• Journal article

Soil moisture forecasting from sensors-based soil moisture, weather and irrigation observations: A systematic review

Ivanova, I. 2025. Soil moisture forecasting from sensors-based soil moisture, weather and irrigation observations: A systematic review. Smart Agricultural Technology. 10 100692. https://doi.org/10.1016/j.atech.2024.100692

TitleSoil moisture forecasting from sensors-based soil moisture, weather and irrigation observations: A systematic review
TypeJournal article
AuthorsIvanova, I.
Abstract

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 require 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 several 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.

Article number100692
JournalSmart Agricultural Technology
Journal citation10
Year2025
PublisherElsevier
Publisher's version
License
CC BY-NC-ND 4.0
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.1016/j.atech.2024.100692
Web address (URL)https://cris.fbk.eu/handle/11582/358789
Publication dates
Published online06 Dec 2024
Published in printMar 2025

Related outputs

Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and Climbing
Ivanova, I. and Wald, M. 2023. Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and Climbing. Human-Centric Intelligent Systems . 3, pp. 344-365. https://doi.org/10.1007/s44230-023-00033-3

Introducing Context in Climbing Crags Recommender System in Arco, Italy
Ivanova, I. and Wald, M. 2023. Introducing Context in Climbing Crags Recommender System in Arco, Italy. IUI '23: 28th International Conference on Intelligent User Interfaces. Sydney, NSW, Australia 27 - 31 Mar 2023 ACM. https://doi.org/10.1145/3581754.3584120

How can we model climbers’ future visits from their past records?
Ivanova, I. and Wald, M. 2023. How can we model climbers’ future visits from their past records? 31st ACM Conference on User Modeling, Adaptation and Personalization. Limassol, Cyprus 26 - 29 Jun 2023 ACM. https://doi.org/10.1145/3563359.3597408

Climbing crags repetitive choices and recommendations
Ivanova, I. 2023. Climbing crags repetitive choices and recommendations. RecSys '23: Seventeenth ACM Conference on Recommender Systems. Singapore 18 - 22 Sep 2023 ACM. https://doi.org/10.1145/3604915.3610652

Climbing crags recommender system in Arco, Italy: a comparative study
Ivanova, I. and Wald, M. 2023. Climbing crags recommender system in Arco, Italy: a comparative study. Frontiers in Big Data. 6 1214029. https://doi.org/10.3389/fdata.2023.1214029

Map and Content-Based Climbing Recommender System
Ivanova, I., Buriro, A. and Ricci, F. 2022. Map and Content-Based Climbing Recommender System. UMAP '22: 30th ACM Conference on User Modeling, Adaptation and Personalization. Barcelona, Spain 04 - 07 Jul 2022 ACM. https://doi.org/10.1145/3511047.3536416

Content-Based Recommendations for Crags and Climbing Routes
Ivanova, I. 2022. Content-Based Recommendations for Crags and Climbing Routes. ENTER 2022 eTourism Conference, January 11-14, 2022. Nankai University, Tianjin, China 11 - 14 Jan 2022 Springer Nature. https://doi.org/10.1007/978-3-030-94751-4_33

Climbing Route Difficulty Grade Prediction and Explanation
Andric, M., Ivanova, I. and Ricci, F. 2021. Climbing Route Difficulty Grade Prediction and Explanation. WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. Melbourne, VIC, Australia 14 - 17 Dec 2021 ACM. https://doi.org/10.1145/3486622.3493932

Knowledge-Based Recommendations for Climbers
Ivanova, I., Andric, M. and Ricci, F. 2021. Knowledge-Based Recommendations for Climbers. 3rd Edition of Knowledge-aware and Conversational Recommender Systems (KaRS) and the 5th Edition of Recommendation in Complex Environments (ComplexRec) co-located with 15th ACM Conference on Recommender Systems (RecSys 2021). Virtual event, Amsterdam, Netherlands 25 Sep 2021 CEUR Workshop Proceedings.

Climber Behavior Modeling and Recommendation
Ivanova, I. 2021. Climber Behavior Modeling and Recommendation. 29th Conference on User Modeling, Adaptation and Personalization (ACM UMAP 2021). Online from Utrecht, the Netherlands. 21 - 25 Jun 2021 ACM. https://doi.org/10.1145/3450613.3459658

Video and Sensor-Based Rope Pulling Detection in Sport Climbing
Ivanova, I., Andrić, M., Moaveninejad, S., Janes, A. and Ricci, F. 2020. Video and Sensor-Based Rope Pulling Detection in Sport Climbing. MM '20: The 28th ACM International Conference on Multimedia. Seattle, WA, USA 16 Oct 2020 ACM. https://doi.org/10.1145/3422844.3423058

Climbing activity recognition and measurement with sensor data analysis
Ivanova, I., Andric, M., Janes, A., Ricci, F. and Zini, F. 2020. Climbing activity recognition and measurement with sensor data analysis. 2020 International Conference on Multimodal Interaction. Virtual Event, Netherlands 25 - 29 Oct 2020 ACM. https://doi.org/10.1145/3395035.3425303

Permalink - https://westminsterresearch.westminster.ac.uk/item/wz9y6/soil-moisture-forecasting-from-sensors-based-soil-moisture-weather-and-irrigation-observations-a-systematic-review


Share this

Tweet
Email

Usage statistics

3 total views
3 total downloads
These values cover views and downloads from WestminsterResearch and are for the period from September 2nd 2018, when this repository was created.