Research on the effect of meteorological factors on Camellia oleifera yield based on decision tree algorithm

doi:10.14088/j.cnki.issn0439-8114.2020.21.039

Abstract

Abstract: In this study, two kinds of decision tree algorithms, CART（Classification and regression tree） and CHAID（Chi-Square automatic interaction detection） were adopted to simulate the yield of Camellia oleifera of Hunan province based on meteorological factors of different phenological phase over from 2010 to 2016. The results showed that the average relative errors of CART and CHAID algorithms were 8.80% and 14.30% respectively, and the trend accuracy were 97.40% and 92.20% respectively. The accumulated temperature above 0 ℃ and the average maximum temperature at flowering stage had the greatest influence on Camellia oleifera yield. In the first fruit expansion period and the peak of oil transformation and accumulation, the diurnal temperature range, the mean minimum temperature and high temperature days were the most important. It is of great significance for improving the efficiency of Camellia oleifera industry chain.

Key words: Camellia oleifera Abel., meteorological factors, decision tree algorithm

CLC Number:

S794.4

HUANG Chao, LIAO Yu-fang, JIANG Yuan-hua, PENG Jia-dong. Research on the effect of meteorological factors on Camellia oleifera yield based on decision tree algorithm[J]. HUBEI AGRICULTURAL SCIENCES, 2020, 59(21): 177-183.

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