论文标题
通过图神经网络学习的动态结构学习，以预测精确农业的土壤水分
Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture
论文作者
论文摘要
土壤水分是精确农业的重要组成部分，因为它直接影响了植被的生长和质量。预测土壤水分对于安排灌溉和优化水的使用至关重要。基于物理的土壤水分模型需要丰富的特征和不可扩展的大量计算。在最近的文献中，常规机器学习模型已用于此问题。这些模型快速而简单，但是它们通常无法捕获土壤水分在一个地区表现出的时空相关性。在这项工作中，我们提出了一种基于图形神经网络的新型解决方案，该解决方案可以在端到端框架中学习时间图结构和预测土壤水分。我们的解决方案能够处理缺失地面真相水分的问题，这在实践中很常见。我们在现实世界的土壤水分数据上显示了我们的算法的优点。
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.
