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All Journal Bulletin of Electrical Engineering and Informatics
Rudy Ivan Jamjachi Yauri
Universidad Tecnológica del Peru
Electrical & Electronics Engineering

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Sustainable greenhouse using IoT and machine learning to optimize the microclimate for lettuce cultivation Rudy Ivan Jamjachi Yauri; Jorge Raul Herbozo Ramirez; Christian Ovalle
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.9877

Abstract

Sustainable agriculture faces increasing challenges due to climate variability, which affects crop productivity and resource efficiency. This study proposes a sustainable greenhouse system that integrates internet of things (IoT) sensors and machine learning models to optimize the microclimate for lettuce cultivation. Environmental data, including temperature, humidity, and light intensity, were collected through IoT sensors and processed using machine learning algorithms, specifically neural networks and support vector machines (SVM), implemented on the Orange data mining platform. The results indicate that the neural network model achieved superior performance, reaching an accuracy of 99.99% in predicting optimal greenhouse climate conditions, outperforming the SVM model. The best-performing model was subsequently implemented on an Arduino-based IoT system to automatically regulate greenhouse conditions. The proposed system improved resource efficiency and supported optimal lettuce growth while promoting sustainable agricultural practices. These findings demonstrate that integrating IoT and machine learning can enhance greenhouse management, contributing to climate-resilient agriculture and improved food production systems.
Co-Authors Christian Ovalle Jorge Raul Herbozo Ramirez