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基于支持向量机回归的营养液调控模型研究
引用本文:崔永杰,王明辉,张鑫宇,宁普才,崔功佩,王琦. 基于支持向量机回归的营养液调控模型研究[J]. 农业机械学报, 2021, 52(1): 312-323
作者姓名:崔永杰  王明辉  张鑫宇  宁普才  崔功佩  王琦
作者单位:西北农林科技大学机械与电子工程学院,陕西杨凌712100;农业农村部农业物联网重点实验室,陕西杨凌712100;西北农林科技大学机械与电子工程学院,陕西杨凌712100;西北农林科技大学机械与电子工程学院,陕西杨凌712100;陕西省农业信息感知与智能服务重点实验室,陕西杨凌712100;陕西旭田光电农业科技有限公司,陕西杨凌712100
基金项目:杨凌示范区产学研用协同创新重大项目(2018CXY-22)和陕西省重点研发计划项目(2019ZDLNY02-04)
摘    要:针对目前设施栽培中营养液动态调配精确度低的问题,提出一种基于支持向量机回归(Support vector machine regression,SVR)的营养液调控模型.首先,通过设计嵌套试验采集了13个温度、50组不同Knop营养液(A:99%Ca(N03)2·4H20、B:98%KN03、C:99%KH2P04、D...

关 键 词:营养液  调控模型  支持向量机回归  离散斜率  人工鱼群算法
收稿时间:2020-09-25

Regulation Model Research of Nutrient Solution Based on Support Vector Machine Regression
CUI Yongjie,WANG Minghui,ZHANG Xinyu,NING Pucai,CUI Gongpei,WANG Qi. Regulation Model Research of Nutrient Solution Based on Support Vector Machine Regression[J]. Transactions of the Chinese Society for Agricultural Machinery, 2021, 52(1): 312-323
Authors:CUI Yongjie  WANG Minghui  ZHANG Xinyu  NING Pucai  CUI Gongpei  WANG Qi
Affiliation:Northwest A&F University; Shaanxi Xutian Photoelectric Agricultural Technology Co., Ltd.
Abstract:Aiming to struggle with the problem of low precision of nutrient solution dynamic deployment in protected cultivation. Based on support vector machine regression(SVR), a model for regulating nutrient solution was established. Firstly, the pH value, EC, K+ concentration, Ca2+concentration and NO-3 concentration of nutrient solution were collected under 13 temperatures and 50 groups of Knop nutrient solution ratio (A:99%Ca(NO3)2·4H2O, B:98%KNO3, C:99%KH2PO4, D:98%MgSO4·7H2O, E:99%EDTA-NaFe), and SVR was used to construct the index value prediction model. Then, the discrete slope method was used to calculate the discrete slope of the content response curve for nutrient solution detection index value and five compounds, and artificial fish swarm algorithm was used to obtain the maximum mutation point of discrete slope. Finally, the optimal regulation model of nutrient solution was constructed based on SVR with the amount of five compounds corresponding to the largest mutation feature site as the optimal control target value. The determination coefficients of the five compounds in the nutrient solution regulation model were 0.99, 0.98, 0.99, 0.96 and 0.99;the root mean square errors were 4.29mg,7.39mg,5.02mg,2.85mg and 3.96mg. These results showed that the fitting effect was good. Compared with the control effect of stepwise regression method to obtain the target value, the average relative errors of the five compounds were reduced by 37.65%, 49.94%, 40.53%, 50.58% and 42.84%. In the validation test, compared with the stepwise regression method, the relative average errors of five compounds in the nutrient solution regulation model was reduced by 46.42%, 52.08%, 54.03%, 53.59% and 54.54%. The average reduction rates of the five compounds were 1.69%, 5.81%, 5.85%, 3.65% and 7.08%, respectively. The nutrient solution regulation model based on SVR had the characteristics of high efficiency and energy saving, which may provide a reference for the practical production and application of protected crop cultivation.
Keywords:nutrient solution  regulation model  support vector machine regression  discrete slope  artificial fish swarm algorithm
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