Irrigation decision model for tomato seedlings based on optimal photosynthetic rate

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

Abstract


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 1.51×10-5, 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.
Keywords: irrigation, decision model, soil moisture, tomato, photosynthetic rate, machine learning
DOI: 10.25165/j.ijabe.20211405.6148

Citation: Wan X B, Li B, Chen D Y, Long X Y, Deng Y F, Wu H R, et al. Irrigation decision model for tomato seedlings based on optimal photosynthetic rate. Int J Agric & Biol Eng, 2021; 14(5): 115–122.

Keywords


irrigation, decision model, soil moisture, tomato, photosynthetic rate, machine learning

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References


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