Work

Machine Learning-Based Soil Moisture Prediction Using Meteorological Data for Enhanced Irrigation Management

Year: 2026

Type: article

Abstract: Irrigation management is critical in smart farming, particularly in Algeria, where palm dates are the principal crop produced and exported. The accuracy of soil moisture metrics is essential for effective irrigation. While sensors are frequently used to get accurate measurements, their dependability can be jeopardised by faults or absence owing to non-installation. This research introduces a machine learning-based prediction approach for estimating soil moisture levels using weather forecasting data. This method is used as an alternative to sensors to provide constant and optimal irrigation techniques in palm date agriculture.

Source: Springer Link (Chiba Institute of Technology)

DOI: 10.1051/e3sconf/202669903005/pdf

Authors: Taki-Eddine Abbassi, Abdelhakim Sahour, Moncef Benkherrat, Saddam Brai, Islam Boutiouta, Farouk Boumehrez

Institutions: Abbès Laghrour University of Khenchela, ECAM-EPMI, Badji Mokhtar-Annaba University, University of Guelma

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: green

OpenAlex ID: W7140087943

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