Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation. Forecasting soil moisture is essential to schedule the irrigation and optimize the use of water. Physics based soil moisture models need rich features and heavy computation which is not scalable. In recent literature, conventional machine learning models have been applied for this problem. These models are fast and simple, but they often fail to capture the spatio-temporal correlation that soil moisture exhibits over a region. In this work, we propose a novel graph neural network based solution that learns temporal graph structures and forecast soil moisture in an end-to-end framework. Our solution is able to handle the problem of missing ground truth soil moisture which is common in practice. We show the merit of our algorithm on real-world soil moisture data.
翻译:土壤湿度是精密农业的重要组成部分,因为它直接影响到植被的增长和质量。预测土壤湿度对于排灌和优化水的使用至关重要。物理土壤湿度模型需要丰富的特征和无法伸缩的重量计算。在最近的文献中,常规机器学习模型已经应用于这一问题。这些模型既快速又简单,但往往未能捕捉到土壤湿度在某一区域展示的地表-时空相关性。在这项工作中,我们提出了一个基于新颖的图形神经网络解决方案,该解决方案将学习时间图结构,并在一个端到端的框架里预报土壤湿度。我们的解决办法能够解决在实际中常见的缺失的地面真实土壤湿度问题。我们在真实世界土壤湿度数据中展示了我们的算法的优点。