Chlorophyll fluorescence-based control of greenhouse supplemental lighting improves energy use efficiency in lettuce.

Abstract

Plant-driven lighting control has been proposed as a strategy to regulate supplemental light-emitting diode (LED) intensity according to real-time plant physiological status. This study developed a multiple linear regression (MLR) model to predict quantum yield of photosystem II (Φ PSII ) from environmental variables and evaluated its integration into a chlorophyll fluorescence-based biofeedback light control. The model incorporated light intensity, CO 2 concentration, air temperature, vapor pressure deficit, short-term light history, and diurnal effects. In a greenhouse validation experiment, supplemental lighting was regulated using either direct chlorophyll fluorometer measurements of Φ PSII (sensor-based control) or Φ PSII values predicted by the machine learning model (ML-based control), and compared with a constant photosynthetic photon flux density (PPFD) treatment. Both sensor- and ML-based control stabilized photochemical activity across the photoperiod relative to constant PPFD. Although plant growth did not differ among treatments, sensor-based ETR control achieved the highest energy use efficiency for LED lighting in this study. These findings demonstrate the feasibility of integrating predictive ML models into plant-based lighting control systems and indicate that sensor-based biofeedback control improved the energy-use efficiency of greenhouse supplemental lighting without compromising crop growth.

Plant phenotyping relevance

植物の光合成生理状態（ΦPSII）を予測・計測するモデルを開発し、蛍光センサーによるフィードバック照明制御へ統合して検証しており、植物フェノタイピング手法が中心です。

Code and data availability

The paper's raw phenotyping data (environmental variables, chlorophyll fluorescence measurements, and the MLR model dataset) are not publicly deposited; the authors state the raw data will be made available upon request. No public code, model checkpoints, or data URLs are provided.

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