A decision-making model for light environment control of tomato seedlings aiming at the knee point of light-response curves

文献类型： 外文期刊

第一作者： Gao, Pan

作者： Gao, Pan;Tian, Ziwei;Lu, Youqi;Lu, Miao;Zhang, Haihui;Hu, Jin;Gao, Pan;Lu, Miao;Hu, Jin;Gao, Pan;Lu, Youqi;Zhang, Haihui;Hu, Jin;Wu, Huarui

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关键词： Photosynthesis rate; U-chord curvature; Knee point; Decision-making model; Machine learning

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 198 卷

页码：

收录情况： SCI

摘要： Light, the energy source for crop photosynthesis, is a key factor for plant growth. The present study proposes a decision-making model of light environment control. The photosynthesis rate of tomato seedlings under different light intensities, temperatures, and CO2 concentrations was determined in a nested experiment. These data were used to construct a predictive model of the photosynthesis rate using the support vector regression method, with an R-2 of 0.9862, a root mean square error of 1.39 mu mol.m(-2).s(-1), and a mean absolute error of 1.18 mu mol.m(-2).s(-1). In total, 861 discrete light-response curves were obtained based on the predictive model, and their knee points were computed using the U-chord curvature method. These knee points were used to form a dataset for constructing a decision-making model for light environment control, with an R-2 of 0.984 and a root mean square error of 9.55 mu mol.m(-2).s(-1). The results of the validation experiment suggested that the average relative error of the model was 1.92%, indicating the robustness of the model. Compared with those of the light saturation control method, the average light demand for the decision-making model decreased by 60.49%, whereas the average photosynthesis rate reduced by 24.40%. Although the photosynthesis rate lost a bit, the rate of light saving is almost three times more than the rate of photosynthesis rate decreased slightly, which improved the production efficiency of tomato.

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