Discrimination of haploid and diploid maize kernels via multispectral imaging

Abstract

The use of doubled haploids (DHs) in maize has become ubiquitous in maize breeding programmes as it allows breeders to go from cross to evaluation in as little as 2 years. Two important aspects of the in vivo DH system used in maize are as follows: (i) the identification of haploid progeny and (ii) doubling of the haploid genome to produce fertile inbred lines. This study is focused on the first step. Currently, identification of maize haploid progeny is performed manually using the R1‐nj seed colour marker. This is a labour‐intensive and time‐consuming process; a method for automated sorting of haploids would increase the efficiency of DH line development. In this study, six inbred lines were crossed with the maternal haploid inducer ‘RWS/RWK‐76’ and a sample of seed was sorted manually for each line. Using the VideometerLab 3 system, spectral imaging techniques were applied to discriminate between haploids and hybrids. Using DNA markers to confirm the haploid/diploid state of the tested seed, for the majority of genotypes haploid identification was possible with over 50% accuracy.

A Diallel Analysis of a Maize Donor Population Response to In Vivo Maternal Haploid Induction: I. Inducibility

“…The effective and efficient use of the maize in vivo maternal DH system relies on the ability to efficiently produce, sort, and effectively identify haploid progeny. There are multiple avenues to increase the efficiency of this system: (i) development and use of improved inducer lines (Coe, 1959;Rober et al, 2005;Geiger, 2009;Prigge et al, 2011), (ii) improved methods of haploid selection through new traits and/or automation (Jones et al, 2012;Melchinger et al, 2015;Smelser et al, 2015;Boote et al, 2016;De La Fuente et al, 2017), and (iii) the method considered in this and other studies (Kebede et al, 2011;Prigge et al, 2011), namely improvement of rates of induction in donor materials.…”

“…Prigge et al, 2011), (ii) improved methods of haploid selection through new traits and/or automation(Jones et al, 2012;Melchinger et al, 2015;Smelser et al, 2015;Boote et al, 2016;De La Fuente et al, 2017), and (iii) the method considered in this and other studies(Kebede et al, 2011;Prigge et al, 2011), namely improvement of rates of induction in donor materials.…”

Multispectral Imaging for Plant Food Quality Analysis and Visualization

“…Using 19 optimal wavelengths, a multi‐model strategy was developed which exhibited a highly accurate discrimination of maize seed varieties (96.57%) (Yang and others ). However, multispectral imaging could only identify maize haploid progeny and hybrid progeny with 50% accuracy (De and others ). In addition, a multispectral imaging technique could be utilized to assess quality attributes of rice, tomato seed, oat kernel, and tubers.…”

A data transfer method for improving seed identification of maize (Zea mays) haploid breeding based on genetic similarity

“…In order to support this technology, the NIR‐DT spectral analysis was used to help identifying haploid seeds (Janes et al, ; Qin, Ma, Chen, Li, & Yan, ; Qin et al, ). The NIR has the advantages of being non‐destructive (Jiang et al, ; Pasquini, ; Rhiel, Cohen, Murhammer, & Arnold, ; Sandora et al, ), real‐time (Alves‐Rausch, Bienert, Grimm, & Bergmaier, ; Blanco & Villarroya, ; Sandnes et al, ; Sato, Zahlner, Berghofer, Lošák, & Vollmann, ) and of less cost (Sandora et al, ) than other methods, like nuclear magnetic resonance (NMR) (Chen & Song, ), weighting (Smelser et at., ) and image (De La Fuente et al, ). Through the diffuse transmittance (DT) mode, the spectra of non‐uniform organism seeds can be effectively collected with less disturbing effects from other side of seed (Qin et al, , ), and this improvement shows a high performance of seed identification (Li & Li, ; Lin, Yu, Li, & Qin, ; Yu et al, ).…”

“…2003), weighting (Smelser et at., 2015) and image (De La Fuente et al, 2017). Through the diffuse transmittance (DT) mode, the spectra of non-uniform organism seeds can be effectively collected with less disturbing effects from other side of seed (Qin et al, 2015(Qin et al, , 2016, and this improvement shows a high performance of seed identification Lin, Yu, Li, & Qin, 2018;Yu et al, 2018).…”