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Polyhouse Agriculture with AI: Strategies for Climate Control, Energy Efficiency and Yield Improvement

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Abstract

Artificial Intelligence is applied to an extensive array of climate change responses, from data collection and classification to conservation decision-making and rule enforcement, leading to improved understanding and mitigation efforts. Greenhouse and polyhouse farming practices could significantly add to the carbon footprint, posing a challenge to be addressed if sustainable and environmentally friendly solutions are to be assured. This study describes the use of Artificial Intelligence in polyhouse agriculture to achieve better yields, save energy and advocate for sustainable farming. This study further elucidates the impact of AI-driven climate control systems on the quality of crops, resource use efficiency and productivity. The findings demonstrated the enormous potential of AI technology to improve plant growth, lower greenhouse gas emissions and increase sustainability in polyhouse agriculture. AI-driven climate control systems optimize the growing environment and reduce energy use with reduced emissions of GHGs, thus contributing to sustainable farming. However, this study also emphasizes the need for detailed research into the deployment and consequences of AI technology within polyhouse operations. The key bottlenecks are availability, security and regulatory compliance related to the generation of data. Therefore, need for a holistic approach to the design phase of AI-driven climate control systems in polyhouses.

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The corresponding author can provide the datasets upon request.

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Acknowledgements

The research work was made possible with the support of BMS Institute of Technology & Management (Affiliated to VTU -Belgaum), Bengaluru, Karnataka, India which provided the necessary facilities.

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Correspondence to K. P. Mayuri.

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Mayuri, K.P., Kathavate, S. & Niranjanamurthy, M. Polyhouse Agriculture with AI: Strategies for Climate Control, Energy Efficiency and Yield Improvement. SN COMPUT. SCI. 5, 1119 (2024). https://doi.org/10.1007/s42979-024-03432-w

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