A critical step toward the success of the doubled haploid (DH) technique is the haploid identification within induction crosses. The R1-nj marker is the principal mechanism employed in this task enabling the selection of haploids at the seed stage. Although it seems easy to identify haploid seeds, this task is performed manually by visual classification, which becomes an inefficient process in terms of time and labor. Also, differential phenotypic expression of the R1-nj marker results in high rates of false positives among haploid seeds. For the first time, an image-based convolutional neural network (CNN) was trained to identify true positives among putative haploid seeds. The experiment was conducted using 3,000 maize (Zea mays L.) seeds from induction crosses classified as haploid (1,000), diploid (1,000), and inhibited (1,000) class. Images were taken from each seed, and then seeds were planted in the field to confirm their ploidy. For putative haploids (R1-nj phenotype), the classification accuracy on average was 94.39%, 97.07% for the haploid class, and 91.71% for the diploid class. However, the CNN model was unable to distinguish true haploid seeds among the putative haploid class, which indicates that CNN did not recognize different patterns between them. Finally, we provided a highly accurate and trained CNN model to the scientific community to classify haploid maize seeds via R1-nj, which can support maize breeders to optimize DH pipelines, mainly for small breeding programs with limited resources.

中文翻译：

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使用卷积神经网络改进单倍体玉米种子的识别

双单倍体 (DH) 技术成功的关键一步是在感应杂交中进行单倍体鉴定。在R1-NJ标记是在该任务使单倍体的选择在种子阶段中采用的主要机制。虽然识别单倍体种子似乎很容易，但这项任务是通过视觉分类手动执行的，这在时间和劳动力方面变得效率低下。此外，R1-nj标记的差异表型表达导致单倍体种子的假阳性率很高。首次训练基于图像的卷积神经网络 (CNN) 来识别推定的单倍体种子中的真阳性。该实验使用 3,000 粒玉米（Zea maysL.) 来自诱导杂交的种子，分为单倍体 (1,000)、二倍体 (1,000) 和抑制 (1,000) 类。对每颗种子拍摄图像，然后将种子种植在田间以确认它们的倍性。对于推定的单倍体（R1-nj表型），平均分类准确率为 94.39%，单倍体类为 97.07%，二倍体类为 91.71%。然而，CNN 模型无法在假定的单倍体类别中区分真正的单倍体种子，这表明 CNN 无法识别它们之间的不同模式。最后，我们向科学界提供了一个高度准确和训练有素的 CNN 模型，通过R1-nj对单倍体玉米种子进行分类，它可以支持玉米育种者优化​​ DH 管道，主要用于资源有限的小型育种计划。