Research on a Multi-Lens Multispectral Camera for Identifying Haploid Maize Seeds

Xiantao He, Jinting Zhu, Pinxuan Li, Dongxing Zhang, Li Yang (), Tao Cui, Kailiang Zhang and Xiaolong Lin
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Xiantao He: College of Engineering, China Agricultural University, Beijing 100083, China
Jinting Zhu: College of Engineering, China Agricultural University, Beijing 100083, China
Pinxuan Li: College of Engineering, China Agricultural University, Beijing 100083, China
Dongxing Zhang: College of Engineering, China Agricultural University, Beijing 100083, China
Li Yang: College of Engineering, China Agricultural University, Beijing 100083, China
Tao Cui: College of Engineering, China Agricultural University, Beijing 100083, China
Kailiang Zhang: College of Engineering, China Agricultural University, Beijing 100083, China
Xiaolong Lin: College of Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2024, vol. 14, issue 6, 1-12

Abstract: Haploid breeding can shorten the breeding period of new maize varieties and is an important means to increase maize yield. In the breeding program, a large number of haploid seeds need to be screened, and this step is mainly achieved manually, which hinders the industrialization of haploid maize breeding. This article aims to develop a multispectral camera to identify the haploid seeds automatically. The camera was manufactured by replacing narrow-band filters of the ordinary CCD camera, and the RGB, 405 nm, 980 nm and 1050 nm images of haploid or diploid seeds were simultaneously captured (the characteristic wavelengths were determined according to color and high-oil markers of maize). The performance was tested using four maize varieties with the two genetic markers. The results show that the developed multispectral camera significantly improved the recognition accuracy of haploid maize seeds to 92.33%, 97.33%, 97% and 93.33% for the TYD1903, TYD1904, TYD1907 and TYD1908 varieties, respectively. The cameras in the near-infrared region (wavelengths of 980 nm and 1050 nm) achieved better performance for the varieties of high-oil marker, with an increase of 0.84% and 1.5%, respectively. These results demonstrate the strong potential of the multispectral imaging technology in the haploid seed identification of maize.

Keywords: maize; haploid seed identification; multispectral imaging; AlexNet-based model (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: 2024
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