ORIGINAL RESEARCH article
Front. Agron.
Sec. Field Water Management
This article is part of the Research TopicAdvancing Precision Irrigation: Innovative Technologies, AI-Driven Solutions, and Strategies for Widespread AdoptionView all articles
Integrating OPTRAM and Machine Learning with Multimodal EO Proxies for Optimized Irrigation Scheduling in Smallholder Systems: A Vhembe District Case Study
Provisionally accepted
- 1University of Debrecen, Debrecen, Hungary
- 2Pecsi Tudomanyegyetem Termeszettudomanyi Kar, Pécs, Hungary
- 3Magyar Agrar- es Elettudomanyi Egyetem, Gödöllő, Hungary
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Climate variability and recurrent droughts pose increasing irrigation challenges for smallholder maize farmers in southern Africa. This study developed a scalable Earth observation and artificial intelligence (EO–AI) framework combining satellite data, machine learning, and crop water modeling to estimate daily maize evapotranspiration actual crop evapotranspiration (ETc) in South Africa's Vhembe District. Five machine learning models were rigorously validated against benchmark ETc derived from the FAO-56 Penman–Monteith reference evapotranspiration (ET₀) method multiplied by locally calibrated crop coefficients (Kc). Random Forest and k-Nearest Neighbors models demonstrated superior performance, with R2 consistently exceeding 0.99, root mean square error (RMSE) below 0.06 mm/day, and normalized RMSE (NRMSE) less than 2%, outperforming support vector machine, MARS, and XGBoost models. The EO–AI framework effectively captured fine-scale spatial and temporal ETc variability, with daily actual maize ETc at 6.5 mm/day during peak crop sensitivity periods. An operational irrigation decision-support prototype translated these predictions into targeted field-level water-deficit alerts for farmers. This work highlights the value of EO–AI frameworks for delivering high-resolution, daily ETc mapping in fragmented, cloud-prone landscapes, enabling more precise and resilient irrigation strategies for smallholder systems.
Keywords: artificial intelligence, crop water modeling, Earth Observation, evapotranspiration, irrigation, machine learning, Smallholder maize farmers
Received: 01 Sep 2025; Accepted: 08 Dec 2025.
Copyright: © 2025 Nxumalo, Ramabulana, Dlamini, Angura and Nagy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tondani Sanah Ramabulana
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