An Artificial Intelligence-Powered Environmental Control System for Resilient and Efficient Greenhouse Farming

Meng-Hsin Lee, Ming-Hwi Yao (), Pu-Yun Kow, Bo-Jein Kuo and Fi-John Chang ()
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Meng-Hsin Lee: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 106216, Taiwan
Ming-Hwi Yao: Agricultural Engineering Division, Taiwan Agricultural Research Institute, Ministry of Agriculture, Taichung City 413008, Taiwan
Pu-Yun Kow: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 106216, Taiwan
Bo-Jein Kuo: Department of Agronomy, National Chung Hsing University, Taichung City 402202, Taiwan
Fi-John Chang: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 106216, Taiwan

Sustainability, 2024, vol. 16, issue 24, 1-18

Abstract: The rise in extreme weather events due to climate change challenges the balance of supply and demand for high-quality agricultural products. In Taiwan, greenhouse cultivation, a key agricultural method, faces increasing summer temperatures and higher operational costs. This study presents the innovative AI-powered greenhouse environmental control system (AI-GECS), which integrates customized gridded weather forecasts, microclimate forecasts, crop physiological indicators, and automated greenhouse operations. This system utilizes a Multi-Model Super Ensemble (MMSE) forecasting framework to generate accurate hourly gridded weather forecasts. Building upon these forecasts, combined with real-time in-greenhouse meteorological data, the AI-GECS employs a hybrid deep learning model, CLSTM-CNN-BP, to project the greenhouse’s microclimate on an hourly basis. This predictive capability allows for the assessment of crop physiological indicators within the anticipated microclimate, thereby enabling preemptive adjustments to cooling systems to mitigate adverse conditions. All processes run on a cloud-based platform, automating operations for enhanced environmental control. The AI-GECS was tested in an experimental greenhouse at the Taiwan Agricultural Research Institute, showing strong alignment with greenhouse management needs. This system offers a resource-efficient, labor-saving solution, fusing microclimate forecasts with crop models to support sustainable agriculture. This study represents critical advancements in greenhouse automation, addressing the agricultural challenges of climate variability.

Keywords: greenhouse; artificial intelligence (AI); microclimate forecast; gridded weather forecast; environmental control; photosynthesis rate (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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