基于机器视觉的稻茬麦单茎穗高通量表型分析

doi:10.3864/j.issn.0578-1752.2020.01.004

Abstract

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.

Key words: machine vision, individual stem and ear, high-throughput analysis, trait indices, ear-level grain yield

QiShuo DING,HaiKang LI,KeRun SUN,RuiYin HE,XiaoChan WANG,FuXi LIU,Xiang LI. High-Throughput Phenotyping of Individual Wheat Stem and Ear Traits with Machine Vision[J].Scientia Agricultura Sinica, 2020, 53(1): 42-54.

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拟合模型 Fitting model	模型类型 Model type	模型方程 Model equation
单穗质量与单穗籽粒产量 Ear-derived weight and grain yield	线性Linear	SEY=a₀+a₁×SEW
	二次Quadratic	SEY=a₀+a₁×SEW²
	指数Exponential	ln(SEY)=a₀+a₁×ln(SEW)²
地上部各器官生物量与单穗籽粒产量 Biomass of different organs and ear-derived grain yield	线性Linear	SEY=a₀+a₁×SLW+a₂×SSW+a₃×SEW
	二次Quadratic	SEY=a₀+a₁×SLW²+a₂×SSW²+a₃×SEW²
	拓展Extended	SEY=a₀+a₁×SLW²+a₂×SSW²+a₃×SEW²+a₄×SLW×SSW+a₅×SLW×SEW+a₆×SSW×SEW
	指数Exponential	ln(SEY)=a₀+a₁×ln(SLW)²+a₂×ln(SSW)²+a₃×ln(SEW)²

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	线性Linear	SEY=a₀+a₁×SEL+a₂×SEAW+a₃×SEA+a₄×SEC
	二次Quadratic	SEY=a₀+a₁×SEL²+a₂×SEAW²+a₃×SEA²+a₄×SEC²
	拓展Extended	SEY=a₀+a₁×SEL²+a₂×SEAW²+a₃×SEA²+a₄×SEC²+a₅×SEL×SEAW+a₆×SEL×SEA+a₇×SEL×SEC+...
	指数Exponential	ln(SEY)=a₀+a₁×ln(SEL)²+a₂×ln(SEAW)²+a₃×ln(SEA)²+a₄×ln(SEC)²
茎秆和麦穗形态参数与单穗籽粒产量 Stem and ear morphological parameters and ear-derived grain yield	线性Linear	SEY=a₀+a₁×SEL+a₂×SEAW+a₃×SEA+a₄×SEC+a₅×SSL+a₆×SSAW+a₇×SSA+a₈×SSC
	二次Quadratic	SEY=a₀+a₁×SEL²+a₂×SEAW²+a₃×SEA²+a₄×SEC²+a₅×SSL²+a₆×SSAW²+a₇×SSA²+a₈×SSC²
	拓展Extended	SEY=a₀+a₁×SEL²+a₂×SEAW²+a₃×SEA²+a₄×SEC²+a₅×SSL²+a₆×SSAW²+a₇×SSA²+a₈×SSC²+a₉× SEL×SEAW+...
	指数Exponential	ln(SEY)=a₀+a₁×ln(SEL)²+a₂×ln(SEAW)²+a₃×ln(SEA)²+a₄×ln(SEC)²+a₅×ln(SSL)²+a₆×ln(SSAW)²+a₇× ln(SSA)²+a₈×ln(SSC)²

品种 Variety	单叶片质量SLW (g)	单茎秆质量SSW (g)	单穗质量SEW (g)
宁麦13 Ningmai 13	0.719**	0.765**	0.950**
鲁原502 Luyuan 502	0.510**	0.565**	0.927**
郑麦9023 Zhengmai 9023	0.475**	0.529**	0.919**
指数Exponential	ln(SEY)=a₀+a₁×ln(SLW)²+a₂×ln(SSW)²+a₃×ln(SEW)²

品种 Variety	单茎投影面积SSA (mm²)	单茎周长 SSC (mm)	单茎长 SSL (mm)	单茎平均宽度SSAW (mm)	单穗投影面积SEA (mm²)	单穗周长 SEC (mm)	单穗长 SEL (mm)	单穗平均宽度SEAW (mm)
宁麦13 Ningmai 13	0.090	0.058	0.116	0.112	0.505**	0.478**	0.496**	0.336*
鲁原502 Luyuan 502	-0.052	0.133	0.173	-0.186	0.554**	0.396**	0.649**	0.209
郑麦9023 Zhengmai 9023	0.184	0.224	0.383**	0.114	0.393**	0.320*	0.383**	0.285*

品种 Variety	模型 Model	系数Coefficient		决定系数 R²	F值 F value	P值 P value	误差方差估计 MSp
品种 Variety	模型 Model	a₀	a₁	决定系数 R²	F值 F value	P值 P value	误差方差估计 MSp
宁麦13 Ningmai 13	线性Linear	-0.061	0.783	0.902	443.105	0.000	0.009
宁麦13 Ningmai 13	二次Quadratic	0.459	0.275	0.914	248.683	0.000	0.008
	指数Exponential	3.183	0.071	0.882	357.326	0.000	0.010
鲁原502 Luyuan 502	线性Linear	0.002	0.673	0.859	293.457	0.000	0.023
鲁原502 Luyuan 502	二次Quadratic	0.584	0.168	0.862	146.316	0.000	0.023
	指数Exponential	3.186	0.069	0.670	97.358	0.000	0.060
郑麦9023 Zhengmai 9023	线性Linear	-0.099	0.695	0.844	259.356	0.000	0.036
郑麦9023 Zhengmai 9023	二次Quadratic	0.454	0.191	0.858	290.450	0.000	0.033
	指数Exponential	2.921	0.073	0.744	139.620	0.000	0.059

