Exploring plant stress-reducing ventilation in greenhouses with plant sensors and decision tree analysis

Abstract

In spring and summer, tomato plants grown in greenhouses often experience high levels of (solar) irradiation in a dry atmosphere during the day. On such hot and sunny days, the resulting high transpiration rates greatly deplete the internal water storage pools (i.e., living cells) of the plant, which gives the plant higher daily stress and may result in irreversible plant or fruit damage. To facilitate the replenishment of internal water storage pools of a plant, greenhouse farmers in Belgium and the Netherlands employ a targeted ventilation strategy, which we have dubbed the ‘plant stress-reducing ventilation’ strategy. This is a commonly used, though scientifically largely understudied, technique in greenhouse cultivation. This makes the strategy difficult to master, leaving growers divided on its effectiveness. To better understand and quantify the effects of the stress-reducing ventilation strategy, we equipped tomato plants (Solanum lycopersicum L.) in a commercial Belgian greenhouse with sap flow and stem diameter variation sensors to continuously measure the plant response to the technique. Climate and greenhouse control data were recorded by the climate computer. This plant response was classified and used to generate a decision tree using machine learning, pointing out the most important factors that reduced plant stress when applying the technique. Our approach is novel in the sense that it incorporates plant sensor measurements into a decision tree algorithm for climate control. This integration has proven crucial in comprehending the practical application of the plant stress-reducing ventilation strategy, now better understood from an ecophysiological perspective.

Plant phenotyping relevance

植物センサーでストレス応答を連続測定し、機械学習による意思決定木で解析する手法統合が研究の中心であり、単なる生理測定ではない。

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