Machine Learning Models For Estimating Water Demand In Tropical Crops: A Review And Iot-Based Conceptual Architecture
DOI:
https://doi.org/10.5281/zenodo.21145186Keywords:
Predictive algorithms, distributed IoT ecosystems, evapotranspiration modeling, automated water management, architectural models, telemetry in the tropicsAbstract
Agricultural irrigation management must be efficient given the growing pressure on water resources across tropical regions, and the framework of solutions must naturally include optimal approaches supported by cutting-edge technology. The objective of this study is to analyze the most important Machine Learning models for calculating irrigation water demand in tropical crops and thereby combine the findings with a conceptual architectural model proposal using Internet of Things technologies. The methodology employed is based on a qualitative approach with a bibliographic and documentary design, supported by a systematic review of scientific literature published between 2018 and 2024, carried out in databases such as Scopus, Google Scholar and Web of Science. The results reveal that algorithms such as Random Forest, LSTM, SVM and XGBoost achieve coefficients of determination (R² above 0.87) when forecasting key moisture-related variables, including not only evapotranspiration and soil humidity, but proving particularly effective when combined with real-time data from IoT sensors.
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Copyright (c) 2026 Steven Emiliano Cedeño Meza, Rocío Alexandra Mendoza Villamar, Cesar Augusto Sinchiguano Chiriboga, Michael Enrique Santos Molina (Autor/a)
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