Prediction Model of Meteorological Risk Grade for Potato Late Blight in Hubei Province Based on Discriminant Analysis

doi:10.11924/j.issn.1000-6850.casb2022-0290

Abstract

Abstract:

The long-term prediction methods of meteorological risk grade for incidence of potato late blight were studied to provide a basis for scientific prevention and control of the disease. With the data in the low hilly and plain area of Hubei Province from 2011 to 2019, the correlations between incidence of potato late blight and meteorological factors were analyzed, and the meteorological factors which had great influence on the occurrence and epidemics of potato late blight were selected. By using discriminant analysis method and data of the meteorological factors selected above and artificial control factor from 2011 to 2017, prediction model of meteorological risk grade for incidence of potato late blight was established. The results showed that the incidence of late blight was correlated with the mean minimum temperature in middle March, sunshine duration in early April and wind speed in late April obviously (P<0.05). It was also indicated that the accuracy of the prediction model was 75.9% in self-validation and 75.3% in cross-validation, and the accuracy of the prediction model was 61.7%, which was tested with data from 2018 to 2019. It proved that the prediction model of meteorological risk grade for incidence of potato late blight could basically meet the needs of meteorological service, and it could provide reference for agricultural technicians to carry out late blight prevention and control in advance by predicting the incidence of potato late blight of the year.

Key words: late blight, discriminant analysis, the low hilly and plain area, prediction model, cross-validation

TANG Yang, LIU Kequn, YANG Junjie, DENG Huanhuan, DENG Aijuan, CHE Junzhi. Prediction Model of Meteorological Risk Grade for Potato Late Blight in Hubei Province Based on Discriminant Analysis[J]. Chinese Agricultural Science Bulletin, 2023, 39(8): 74-78.

Figures/Tables 3

References 32

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验证方法	实际等级	预测等级			合计	分类精度/%
验证方法	实际等级	1级	2级	3级	合计	分类精度/%
初始分组验证	1级	67	13	3	83	80.7
	2级	6	18	7	31	58.1
	3级	2	11	47	60	78.3
交叉验证	1级	67	12	4	83	80.7
	2级	7	17	7	31	54.8
	3级	2	11	47	60	78.3

验证方法	实际等级	预测等级			合计	分类精度/%
验证方法	实际等级	1级	2级	3级	合计	分类精度/%
初始分组验证	1级	67	13	3	83	80.7
	2级	6	18	7	31	58.1
	3级	2	11	47	60	78.3
交叉验证	1级	67	12	4	83	80.7
	2级	7	17	7	31	54.8
	3级	2	11	47	60	78.3

实际等级	预测等级			合计	分类精度/%
实际等级	1级	2级	3级	合计	分类精度/%
1级	22	0	0	22	100.0
2级	8	1	6	15	6.7
3级	7	2	14	23	60.9

实际等级	预测等级			合计	分类精度/%
实际等级	1级	2级	3级	合计	分类精度/%
1级	22	0	0	22	100.0
2级	8	1	6	15	6.7
3级	7	2	14	23	60.9