Recent improvement and availability of remote satellite and IoT data offers interesting and diverse applications of artificial intelligence in precision agriculture. Soil moisture is an important component of multiple agricultural and food supply chain practices. It measures the amount of water stored in various depth of soil. Existing data driven approaches for soil moisture prediction use conventional models which fail to capture the dynamic dependency of soil moisture values in near-by locations over time. In this work, we propose to convert the problem of soil moisture prediction as a semi-supervised learning on temporal graphs. We propose a dynamic graph neural network which can use the dependency of related locations over a region to predict soil moisture. However, unlike social or information networks, graph structure is not explicitly given for soil moisture prediction. Hence, we incorporate the problem of graph structure learning in the framework of dynamic GNN. Our algorithm, referred as DGLR, provides an end-to-end learning which can predict soil moisture over multiple locations in a region over time and also update the graph structure in between. Our solution achieves state-of-the-art results on real-world soil moisture datasets compared to existing machine learning approaches.

本文提出了一种基于图神经网络的解决方案，学习时变图结构并在端到端框架中预测土壤湿度。该解决方案能够处理实践中常见的缺失地面真实土壤湿度的问题，并在真实世界的土壤湿度数据上展示了我们算法的优点。

面向精准农业的图神经网络动态结构学习预测土壤湿度