Elena (2026) Predicció avançada de la humitat del terreny en camps valencians d'Agricultura de Precisió amb optimització per a IoT

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Hispana
• Year: 2026
• Date: 2026-07-03
• Authors: Elsa Guerra Elena
• DOI: None

Research Groups

• University of Valencia (Departamento de Ingeniería Agrícola)
• Institute for Sustainable Agriculture (IAS)

Short Summary

This study develops an advanced system for soil moisture prediction in agricultural fields within the Valencian Community, integrating in-situ sensors and machine learning models optimized for IoT devices. The results establish a replicable procedure for forecasting soil moisture at least 24 hours in advance during drainage periods in irrigated fields.

Objective

• Investigate the feasibility of using artificial intelligence to predict soil moisture levels in irrigated agricultural fields within the Valencian Community.

Study Configuration

• Spatial Scale: Local scale, focusing on representative citrus crop plots within the Valencian Community.
• Temporal Scale: Real-time and short-term forecasting (up to 24 hours) during drainage periods.

Methodology and Data

• Models used: Machine Learning (ML), Recurrent Neural Networks (RNN)
• Data sources: In-situ sensors, IoT nodes collecting time-series data of soil moisture at various depths, agro-climatic variables (temperature, precipitation, radiation, and rainfall)

Main Results

• The study demonstrates the viability of integrating efficient predictive models into agricultural IoT infrastructures for forecasting soil moisture levels.
• A replicable procedure is established for predicting soil moisture at least 24 hours in advance during drainage periods in irrigated fields.

Contributions

• This study contributes to more sustainable water management in Valencian agriculture by providing a reliable and efficient method for predicting soil moisture levels.
• The research highlights the potential of integrating machine learning models with IoT devices for optimizing irrigation systems and reducing water waste.

Funding

• Project reference: RTI2018-102066-B-I00 (Ministerio de Ciencia, Innovación y Universidades)
• Program: Retos-Colaboración 2018

Citation

@article{Elena2026Predicció,
  author = {Elena, Elsa Guerra},
  title = {Predicció avançada de la humitat del terreny en camps valencians d'Agricultura de Precisió amb optimització per a IoT},
  journal = {Hispana},
  year = {2026},
  url = {https://openalex.org/W7206488099}
}