Identification of Male and Female Parents for Hybrid Rice Seed Production Using UAV-Based Multispectral Imagery

Hanchao Liu, Yuan Qi, Wenwei Xiao, Haoxin Tian, Dehua Zhao, Ke Zhang, Junqi Xiao, Xiaoyang Lu, Yubin Lan and Yali Zhang
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Hanchao Liu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Yuan Qi: College of Mechanical Engineering, Nantong Vocational University, Nantong 226007, China
Wenwei Xiao: Mechanical and Electrical Department, Foshan Huacai Vocational and Technical School, Foshan 528000, China
Haoxin Tian: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Dehua Zhao: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Ke Zhang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Junqi Xiao: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Xiaoyang Lu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Yubin Lan: National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China
Yali Zhang: College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2022, vol. 12, issue 7, 1-16

Abstract: Identifying and extracting male and female parent of hybrid rice and then accurately judging the spikelet flowering of male parents is the basis of hybrid rice pollination. Currently, male parent flowering information extraction for hybrid rice is basically obtained by manual recognition. In this study, remote sensing images of parental rice fields were obtained with a multispectral camera carried by a UAV (Umanned Aerial Vehicle). Six kinds of visible light vegetation indices and four kinds of multispectral vegetation indices, together with two classification methods, pixel-based supervised classification and sample-based object-oriented classification, were applied to identify the male and female parents of hybrid rice, after which the accuracies of the methods were compared. The results showed that the visible vegetation index had a better effect in pixel-based supervised classification. The kappa coefficient of ExGR (Excess Green minus Excess Red index) classification was 0.9256 and the total accuracy was 0.9552. The extraction accuracy was higher than that of the other vegetation indices and object-oriented classification. In pixel-based supervised classification, the maximum likelihood method achieved the highest identification accuracy and shortest calculation time. Taking the remote sensing images obtained with a UAV as a data source, maximum likelihood supervised classification based on ExGR index can more effectively and quickly identify the field information of male and female parents of hybrid rice so as to provide a reference for determining optimal pollination timing for hybrid rice in large-scale seed production farms.

Keywords: UAV; hybrid rice; pollination; multispectral imagery; vegetation index; supervised classification; object-oriented classification; kappa coefficient; maximum likelihood; extraction accuracy (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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