基于大数据的小麦蚜虫发生程度决策树预测分类模型

doi:10.11959/j.issn.2096-0271.2016007

Abstract

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%.

Key words: wheat aphids, agricultural big data, decision tree, classification model

CLC Number:

S431.9

Qingqing ZHANG, Yong LIU, Shaomin MU, Fujiang WEN. Decision tree predictive classification model on the occurrence degree of wheat aphids based on big data[J]. Big Data Research, 2016, 2(1): 59-67.

Figures/Tables 7

References 19

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级别	1级	2级	3级	4级	5级
百株蚜量（头，Y）	Y≤500	500<Y≤1 500	1 500<Y≤2 500	2 500<Y≤3 500	Y>3 500

输入变量	信息增益率
最大风速（0.1 m/s）	0
平均水汽压（0.1 hPa）	0.004 3
日最低气温（0.1 ℃）	0.018 4
日最高本站气压（0.1 hPa）	0.051
20∶00-次日20∶00降水量（0.1 mm）	0.068 3
日最低本站气压（0.1 hPa）	0.080 1
极大风速风向（方位）	0.107
寄生蜂（头/百株）	0.130 2
瓢虫（头/m²）	0.162 5
日照时数（0.1 h）	0.378 2

正确	错误	总计
223个	21个	244个
91.49%	8.51%	100%

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

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