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An Efficient Regulation Model of Light Environment For Greenhouse Cucumber
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2021 ASABE Annual International Virtual Meeting 2100375.(doi:10.13031/aim.202100375)
Authors: Pan Gao, Jin Hu
Keywords: Photosynthetic rate; support vector regression; Predictive model; light environment.
Abstract. Light is the energy source of photosynthesis and plays an important role in development of crops. Greenhouse light environment includes light intensity (LI) and light quality (LQ). The photosynthetic rate (Pn) of plants is affected by the coupling of light environment and other environments. It is important to build a Pn prediction model of protected crops which integrates LI, LQ and other environmental factors. In this paper, cucumber was taken as experimental material, and a nested experiment was designed to measure the Pn under different temperature, CO2 concentration ([CO2]), LI and LQ. On the bases of these measured data, a predictive model of Pn was built by using support vector regression (SVR) algorithm. The coefficient of determination (DC) of training set is 0.9990, and the root mean square error (RMSE) is 0.0478 μmol·m-2·s-1. The results showed that the model is highly accurate after training. The validation results of predictive model showed that the fitting slope of measured Pn values and predicted Pn values was 1.0015, and the intercept was 0.0223, which indicated that the model could accurately predict the Pn of leaves under different environmental conditions.
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