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

Author

Listed:
  • Xiantao He

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Soil-Machine-Plant System Technology of Ministry of Agriculture, 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
    Key Laboratory of Soil-Machine-Plant System Technology of Ministry of Agriculture, Beijing 100083, China)

  • Li Yang

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Soil-Machine-Plant System Technology of Ministry of Agriculture, Beijing 100083, China)

  • Tao Cui

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Soil-Machine-Plant System Technology of Ministry of Agriculture, Beijing 100083, China)

  • Kailiang Zhang

    (College of Engineering, China Agricultural University, Beijing 100083, China
    Key Laboratory of Soil-Machine-Plant System Technology of Ministry of Agriculture, Beijing 100083, China)

  • Xiaolong Lin

    (College of Engineering, China Agricultural University, Beijing 100083, China)

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.

Suggested Citation

  • Xiantao He & Jinting Zhu & Pinxuan Li & Dongxing Zhang & Li Yang & Tao Cui & Kailiang Zhang & Xiaolong Lin, 2024. "Research on a Multi-Lens Multispectral Camera for Identifying Haploid Maize Seeds," Agriculture, MDPI, vol. 14(6), pages 1-12, May.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:6:p:800-:d:1399673
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    References listed on IDEAS

    as
    1. Wenjie Zhang & Liang Zhu & Qifeng Zhuang & Dong Chen & Tao Sun, 2023. "Mapping Cropland Soil Nutrients Contents Based on Multi-Spectral Remote Sensing and Machine Learning," Agriculture, MDPI, vol. 13(8), pages 1-19, August.
    2. Olaf Erenstein & Moti Jaleta & Kai Sonder & Khondoker Mottaleb & B.M. Prasanna, 2022. "Global maize production, consumption and trade: trends and R&D implications," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(5), pages 1295-1319, October.
    3. Xin Yang & Shichen Gao & Qian Sun & Xiaohe Gu & Tianen Chen & Jingping Zhou & Yuchun Pan, 2022. "Classification of Maize Lodging Extents Using Deep Learning Algorithms by UAV-Based RGB and Multispectral Images," Agriculture, MDPI, vol. 12(7), pages 1-16, July.
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