High-Throughput Phenotyping of Individual Wheat Stem and Ear Traits with Machine Vision
DING QiShuo1, LI HaiKang1, SUN KeRun2, HE RuiYin1, WANG XiaoChan1, LIU FuXi1, LI Xiang11 College of Engineering, Nanjing Agricultural University/Key Laboratory of Intelligent Agricultural Equipment of Jiangsu Province, Nanjing 210031 2 Jiangsu Yinhua Chunxiang Machinery Manufacturing Co. Ltd., Lianyungang 222200, Jiangsu
Abstract 【Objective】 High-throughput phenotyping (HTP) is not only an important tool of modern agriculture for crop breeding, but also a powerful means to illustrate physiological and ecological mechanisms of crops in the field. However, the basic features of structural components of each HTP tools have to be illustrated. It is therefore necessary to investigate what a technical feature is applicable to machine vision based HTP system.【Method】 An image-processing tool was developed to measure stem-and-ear level traits of each individual wheat stem. Three wheat species, i.e. Ningmai 13, Luyuan 502 and Zhengmai 9023, were used for plot experiment analysis. The wheat was sown with luffer board having equally spaced seeding holes. The precision seeding tools were applied to control wheat population accurately. At the maturity of post-paddy wheat, the integrated image of stem and ear was obtained, and the image was subjected to gray enhancement, histogram equalization, S component extraction, Otsu threshold segmentation, stem and ear separation, and stem and ear morphology parameters. The morphological parameters of the individual organs per stem-panicle of the extracted post-paddy wheat included stem length, average stem width, stem projection area, stem circumference, ear length, average ear width, ear projection area and ear circumference. In addition, traditional methods of measurement were used to derive single leaf weight, single stem weight, single ear weight and single ear yield etc. Linear, quadratic, extended and exponential models were applied for the regression on the collected multi-dimensional data sets, including correlations between ear and stem level biomass and individual ear grain yield, interrelationships among morphological parameters of stem and ear and single ear grain yield. Correlation analysis and regression analysis were performed on the processed indices of wheat. Based on this case study, some key aspects of technologies were discussed concerning on the application of machine vision tools on high-throughput phenotyping in the field.【Result】Results showed that correlation coefficients of individual stem and leaf weight with individual ear grain yield decreased steadily from Ningmai 13 to Luyuan 502, and till Zhengmai 9023. Correlation coefficient of stem and ear morphological parameters with individual ear grain yield was significantly lower than that among the biomasses. However, composite morphological parameter, which integrated single ear projection area and single ear length, was found significantly correlated with individual ear grain yield. The best regression model for the correlation between stem and ear biomass and individual ear grain yield of the three wheat species were different. Morphological parameters derived from ear images failed to predict individual ear grain yield precisely. However, combined morphological parameters from wheat stem and wheat ear revealed the best result of regression with extension models. Composite morphological stem-and-ear level traits of individual wheat stem provided more accurate prediction on the ear-derived grain yield, which could make the yield prediction with growth-stage traits collected with machine vision technically possible. Machine vision tools of HTP provided a much higher sets of agronomic trait indices as compared with traditional methods, providing more options for the illustration on the correlations among agronomic traits and path-analysis on crop yield. It in turn resulted into high-dimensional data sets and technical difficulties impeding the identification on valuable information. 【Conclusion】A basic infrastructure of HTP machine vision tools for field wheat stand was defined as multi-scale and automatic adaptation aspect. It should be autonomously adaptable to multi-scales concerning with the field, crop stand, individual crop and organ-level traits of each individual crop. It also provided traits identification and calculation with statistical analysis on different physiological periods of wheat, e.g. seedling stage, tillering stage, jointing stage etc. Meanwhile, in each development stage of the machine-vision HTP tools and for each functional module, in-depth involvement of agronomical calibration was required. In safeguarding the reliability of machine-vision tools, standardization on referencing HTP-derived traits was also necessary. Keywords:machine vision;individual stem and ear;high-throughput analysis;trait indices;ear-level grain yield
PDF (1169KB)元数据多维度评价相关文章导出EndNote|Ris|Bibtex收藏本文 本文引用格式 丁启朔, 李海康, 孙克润, 何瑞银, 汪小旵, 刘富玺, 厉翔. 基于机器视觉的稻茬麦单茎穗高通量表型分析[J]. 中国农业科学, 2020, 53(1): 42-54 doi:10.3864/j.issn.0578-1752.2020.01.004 DING QiShuo, LI HaiKang, SUN KeRun, HE RuiYin, WANG XiaoChan, LIU FuXi, LI Xiang. High-Throughput Phenotyping of Individual Wheat Stem and Ear Traits with Machine Vision[J]. Scientia Acricultura Sinica, 2020, 53(1): 42-54 doi:10.3864/j.issn.0578-1752.2020.01.004
SEY:单穗籽粒产量;SLW:单叶片质量;SSW:单茎秆质量;SEW:单穗质量。下同 SEY: Single ear yield; SLW: Single leaf weight; SSW: Single stem weight; SEW: Single ear weight. The same as below
Table 2 表2 表2小麦单茎穗茎秆和麦穗形态参数与单穗籽粒产量回归模型 Table 2The regression model between stem & ear morphological parameters per stem-panicle and ear-derived grain yield of wheat
拟合模型 Fitting model
模型类型 Model type
模型方程 Model equation
麦穗形态参数与单穗籽粒产量 Ear morphological parameters and ear-derived grain yield
SEL:单穗长;SEAW:单穗平均宽度;SEA:单穗投影面积;SEC:单穗周长;SSL:单茎长;SSAW:单茎平均宽度;SSA:单茎投影面积;SSC:单茎周长。下同 SEL: Single ear length; SEAW: Average width of single ear; SEA: Single ear area; SEC: Single ear circumference; SSL: Single stem length; SSAW: Average width of single stem; SSA: Single stem area; SSC: Single stem circumference. The same as below
Table 4 表4 表4小麦单茎穗茎秆和麦穗形态参数与单穗籽粒产量的相关性 Table 4Correlations between stem and ear morphological parameters per stem-panicle and ear-derived grain yield of wheat
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