Maize haploid breeding technology is able to identify haploid seeds non‐destructively, rapidly and at low cost with the help of Near‐infrared (NIR) spectral analysis. However, due to the hybridization of numerous parents and the low production rate of haploid, the haploid data collection becomes a burden for engineering this technology. Biologically, there are considerable similarities between the progeny of the same female parent and different male parents. Based on this advantage, similar spectral data can be transferred when the NIR technology is employed. A revised method of Transfer adaptive boost (TrAdaBoost) is proposed to improve identifying for the backpropagation neural network (BPNN) classifier. To avoid the negative transfer, a screening thresh is used to select out similar data, and the amount of these data are limited to join current training. The results show that the identification performances are improved significantly when the data amount is small. This method shows a high ability to make the seed identification more convenient for engineering maize haploid breeding.

中文翻译：

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基于遗传相似度的玉米单倍体育种种子鉴定的数据传输方法

玉米单倍体育种技术能够借助近红外（NIR）光谱分析无损，快速，低成本地鉴定单倍体种子。然而，由于许多亲本的杂交和单倍体的低生产率，单倍体数据收集成为对该技术进行工程设计的负担。从生物学上说，同一雌父母和不同雄父母的后代之间有相当大的相似之处。基于此优势，当采用NIR技术时，可以传输相似的光谱数据。提出了一种改进的传递自适应增强（TrAdaBoost）方法，以改进对反向传播神经网络（BPNN）分类器的识别。为了避免负向转移，我们使用筛选脱粒来选择相似数据，并且这些数据的数量仅限于参加当前的培训。结果表明，当数据量较小时，识别性能显着提高。该方法显示出较高的能力，使种子鉴定更容易用于工程玉米单倍体育种。