Guo Yixin, Gao Zhiqiang, Zhang Zhanguo, Li Yang, Hu Zhenbang, Xin Dawei, Chen Qingshan, Zhu Rongsheng
College of Engineering, Northeast Agricultural University, Harbin, China.
College of Arts and Sciences, Northeast Agricultural University, Harbin, China.
Front Plant Sci. 2022 Jul 11;13:906751. doi: 10.3389/fpls.2022.906751. eCollection 2022.
The stem-related phenotype of mature stage soybean is important in soybean material selection. How to improve on traditional manual methods and obtain the stem-related phenotype of soybean more quickly and accurately is a problem faced by producers. With the development of smart agriculture, many scientists have explored soybean phenotypes and proposed new acquisition methods, but soybean mature stem-related phenotype studies are relatively scarce. In this study, we used a deep learning method within the convolutional neural network to detect mature soybean stem nodes and identified soybean structural features through a novel directed search algorithm. We subsequently obtained the pitch number, internodal length, branch number, branching angle, plant type spatial conformation, plant height, main stem length, and new phenotype-stem curvature. After 300 epochs, we compared the recognition results of various detection algorithms to select the best. Among them, YOLOX had a maximum average accuracy (mAP) of 94.36% for soybean stem nodes and scale markers. Through comparison of the phenotypic information extracted by the directed search algorithm with the manual measurement results, we obtained the Pearson correlation coefficients, R, of plant height, pitch number, internodal length, main stem length, stem curvature, and branching angle, which were 0.9904, 0.9853, 0.9861, 0.9925, 0.9084, and 0.9391, respectively. These results show that our algorithm can be used for robust measurements and counting of soybean phenotype information, which can reduce labor intensity, improve efficiency, and accelerate soybean breeding.
成熟阶段大豆的茎相关表型在大豆材料选择中很重要。如何改进传统的人工方法，更快、更准确地获取大豆的茎相关表型是生产者面临的一个问题。随着智慧农业的发展，许多科学家对大豆表型进行了探索并提出了新的获取方法，但大豆成熟茎相关表型的研究相对较少。在本研究中，我们使用卷积神经网络中的深度学习方法来检测成熟大豆的茎节点，并通过一种新颖的定向搜索算法识别大豆的结构特征。随后，我们获得了节数、节间长度、分枝数、分枝角度、株型空间构型、株高、主茎长度以及新的表型——茎曲率。经过300个轮次的训练后，我们比较了各种检测算法的识别结果以选出最佳算法。其中，YOLOX对大豆茎节点和尺度标记的最大平均精度（mAP）为94.36%。通过将定向搜索算法提取的表型信息与人工测量结果进行比较，我们得到了株高、节数、节间长度、主茎长度、茎曲率和分枝角度的皮尔逊相关系数R，分别为0.9904、0.9853、0.9861、0.9925、0.9084和0.9391。这些结果表明，我们的算法可用于对大豆表型信息进行可靠的测量和计数，能够降低劳动强度、提高效率并加速大豆育种。
