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

arXiv: https://arxiv.org/abs/2012.03506 · HTML: https://mlsanity.com/paper/2012.03506/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management](https://mlsanity.com/paper/2212.06565/) (2022)
- [Time-Series Foundation Models for Forecasting Soil Moisture Levels in Smart Agriculture](https://mlsanity.com/paper/2405.18913/) (2024)
- [A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks](https://mlsanity.com/paper/2410.21006/) (2024)
- [Spatio-Temporal Graph Neural Networks for Dairy Farm Sustainability Forecasting and Counterfactual Policy Analysis](https://mlsanity.com/paper/2512.19970/) (2025)

## Abstract (arXiv)

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.