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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Meng-Hsin Lee & Ming-Hwi Yao & Pu-Yun Kow & Bo-Jein Kuo & Fi-John Chang, 2024. "An Artificial Intelligence-Powered Environmental Control System for Resilient and Efficient Greenhouse Farming," Sustainability, MDPI, vol. 16(24), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:24:p:10958-:d:1543409
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    References listed on IDEAS

    as
    1. Oladayo S. Ajani & Member Joy Usigbe & Esther Aboyeji & Daniel Dooyum Uyeh & Yushin Ha & Tusan Park & Rammohan Mallipeddi, 2023. "Greenhouse Micro-Climate Prediction Based on Fixed Sensor Placements: A Machine Learning Approach," Mathematics, MDPI, vol. 11(14), pages 1-14, July.
    2. Md Nafiul Islam & Md Zafar Iqbal & Mohammod Ali & Md Ashrafuzzaman Gulandaz & Md Shaha Nur Kabir & Seung-Ho Jang & Sun-Ok Chung, 2023. "Evaluation of a 0.7 kW Suspension-Type Dehumidifier Module in a Closed Chamber and in a Small Greenhouse," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    3. Taotao Xu & Lijian Yao & Lijun Xu & Qinhan Chen & Zidong Yang, 2023. "Image Segmentation of Cucumber Seedlings Based on Genetic Algorithm," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    Full references (including those not matched with items on IDEAS)

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