基于大数据的玉米田四代棉铃虫发生量的预测模型

赵雷; 杨波; 刘勇; 牟少敏; 温孚江

doi:10.11959/j.issn.2096-0271.2016008

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基于大数据的玉米田四代棉铃虫发生量的预测模型

专题：农业大数据 | 更新时间：2024-06-03

- 基于大数据的玉米田四代棉铃虫发生量的预测模型
- Forecasting model for the fourth generation of cotton bollworm in corn fields based on big data
- 大数据 2016年2卷第1期页码：2016008
- 作者机构：
  
  山东农业大学农业大数据研究中心，山东泰安 271018
- 作者简介：
  
  [ "赵雷（1990-），女，山东农业大学硕士生，主要研究方向为农业科研与大数据。" ]
  [ "刘勇（1968-），男，山东农业大学教授、博士生导师，主要研究方向为害虫绿色防控和农业大数据。" ]
  [ "牟少敏（1964-），男，博士，山东农业大学教授，主要研究方向为大数据、机器学习和模式识别。" ]
  [ "温孚江（1955-），男，现任山东农业大学校长、教授，农业大数据创新战略联盟理事长，全国人民代表大会常务委员会委员。早年留学美国，并获博士学位。主要从事植物保护研究和宏观农业研究工作。发表论文210余篇，专著5部。最近一部专著《大数据农业》由中国农业出版社于2015年9月出版。目前主要从事农业大数据应用研究工作，是我国农业大数据研究主要发起人之一。" ]
- 基金信息：
  
  山东省农业重大应用技术创新课题基金资助项目;Major Innovation of Applied Technology in Agriculture of Shandong Province
- DOI：10.11959/j.issn.2096-0271.2016008
  中图分类号： S431.9
- 网络首发：2016-01，
  
  纸质出版：2016-01-20
- 稿件说明：
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赵雷, 杨波, 刘勇, 等. 基于大数据的玉米田四代棉铃虫发生量的预测模型[J]. 大数据, 2016,2(1):2016008.

Lei ZHAO, Bo YANG, Yong LIU, et al. Forecasting model for the fourth generation of cotton bollworm in corn fields based on big data[J]. BIG DATA RESEARCH, 2016, 2(1): 2016008.
赵雷, 杨波, 刘勇, 等. 基于大数据的玉米田四代棉铃虫发生量的预测模型[J]. 大数据, 2016,2(1):2016008. DOI： 10.11959/j.issn.2096-0271.2016008.

Lei ZHAO, Bo YANG, Yong LIU, et al. Forecasting model for the fourth generation of cotton bollworm in corn fields based on big data[J]. BIG DATA RESEARCH, 2016, 2(1): 2016008. DOI： 10.11959/j.issn.2096-0271.2016008.

摘要

提出了一种基于支持向量机的预测模型。根据山东省1999-2013年玉米田第四代棉铃虫发生程度采集的数据，采用支持向量回归（SVR）算法，构建了玉米田第四代棉铃虫发生程度与其关联因子间的非线性关系模型，并对该方法进行了测试与分析。结果表明，由SVR预测模型得到的预测发生量与实际发生量基本一致，预测的平均绝对百分比误差为4.36%，预测值与实际值的相关系数为0.960 6，为玉米田第四代棉铃虫的有效防控提供了科学指导。

Abstract

The monitoring and forecasting model was put forward based on support vector machine program.According to the data collection of the fourth generation occurrence degree of the corn bollworm in Shandong province from 1999 to 2013

the support vector regression (SVR) method was adopted to build the nonlinear correlation model between the occurrence degree of the fourth generation bollworm and the associated factors.The method and the model were tested and analyzed.The results showed that the SVR forecasting model for prediction was almost in accord with the actual insect occurrence situation.The mean absolute percentage error was 4.36%

and the actual and estimated value of the correlation coefficient was 0.960 6.It could provide effective and accurate guidance to the cotton bollworm control in corn fields.

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Keywords

references

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