{"type":"ScholarlyArticle","title":"Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture","authors":[{"name":"Anoushka Vyas","slug":"anoushka-vyas","given":"Anoushka","family":"Vyas","affiliation":"Department of Computer Science , Virginia Tech , Arlington , VA , USA"},{"name":"Sambaran Bandyopadhyay","slug":"sambaran-bandyopadhyay","given":"Sambaran","family":"Bandyopadhyay","affiliation":"Amazon"}],"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.","doi":"10.24963/ijcai.2022/720","url":"https://ijcai.org/proceedings/2022/720","pdf":"https://ijcai.org/proceedings/2022/0720.pdf","markdown":"https://ijcai.org/proceedings/2022/720.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/720","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Research Papers","track":"AI for Good","number":720,"pages":{"first":5185,"last":5191},"keywords":["Multidisciplinary Topics and Applications: Sustainable Development Goals","Data Mining: Mining Spatial and/or Temporal Data","Data Mining: Mining Graphs","Multidisciplinary Topics and Applications: Computational Sustainability"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987131","length_sec":92,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985274","length_sec":714,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Ahmad et al., 2010] Sajjad Ahmad, Ajay Kalra, and Haroon Stephen. 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