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Analysis of Irrigation Performance of a Solid-Set Sprinkler Irrigation System at Different Experimental Conditions
 
 
Article

Optimal Sprinkler Application Rate of Water–Fertilizer Integration Machines Based on Radial Basis Function Neural Network

by 1,2,3, 1,2,3, 1,2,3,* and 1,3
1
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
2
Guangdong Engineering Research Centre for High Efficient Utility of Water, Fertilizers and Solar Intelligent Irrigation, Guangzhou University, Guangzhou 510006, China
3
Advanced Institute of Engineering Science for Intelligent Manufacturing, Guangzhou University, Guangzhou 510006, China
*
Author to whom correspondence should be addressed.
Academic Editors: Haijun Yan and Xingye Zhu
Water 2022, 14(18), 2838; https://doi.org/10.3390/w14182838
Received: 20 August 2022 / Revised: 6 September 2022 / Accepted: 7 September 2022 / Published: 12 September 2022
(This article belongs to the Special Issue Advances in Sprinkler Irrigation Systems and Water Saving)
The application rate for sprinkler irrigation of water–fertilizer integration machines is an important technical parameter for efficient operation. If the value is too large, the equipment will produce runoff; if it is too small, the equipment will run too long and waste energy. Therefore, it is necessary to provide a feasible scientific and theoretical basis for developing a reasonable application rate. In this study, a mathematical model of soil infiltration for sprinkler irrigation with water and fertilizer integration machines was developed. Soil water accumulation time for different soil’s initial water content, bulk density, sprinkler application rate and soil texture were derived by the finite element method, and these data were used as a training database for the neural network. To make the neural network convenient for predicting the optimal application rate of sprinkler irrigation (the maximum application rate of sprinkler irrigation without runoff) in practice, the time of waterlogging, was multiplied by the optimal application rate of sprinkler irrigation to obtain the total irrigation volume. The optimal application rate of the sprinkler irrigation prediction model of radial basis function (RBF) neural network was constructed with total irrigation water, soil bulk density, initial water content and soil texture as inputs and compared with BP neural network and generalized regression neural network. The highest prediction accuracy of RBF neural network was obtained, and its average relative error was 0.11. To verify the accuracy of the RBF neural network application rate of sprinkler irrigation prediction model in real life, a sprinkler experiment was conducted in the laboratory of Guangzhou University, and the collected soil and lawn of Guangzhou University were used to simulate the actual environment. The results showed that the relative error between the RBF neural network prediction results and the actual values was generally around 10%, while for a total irrigation volume of 58 mm, the optimal application rate of sprinkler irrigation calculated with the model was 42 (mm/h), which can save 70% of irrigation time compared to the case of using the stable infiltration rate of soil as the application rate of sprinkler irrigation without water and fertilizer. Water and fertilizer losses were not observed. This indicates that the model proposed in this study is of practical value in determining the optimum application rate of sprinkler irrigation for water–fertilizer integration machines. View Full-Text
Keywords: optimal sprinkler application rate; water–fertilizer integration; RBF neural network; soil irrigation optimal sprinkler application rate; water–fertilizer integration; RBF neural network; soil irrigation
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MDPI and ACS Style

Liu, X.; Zhu, X.; Liang, Z.; Zou, T. Optimal Sprinkler Application Rate of Water–Fertilizer Integration Machines Based on Radial Basis Function Neural Network. Water 2022, 14, 2838. https://doi.org/10.3390/w14182838

AMA Style

Liu X, Zhu X, Liang Z, Zou T. Optimal Sprinkler Application Rate of Water–Fertilizer Integration Machines Based on Radial Basis Function Neural Network. Water. 2022; 14(18):2838. https://doi.org/10.3390/w14182838

Chicago/Turabian Style

Liu, Xiaochu, Xiangjin Zhu, Zhongwei Liang, and Tao Zou. 2022. "Optimal Sprinkler Application Rate of Water–Fertilizer Integration Machines Based on Radial Basis Function Neural Network" Water 14, no. 18: 2838. https://doi.org/10.3390/w14182838

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