Paper Info
Title
Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture.
Abstract
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.
Year
DOI
Venue
2022
10.24963/ijcai.2022/720
International Joint Conference on Artificial Intelligence
Keywords
DocType
Citations 
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
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Anoushka Vyas100.68
Sambaran Bandyopadhyay2149.52