Multi-objective optimal regulation model and system based on whole plant photosynthesis and light use efficiency of lettuce

Abstract

Lettuce growth and light energy consumption in a plant factory with artificial lighting (PFAL) were studied, and whole plant photosynthetic rate (ACO₂) and light use efficiency (LUE) data were obtained on different days after planting (DAP) under different photosynthetic photon flux densities (PPFDs). Genetic algorithm‐support vector regression (GA-SVR) was used to construct the ACO₂ and LUE prediction models. The coefficient of determination (R²) between the predicted and measured values of the ACO₂ model was 0.97 and the root mean square error (RMSE) was 0.42 μmol·mol⁻¹·plant⁻¹·min⁻¹, and R² between the predicted and measured values of the LUE model was 0.97 and RMSE was 0.36%. The ACO₂ and LUE prediction models were used as the objective functions, and multi-objective search was performed by the non-dominated sorting genetic algorithm II (NSGA-II) and the distance-based knee point detection method were used to obtained the optimal equilibrium solution for different DAPs. The optimal equilibrium solutions were used as the basis to establish the light regulation model based on lettuce DAP with R² of 0.99. To validate the effect of model regulation, a lettuce light regulation system was built using an artificial climate chamber for a 30-day system validation. The results showed that compared with the traditional quantitative light supplementation method, the dry matter of model regulation significantly increased by 39.23% and 29.48% compared with quantitative PPFD150 (μmol·m⁻²·s⁻¹) and PPFD200 (μmol·m⁻²·s⁻¹). Model regulation increased the number of total light quanta consumed by 1.39% over PPFD150 and decreased by 23.96% over PPFD200; however, plant productivity increased by 35.35% and 33.14%, respectively. Model regulation significantly reduced the number of light quanta consumed per unit mass of lettuce production by 24.35% for PPFD150 and 41.54% for PPFD200, and LUE of light-emitting diode energy into dry matter was significantly increased by 33.49% and 75.09%. Therefore, the light regulation model based on multi-objective optimization in this study could improve crop yield and increase LUE.

Plant phenotyping relevance

レタスの全植物光合成速度と光利用効率という生理形質を予測するGA-SVRモデルを開発し、光制御システムとして検証しており、形質取得・推定手法が研究の中心である。

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