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Method for optimizing controlled conditions of plant growth using U-chord curvature

文献类型： 外文期刊

作者： Gao, Pan 1 ; Li, Bin 1 ; Bai, Jinghua 1 ; Lu, Miao 1 ; Feng, Pan 1 ; Wu, Huarui 4 ; Hu, Jin 1 ;

作者机构： 1.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Agr Internet Things, Yangling 712100, Shaanxi, Peoples R China

3.Shaanxi Key Lab Agr Informat Percept & Intelligen, Yangling 712100, Shaanxi, Peoples R China

4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Photosynthetic rate prediction model; Environmental regulation; U-chord curvature; Machine learning

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2021 年 185 卷

页码：

收录情况： SCI

摘要： Temperature, light and CO2 are three environmental factors that directly affect the photosynthetic rate of plants. Exploring the relationship between these three factors and the photosynthetic rate, and optimizing the environmental conditions, is the key to realize efficient production of greenhouse crops. A nested experiment was carried out to measure the photosynthetic rate of cucumber seedlings under different environmental conditions. On the basis of these data, a photosynthetic rate prediction model was established by using machine learning method. A method that avoiding the excessive consumption of light and CO2 resources and promoting the photosynthetic rate based on U-chord curvature is proposed for more efficient regulation of photosynthetic rate. Under different temperature conditions, the prediction model can be used to construct photosynthetic rate surfaces under the interaction of light and CO2. After the surface is discretized, the maximum U-chord curvature points of discrete light response and CO2 response curves are calculated. These points are fitted by polynomials as the regulation boundary to obtain the target space that can achieve efficient regulation. The prediction model, with temperature, light and CO2 as inputs, had high accuracy, and the regulation method was effective. The determination coefficient and root mean square error of the prediction model were 0.99 and 0.85 mu mol.m 2.s 1, respectively. Compared with the traditional maximum photosynthetic rate regulation method, this new method reduced the photosynthetic rate by under 16%, but saved 41% of light and 49% of CO2 inputs, which illustrated this method improved production efficiency while basically maintained maximum photosynthetic rate.

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