Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture

Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture

Anoushka Vyas, Sambaran Bandyopadhyay

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
AI for Good. Pages 5185-5191. https://doi.org/10.24963/ijcai.2022/720

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.
Keywords:
Multidisciplinary Topics and Applications: Computational Sustainability
Data Mining: Mining Graphs
Data Mining: Mining Spatial and/or Temporal Data
Multidisciplinary Topics and Applications: Sustainable Development Goals