| 摘要: |
| 为探索基于常规监测数据的神经网络预警模型在农产品传统风险管理中的应用,以2011—2012年我国5省市的蔬菜中农药残留监测数据为样本,采取神经网络方法建立风险预警模型。首先,以产品种类、监测环节、监测时间和蔬菜产地为参考采用专家打分法将样本进行安全性评级,然后将经过筛选和预处理的45种农药监测数据,作为BP神经网络输入层,并根据不安全蔬菜的风险程度,以非常安全(A)、比较安全(B)、基本安全(C)、较不安全(D)和不安全(E)5个等级作为输出层,农药残留数据经过处理整合后得到16个样本,通过对其中14个样本进行拟合训练,得到预警模型及2个验证样本的评分结果分别为2.343 0和3.171 5,与实际评分结果隶属同一安全等级。证明基于客观监测数据的神经网络预警模型对于蔬菜中农药残留的预警是有效的。 |
| 关键词: BP神经网络 预警模型 蔬菜农药残留 监测数据 |
| DOI：10.11841/j.issn.1007-4333.2015.02.033 |
| 投稿时间：2014-07-09 |
| 基金项目:中国农业科学院基本科研业务费项目支持(2012ZL016) |
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| Research on the early-warning model of vegetable pesticide residues based on a neural network |
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ZHANG Xing-lian1, ZHANG Hui-yuan2, TANG Xiao-chun2, QIAN Yong-zhong1, LI Xiao-man2
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| (1.Institute of Quality Standards & Testing Technology for Agro-Products, Chinese Academy of Agricultural Sciences/Key Laboratory of Agro-food Safety and Quality, Ministry of Agriculture, Beijing 100081, China;2.School of Agricultural Economics and Rural Development, Renmin University of China, Beijing 100872, China) |
| Abstract: |
| In order to explore the application of ANN early warning model, based on routine monitoring data in the traditional risk management of agricultural products, the pesticide residues in vegetable monitoring data from China's five provinces from 2011 to 2012 were taken as samples and the ANN risk early warning model was established.First, the experts rated the safety of samples, taking product variety, monitoring link and monitoring time and vegetable origin for reference.Then 45 kinds of pesticides filtered and monitoring data preprocessed were taken as the BP neural network input layer.And the five degrees of very safe(A), relatively safe(B), basic safe(C), relatively unsafe(D) and unsafe(E) were set as the output layer.Choosing 14 samples as training samples of the ANN warning model, the results of 2 validation samples were 2.343 0 and 3.171 5 respectively.The model results and actual results belong to the same safety level.It proved that the ANN early warning model based on the objective monitoring data is valid for the pesticide residue risk in vegetables. |
| Key words: BP neural network early-warning model pesticide residues of vegetables monitoring data |
