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

Lin, Jianchu; Li, Jinlong; Li, Weijun; Qin, Hong; Chen, Shaojiang

doi:10.1111/pbr.12746

Cited by 2 publications

(1 citation statement)

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“…Currently, the widely used method for haploid identification is the R1-nj color marker system, where the haploid seeds show purple color on the aleurone only and the diploids exhibit purple color on both the aleurone and scutellum (Chaikam et al 2015). To identify the haploid kernels more accurately and worthwhile, multiple different approaches or markers have been developed, including oil content (Ming 2003), Near-Infrared Spectroscopy (NIR) (Jones et al 2012;Lin et al 2019), and the double-fluorescence protein (DFP) marker (Dong et al 2018). Chromosome doubling, or the haploid male fertility (HMF) and haploid female fertility (HFF), can be enhanced by chemical reagents, such as colchicine and herbicide (Saisingtong et al 1996), but these chemicals are both harmful for human health and detrimental for the environment.…”

Section: Introductionmentioning

confidence: 99%

Genomic selection to optimize doubled haploid-based hybrid breeding in maize

Cheng

Guo

et al. 2020

Preprint

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Crop improvement, as a long-term endeavor, requires continuous innovations in technique from multiple perspectives. Doubled haploid (DH) technology for pure inbred production, which shaves years off of the conventional selfing approach, has been widely used for breeding. However, the final success rate of in vivo maternal DH production is determined by four factors: haploids induction, haploids identification, chromosome doubling, and successful selfing of the fertile haploid plants to produce DH seeds. Traits in each of these steps, if they can be accurately predicted using genomic selection methods, will help adjust the DH production protocol and simplify the logistics and save costs. Here, a hybrid population (N=158) was generated based on an incomplete half diallel design using 27 elite inbred lines. These hybrids were induced to create F1-derived haploid families. The hybrid materials, as well as the 27 inbreds, the inbred-derived haploids (N=200), and the F1-derived haploids (N=5,000) were planted in the field to collect four DH-production traits, three yield-related traits, and three developmental traits. Quantitative genetics analysis suggested that in both diploids and haploid families, most of the developmental traits showed high heritability, while the DH-production and developmental traits exhibited intermediate levels of heritability. By employing different genomic selection models, our results showed that the prediction accuracy ranged from 0.52 to 0.59 for the DH-production traits, 0.50 to 0.68 for the yield-related traits, and 0.44 to 0.87 for the developmental traits. Further analysis using index selection achieved the highest prediction accuracy when considering both DH production efficiency and the agronomic trait performance. Furthermore, the long-term responses through simulation confirmed that index selection would increase the genetic gain for targeted agronomic traits while maintaining the DH production efficiency. Therefore, our study provides an optimization strategy to integrate GS technology for DH-based hybrid breeding.

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Section: Introductionmentioning

confidence: 99%

Genomic selection to optimize doubled haploid-based hybrid breeding in maize

Cheng

Guo

et al. 2020

Preprint

Self Cite

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Automated seed identification with computer vision: challenges and opportunities

Zhao

Haque

Wang

2022

Seed Science and Technology

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Applying advanced technologies such as computer vision is highly desirable in seed testing. Among testing needs, computer vision is a feasible technology for conducting seed and seedling classification used in purity analysis and in germination tests. This review focuses on seed identification that currently encounters extreme challenges due to a shortage of expertise, time-consuming training and operation, and the need for large numbers of reference specimens. The reviewed computer vision techniques and application strategies also apply to other methods in seed testing. The review describes the development of machine learning-based computer vision in automating seed identification and their limitations in feature extraction and accuracy. As a subset of machine learning techniques, deep learning has been applied successfully in many agricultural domains, which presents potential opportunities for its application in seed identification and seed testing. To facilitate application in seed testing, the challenges of deep learning-based computer vision systems are summarised through analysing their application in other agricultural domains. It is recommended to accelerate the application in seed testing by optimising procedures or approaches in image acquisition technologies, dataset construction and model development. A concept flow chart for using computer vision systems is proposed to advance computer-assisted seed identification.

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

Cited by 2 publications

References 44 publications

Genomic selection to optimize doubled haploid-based hybrid breeding in maize

Genomic selection to optimize doubled haploid-based hybrid breeding in maize

Automated seed identification with computer vision: challenges and opportunities

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