Wu Guangxia, Fei Lin, Deng Limiao, Yang Haoyan, Han Meng, Han Zhongzhi, Zhao Longgang
College of Agronomy, Qingdao Agricultural University, Qingdao 266109, China.
Academy of Dongying Efficient Agricultural Technology and Industry on Saline and Alkaline Land in Collaboration with Qingdao Agricultural University, Dongying 257091, China.
Plants (Basel). 2023 Jun 14;12(12):2315. doi: 10.3390/plants12122315.
The accurate identification and classification of soybean mutant lines is essential for developing new plant varieties through mutation breeding. However, most existing studies have focused on the classification of soybean varieties. Distinguishing mutant lines solely by their seeds can be challenging due to their high genetic similarities. Therefore, in this paper, we designed a dual-branch convolutional neural network (CNN) composed of two identical single CNNs to fuse the image features of pods and seeds together to solve the soybean mutant line classification problem. Four single CNNs (AlexNet, GoogLeNet, ResNet18, and ResNet50) were used to extract features, and the output features were fused and input into the classifier for classification. The results demonstrate that dual-branch CNNs outperform single CNNs, with the dual-ResNet50 fusion framework achieving a 90.22 ± 0.19% classification rate. We also identified the most similar mutant lines and genetic relationships between certain soybean lines using a clustering tree and t-distributed stochastic neighbor embedding algorithm. Our study represents one of the primary efforts to combine various organs for the identification of soybean mutant lines. The findings of this investigation provide a new path to select potential lines for soybean mutation breeding and signify a meaningful advancement in the propagation of soybean mutant line recognition technology.
通过诱变育种培育新的植物品种时，准确识别和分类大豆突变系至关重要。然而，现有的大多数研究都集中在大豆品种的分类上。由于大豆突变系的遗传相似性很高，仅通过其种子来区分它们可能具有挑战性。因此，在本文中，我们设计了一种由两个相同的单卷积神经网络（CNN）组成的双分支卷积神经网络，将豆荚和种子的图像特征融合在一起，以解决大豆突变系分类问题。使用四个单卷积神经网络（AlexNet、GoogLeNet、ResNet18和ResNet50）来提取特征，并将输出特征融合后输入分类器进行分类。结果表明，双分支卷积神经网络的性能优于单卷积神经网络，双ResNet50融合框架的分类率达到了90.22±0.19%。我们还使用聚类树和t分布随机邻域嵌入算法确定了某些大豆品系之间最相似的突变系和遗传关系。我们的研究是将各种器官结合起来识别大豆突变系的主要努力之一。本研究结果为大豆诱变育种选择潜在品系提供了一条新途径，标志着大豆突变系识别技术传播方面的一项有意义的进展。
