Irrigation decision model for tomato seedlings based on optimal photosynthetic rate

文献类型： 外文期刊

第一作者： Wan, Xiangbei

作者： Wan, Xiangbei;Li, Bin;Chen, Danyan;Long, Xingyue;Deng, Yifei;Hu, Jin;Wan, Xiangbei;Li, Bin;Chen, Danyan;Hu, Jin;Wu, Huarui

作者机构：

关键词： irrigation; decision model; soil moisture; tomato; photosynthetic rate; machine learning

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.032； 五年影响因子：2.137 ）

ISSN： 1934-6344

年卷期： 2021 年 14 卷 5 期

页码：

收录情况： SCI

摘要： Soil moisture is a major environmental factor that influences tomato growth and development. Suitable soil moisture not only increases tomato production but also saves irrigation water. This paper developed an irrigation decision model, called soil moisture regulation model, for optimizing growth of tomato seedlings while saving water. The data used to establish models were collected from a multi-gradient nested experiment, in which temperature, photosynthetic photon flux density (PPFD), carbon dioxide (CO2) concentration and soil moisture were variables and the corresponding photosynthetic rate was measured. Subsequently, a prediction model of tomato photosynthetic rate was constructed using support vector regression (SVR) algorithm. With photosynthetic rate prediction model as fitness function, genetic algorithm (GA) was used to find the optimal soil moisture under each combination of the above environmental factors. Finally, back propagation neural network (BPNN) algorithm was used to establish a decision model of tomato irrigation, which could provide the optimal soil moisture under current environment. For the soil moisture regulation model constructed here, the coefficient of determination was 0.9738, the mean square error of the test set was 1.51x10(-5), the slope of the verified straight line was 0.9752, and the intercept was 0.00916. This model demonstrated high precision, which thereby provides a theoretical basis for accurate irrigation control in the greenhouse facility environment.

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