Key points are not available for this paper at this time.
Agriculture is one of the most essential industries since it provides food for the entire population worldwide. Maintaining limited water resources is a challenging problem in this field, as growing healthy vegetables and fruits requires consistent plants watering. To automatize this maintenance, software companies started developing solutions utilizing artificial intelligence tools to forecast soil moisture levels from past observations of soil humidity, weather and irrigation, measured by different sensors. This forecast is useful for irrigation decisions support and crop growth monitoring. Even though such solutions are widely developed, still, a transparent, unified methodology how forecasting models for irrigation management from sensors should be designed and evaluated is still missing. In this paper, we provide such methodology from analysis of state-of-the-art scientific articles presenting forecasting methods for soil moisture from sensor data. This review tackles seeveral research question of how to forecast future soil moisture level from sensor-based past observations of soil moisture, weather and irrigation information. Furthermore, we follow the standard Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedures for literature search analysis in computer science. As a result of literature search, we summarized 60 scientific articles presenting soil moisture forecast published from 2014 to 2024. In conclusion, we present the main challenges in forecasting soil moisture and suggest how they can be addressed. • Soil moisture forecasting models from the past sensor-based observations. • Survey of the past ten years articles in the domain of forecasting. • Machine learning models applied in smart agriculture for soil moisture prediction for better irrigation.
Iustina Ivanova (Fri,) studied this question.