Decision tree predictive classification model on the occurrence degree of wheat aphids based on big data

Qingqing ZHANG; Yong LIU; Shaomin MU; Fujiang WEN

doi:10.11959/j.issn.2096-0271.2016007

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Decision tree predictive classification model on the occurrence degree of wheat aphids based on big data

Special topics:agricultural data | 更新时间：2024-06-03

- Decision tree predictive classification model on the occurrence degree of wheat aphids based on big data
- BIG DATA RESEARCH Vol. 2, Issue 1, Pages: 2016007(2016)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2016007
  CLC： S431.9
- Online First：2016-01，
  
  Published：20 January 2016
- 稿件说明：
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Qingqing ZHANG, Yong LIU, Shaomin MU, et al. Decision tree predictive classification model on the occurrence degree of wheat aphids based on big data[J]. BIG DATA RESEARCH, 2016, 2(1): 2016007.
DOI：

Qingqing ZHANG, Yong LIU, Shaomin MU, et al. Decision tree predictive classification model on the occurrence degree of wheat aphids based on big data[J]. BIG DATA RESEARCH, 2016, 2(1): 2016007. DOI： 10.11959/j.issn.2096-0271.2016007.

摘要

小麦蚜虫是危害小麦的主要害虫。其发生程度预测特别是短期预测一直是植物保护领域难以解决的科学问题。传统预测方法通常仅采用温湿度，预测结果与实际发生匹配度不高。基于大数据的理念和数据挖掘技术，通过对2003-2013年小麦蚜虫发生程度与瓢虫、寄生蜂、日最高气压、日照时数等18种变量关系的决策树分析，构建了分类模型。经分析发现，日照时数与小麦蚜虫的发生程度关联度最高，其次是天敌瓢虫。该模型置信度为91.49%，且运行稳健。

Abstract

Wheat aphids are the main pests of wheat crops.The monitoring and forecasting of their occurrence degree

especially the short-term occurrence degree

is much difficult.Many traditional predictions rely only on temperature and humidity

so the match degree to the actual occurrence value is low.Based on the concept of big data and data mining programs

the predictive classification model was established by means of the decision tree analysis of the relationship between the occurrence degree of aphids and up to 18 variables.It was found out that the duration of sunshine has the highest degree of relevance to the forecasting level of aphids

followed by ladybird.The confidence coefficient of the model that runs steadily in the experiment is 91.49%.

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references

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