Xiong Xiong, Yu Lejun, Yang Wanneng, Liu Meng, Jiang Ni, Wu Di, Chen Guoxing, Xiong Lizhong, Liu Kede, Liu Qian
Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, 1037 Luoyu Rd., Wuhan, 430074 People's Republic of China.
National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, 430070 People's Republic of China.
Plant Methods. 2017 Jan 31;13:7. doi: 10.1186/s13007-017-0157-7. eCollection 2017.
The fitness of the rape leaf is closely related to its biomass and photosynthesis. The study of leaf traits is significant for improving rape leaf production and optimizing crop management. Canopy structure and individual leaf traits are the major indicators of quality during the rape seedling stage. Differences in canopy structure reflect the influence of environmental factors such as water, sunlight and nutrient supply. The traits of individual rape leaves traits indicate the growth period of the rape as well as its canopy shape.
We established a high-throughput stereo-imaging system for the reconstruction of the three-dimensional canopy structure of rape seedlings from which leaf area and plant height can be extracted. To evaluate the measurement accuracy of leaf area and plant height, 66 rape seedlings were randomly selected for automatic and destructive measurements. Compared with the manual measurements, the mean absolute percentage error of automatic leaf area and plant height measurements was 3.68 and 6.18%, respectively, and the squares of the correlation coefficients (R) were 0.984 and 0.845, respectively. Compared with the two-dimensional projective imaging method, the leaf area extracted using stereo-imaging was more accurate. In addition, a semi-automatic image analysis pipeline was developed to extract 19 individual leaf shape traits, including 11 scale-invariant traits, 3 inner cavity related traits, and 5 margin-related traits, from the images acquired by the stereo-imaging system. We used these quantified traits to classify rapes according to three different leaf shapes: mosaic-leaf, semi-mosaic-leaf, and round-leaf. Based on testing of 801 seedling rape samples, we found that the leave-one-out cross validation classification accuracy was 94.4, 95.6, and 94.8% for stepwise discriminant analysis, the support vector machine method and the random forest method, respectively.
In this study, a nondestructive and high-throughput stereo-imaging system was developed to quantify canopy three-dimensional structure and individual leaf shape traits with improved accuracy, with implications for rape phenotyping, functional genomics, and breeding.
油菜叶片的适应性与其生物量和光合作用密切相关。叶片性状的研究对于提高油菜叶片产量和优化作物管理具有重要意义。冠层结构和单叶性状是油菜苗期质量的主要指标。冠层结构的差异反映了水分、光照和养分供应等环境因素的影响。单个油菜叶片的性状表明了油菜的生长时期及其冠层形状。
我们建立了一个高通量立体成像系统,用于重建油菜幼苗的三维冠层结构,从中可以提取叶面积和株高。为了评估叶面积和株高的测量精度,随机选择了66株油菜幼苗进行自动和破坏性测量。与手动测量相比,自动叶面积和株高测量的平均绝对百分比误差分别为3.68%和6.18%,相关系数(R)的平方分别为0.984和0.845。与二维投影成像方法相比,使用立体成像提取的叶面积更准确。此外,还开发了一种半自动图像分析流程,从立体成像系统获取的图像中提取19个单叶形状性状,包括11个尺度不变性状、3个内腔相关性状和5个边缘相关性状。我们使用这些量化性状根据三种不同的叶形对油菜进行分类:花叶、半花叶和圆叶。基于对801个油菜幼苗样本的测试,我们发现逐步判别分析、支持向量机方法和随机森林方法的留一法交叉验证分类准确率分别为94.4%、95.6%和94.8%。
在本研究中,开发了一种无损且高通量的立体成像系统,以提高精度量化冠层三维结构和单叶形状性状,对油菜表型分析、功能基因组学和育种具有重要意义。