Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture
This work is significant for farmers and agricultural practitioners by providing a more scalable and accurate method for forecasting soil moisture, which can optimize irrigation and water usage.
This paper addresses the challenge of forecasting soil moisture, crucial for precision agriculture, by proposing a novel graph neural network (GNN) solution. The GNN learns temporal graph structures and forecasts soil moisture in an end-to-end framework, effectively handling missing ground truth data.
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.