation of physical systems, digital twin technology can enhance data-driven decision-making in digital agriculture. Our research shows how digital twins are useful for detecting inconsistencies in agricultural weather data measurements, which are key attributes for various agricultural decision-making and automation tasks. We develop a modular framework named Cerealia that allows end-users to check for data inconsistencies when perfect weather feeds are unavailable. Cerealia uses neural network models to check anomalies and aids end-users in informed decision-making. We develop a prototype of Cerealia using the NVIDIA Jetson Orin platform and test it with an operational weather network established in a commercial orchard as well as publicly available weather datasets.
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