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Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology. | LitMetric

Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology.

Front Plant Sci

Department of Seed Science & Biotechnology, The Innovation Center (Beijing) of Crop Seeds whole-process Technology Research Ministry of Agriculture and Rural Affairs (MOA), Beijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China.

Published: September 2022

AI Article Synopsis

  • The main methods for producing hybrid wheat involve chemical hybridization and genic male sterility, but achieving complete sterility in female plants is challenging due to various factors, leading to seed purity issues.
  • Traditional methods for detecting seed purity are labor-intensive and destructive, prompting the need for a non-destructive classification technique.
  • The study utilized hyperspectral imaging and machine learning (PLS-DA) to distinguish between hybrid and female parent seeds, achieving high accuracy rates (up to 98.25%), which shows promise for faster and more efficient seed purity detection in the future.

Article Abstract

Chemical hybridization and genic male sterility systems are two main methods of hybrid wheat production; however, complete sterility of female wheat plants cannot be guaranteed owing to the influence of the growth stage and weather. Consequently, hybrid wheat seeds are inevitably mixed with few parent seeds, especially female seeds. Therefore, seed purity is a key factor in the popularization of hybrid wheat. However, traditional seed purity detection and variety identification methods are time-consuming, laborious, and destructive. Therefore, to establish a non-destructive classification method for hybrid and female parent seeds, three hybrid wheat varieties (Jingmai 9, Jingmai 11, and Jingmai 183) and their parent seeds were sampled. The transmittance and reflectance spectra of all seeds were collected hyperspectral imaging technology, and a classification model was established using partial least squares-discriminant analysis (PLS-DA) combined with various preprocessing methods. The transmittance spectrum significantly improved the classification of hybrids and female parents compared to that obtained using reflectance spectrum. Specifically, using transmittance spectrum combined with a characteristic wavelength-screening algorithm, the Detrend-CARS-PLS-DA model was established, and the accuracy rates in the testing sets of Jingmai 9, Jingmai 11, and Jingmai 183 were 95.69%, 98.25%, and 97.25%, respectively. In conclusion, transmittance hyperspectral imaging combined with a machine learning algorithm can effectively distinguish female parent seeds from hybrid seeds. These results provide a reference for rapid seed purity detection in the hybrid production process. Owing to the non-destructive and rapid nature of hyperspectral imaging, the detection of hybrid wheat seed purity can be improved by online sorting in the future.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554440PMC
http://dx.doi.org/10.3389/fpls.2022.1015891DOI Listing

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