A Cucumber Photosynthetic Rate Prediction Model in Whole Growth Period with Time Parameters

Zichao Wei, Xiangbei Wan, Wenye Lei, Kaikai Yuan, Miao Lu, Bin Li, Pan Gao, Huarui Wu () and Jin Hu ()
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Zichao Wei: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Xiangbei Wan: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Wenye Lei: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Kaikai Yuan: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Miao Lu: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Bin Li: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Pan Gao: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Huarui Wu: National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Jin Hu: College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China

Agriculture, 2023, vol. 13, issue 1, 1-14

Abstract: Photosynthetic rate prediction models can provide guidance for crop photosynthetic process optimization, which has been widely used in the precise regulation of the protected environment. The photosynthetic capacity of crops continuously changes during their whole growth process. Previous studies on photosynthetic models mainly consider the interaction between a crop’s photosynthetic rate and its outer environmental conditions and have been able to predict a crop’s photosynthetic rate in a certain growth period. However, photosynthetic rate prediction models for whole growth periods have not been proposed yet. To solve this question, this paper introduces growing time into a variable set and proposes a method for building a cucumber photosynthetic rate prediction model of whole growth periods. First, the photosynthetic rate of cucumber leaves under different environmental conditions (light, temperature, and CO 2 concentration) during the whole growth period was obtained through a multi-gradient nested test. With the environmental data and the cultivation time as the inputs, a photosynthetic rate prediction model was built using the Support Vector Regression algorithm. In order to obtain better modeling results, multiple kernel functions were used for pretraining, and the parameters of the Support Vector Regression algorithm were optimized based on multiple population genetic algorithms. Compared with a Back Propagation neural network and Non-linear Regression method, the Support Vector Regression model optimized had the highest accuracy, with the coefficient of determination of the test set was 0.998, and the average absolute error was 0.280 μmol·m −2 ·s −1 , which provides a theoretical solution for the prediction of the cucumber photosynthetic rate during the whole growth period.

Keywords: cucumber; photosynthetic rate; SVR algorithm optimization; the whole growth period (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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