AI-Enabled Predictive Control for Tropical Greenhouse Farming: Environmental Stability, Resource Efficiency, and Yield Improvements
Keywords:
Artificial Intelligence , Smart Greenhouse , Predictive Control, Resource Efficiency, Tropical AgricultureAbstract
Purpose: This study aims to evaluate the effectiveness of an AI-enabled predictive control system in enhancing environmental stability, resource efficiency, and crop productivity within tropical greenhouse farming.
Subjects and Methods: The research was conducted using a 4 × 6-meter greenhouse prototype integrating IoT sensors and machine learning algorithms, including Random Forest and Artificial Neural Networks. Lettuce (Lactuca sativa) served as the model crop. The AI-driven greenhouse was compared with a conventional manually operated system across a full cultivation cycle. Data collected included microclimate parameters, water and energy consumption, plant growth indicators, and final yield.
Results: The AI-enabled greenhouse maintained more consistent environmental conditions, keeping temperature, humidity, light intensity, and soil moisture within optimal ranges. Water use was reduced by approximately 38%, and energy consumption decreased by 13% compared to the conventional system. Plants grown under predictive control exhibited stronger vegetative growth, with notable increases in height, leaf number, and canopy size. Yield improved by nearly 30%, accompanied by higher marketable quality. Predictive models demonstrated strong accuracy, supporting reliable real-time decision-making.
Conclusions: The results confirm that AI-based predictive control substantially improves greenhouse performance in tropical environments, offering a sustainable and efficient solution for modern horticultural production.
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