Artificial intelligence can regulate light and climate systems to reduce energy use in plant factories and support sustainable food production

Benjamin Decardi-Nelson, Fengqi You
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Abstract

Plant factories with artificial lighting (PFALs) can boost food production per unit area but require resources such as carbon dioxide and energy to maintain optimal plant growth conditions. Here we use computational modelling and artificial intelligence (AI) to examine plant–environment interactions across ten diverse global locations with distinct climates. AI reduces energy use by optimizing lighting and climate regulation systems, with energy use in PFALs ranging from 6.42 kWh kg−1 in cooler climates to 7.26 kWh kg−1 in warmer climates, compared to 9.5–10.5 kWh kg−1 in PFALs using existing, non-AI-based technology. Outdoor temperatures between 0 °C and 25 °C favour ventilation-related energy use reduction, with outdoor humidity showing no clear pattern or effect on energy use. Ventilation-related energy savings negatively impact other resource utilization such as carbon dioxide use. AI can substantially enhance energy savings in PFALs and support sustainable food production.

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人工智能可以调节光照和气候系统,减少植物工厂的能源消耗,支持可持续食品生产
人工照明植物工厂(PFALs)可以提高单位面积的粮食产量,但需要二氧化碳和能源等资源来维持植物的最佳生长条件。在这里,我们利用计算建模和人工智能(AI)研究了全球十个气候各异地区的植物与环境之间的相互作用。人工智能通过优化照明和气候调节系统减少了能源消耗,在气候较冷的地区,PFAL 的能耗为 6.42 千瓦时/千克-1,在气候较热的地区为 7.26 千瓦时/千克-1,而在使用现有非人工智能技术的 PFAL 中,能耗为 9.5-10.5 千瓦时/千克-1。室外温度在 0 °C 至 25 °C 之间有利于减少与通风相关的能源消耗,而室外湿度对能源消耗没有明显的模式或影响。与通风相关的能源节约会对其他资源的利用产生负面影响,如二氧化碳的使用。人工智能可以大大提高 PFALs 的节能效果,支持可持续粮食生产。
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