High-Throughput Phenotyping of Individual Wheat Stem and Ear Traits with Machine Vision

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品种 Variety	模型 Model	系数Coefficient					决定系R²	F值 F value	P值 P value	误差方差估计 Error variance estimation
品种 Variety	模型 Model	a₀	a₁	a₂	a₃	…	决定系R²	F值 F value	P值 P value	误差方差估计 Error variance estimation
宁麦13 Ningmai 13	线性Linear	-0.065	-0.046	0.120	0.778		0.903	141.909	0.000	0.009
宁麦13 Ningmai 13	二次Quadratic	0.452	0.137	0.038	0.263	…	0.918	80.005	0.000	0.008
	拓展Extended	0.452	1.915	-4.978	0.099	…	0.925	54.835	0.000	0.008
	指数Exponential	3.182	0.002	-0.003	0.072	…	0.885	117.718	0.000	0.011
鲁原502 Luyuan 502	线性Linear	-0.045	0.049	0.092	0.654		0.861	94.656	0.000	0.024
鲁原502 Luyuan 502	二次Quadratic	0.511	-0.243	0.361	0.159	…	0.840	80.046	0.000	0.027
	拓展Extended	0.518	-0.727	-2.887	0.021	…	0.856	42.714	0.000	0.027
	指数Exponential	2.924	0.011	0.004	0.064	…	0.681	2.788	0.000	0.061
郑麦9023 Zhengmai 9023	线性Linear	0.059	-0.032	-0.446	0.792		0.863	96.222	0.000	0.033
	二次Quadratic	0.512	-0.025	-0.204	0.207	…	0.867	100.115	0.000	0.032
	拓展Extended	0.502	2.883	1.752	0.169	…	0.872	48.618	0.000	0.033
	指数Exponential	3.242	0.003	-0.021	0.081	…	0.788	56.975	0.000	0.054

品种 Variety	模型 Model	系数Coefficient					决定系数R²	F值 F value	P值 P value	误差方差估计 Error variance estimation
品种 Variety	模型 Model	a₀	a₁	a₂	a₃	…	决定系数R²	F值 F value	P值 P value	误差方差估计 Error variance estimation
宁麦13 Ningmai 13	线性Linear	-0.067	0.014	0.011	0.000	…	0.337	7.796	0.000	0.063
宁麦13 Ningmai 13	二次Quadratic	0.566	0.000	0.000	0.000	…	0.340	3.695	0.005	0.067
	拓展Extended	0.315	-0.001	-0.017	0.000	…	0.434	3.408	0.003	0.062
	指数Exponential	4.788	0.165	0.109	-0.023	…	0.281	5.979	0.002	0.069
鲁原502 Luyuan 502	线性Linear	0.080	0.016	-0.021	0.000	…	0.447	12.399	0.000	0.096
	二次Quadratic	0.620	0.000	-0.001	0.000	…	0.496	11.049	0.000	0.090
	拓展Extended	0.468	0.000	-0.013	0.000	…	0.618	6.298	0.000	0.078
	指数Exponential	4.800	0.128	-0.021	0.032	…	0.416	10.920	0.000	0.102
郑麦9023 Zhengmai 9023	线性Linear	1.892	0.006	-0.152	0.003	…	0.235	4.107	0.012	0.201
	二次Quadratic	0.713	0.000	-0.001	0.000	…	0.243	3.131	0.025	0.204
	拓展Extended	3.413	0.005	-0.023	0.000	…	0.413	2.322	0.034	0.187
	指数Exponential	5.160	0.259	-0.016	0.034	…	0.283	3.850	0.009	0.193

品种 Variety	模型 Model	系数Coefficient					决定系数R²	F值 F value	P值 P value	误差方差估计 Error variance estimation
品种 Variety	模型 Model	a₀	a₁	a₂	a₃	…	决定系数R²	F值 F value	P值 P value	误差方差估计 Error variance estimation
宁麦13	线性Linear	-1.080	0.023	0.016	0.000	…	0.344	3.757	0.004	0.067
Mingmai 13	二次Quadratic	0.065	0.000	0.000	0.000	…	0.421	2.240	0.030	0.069
	拓展Extended	0.862	0.000	-0.098	0.000	…	0.829	1.468	0.181	0.071
	指数Exponential	3.113	-0.011	0.091	0.029	…	0.328	3.492	0.007	0.069
鲁原502 Luyuan 502	线性Linear	-0.815	0.015	-0.018	0.000	…	0.612	8.075	0.000	0.076
	二次Quadratic	0.140	0.000	0.000	0.000	…	0.617	8.271	0.000	0.075
	拓展Extended	0.299	-0.005	0.066	0.000	…	0.935	6.669	0.000	0.040
	指数Exponential	-0.090	0.142	0.028	0.033	…	0.544	8.565	0.000	0.085
郑麦9023 Zhengmai 9023	线性Linear	-1.253	0.010	0.085	0.002	…	0.303	2.678	0.029	0.198
	二次Quadratic	-0.228	0.000	0.000	0.000	…	0.296	1.836	0.103	0.212
	拓展Extended	34.07	0.030	-0.838	0.000	…	0.897	1.686	0.243	0.155
	指数Exponential	0.812	0.314	0.185	-0.043	…	0.410	3.003	0.011	0.177