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

摘要/Abstract

摘要：

【目的】高通量表型技术不仅是现代育种领域的重要手段,也是解析田间作物生理生态行为的工具,但不同类别高通量表型技术的基础架构特征仍不清楚,因此需要针对机器视觉高通量表型技术进行专门探讨。【方法】本文用机器视觉技术检测计算稻茬麦茎穗一体的表型指标。使用宁麦13、鲁原502和郑麦9023 3个小麦品种,进行小区化对比试验,使用等孔距栅条精播板进行单粒精播,准确控制条播小麦的群体条件。于稻茬麦成熟期进行茎穗一体图像获取,对图像进行灰度增强、直方图均值化、S分量提取、Otsu阈值分割、茎穗分离和茎穗形态参数提取等操作。提取的稻茬麦地上部单茎穗各器官的形态参数包括茎秆长、茎秆平均宽度、茎秆投影面积、茎秆周长、麦穗长、麦穗平均宽度、麦穗投影面积和麦穗周长。同时,使用传统方法获取小麦单叶片质量、单茎秆质量、单穗质量和单穗籽粒产量等农艺性状指标。分别构建线性模型、二次模型、指数模型及拓展模型进行多维指标拟合,包括小麦单茎穗生物量与单穗籽粒产量关系、单茎穗的麦穗形态参数与单穗籽粒产量关系等拟合分析。在单茎穗层面对小麦茎穗的表型指标与单穗籽粒产量之间的关系进行相关分析和回归分析,进而基于机器视觉在小麦茎穗一体方面的个例应用,讨论大田高通量表型分析的机器视觉技术研发的要点。【结果】宁麦13、鲁原502和郑麦9023 3个小麦品种的单叶片质量与单穗籽粒产量的相关系数依次下降,小麦单茎穗形态参数与单穗籽粒产量的相关性显著低于生物量指标,但单穗投影面积、单穗长与单穗籽粒产量依然存在显著正相关。3个小麦品种在单茎穗的各生物量指标与单穗籽粒产量的最优回归模型各不相同,麦穗图像的形态参数不能准确反映单穗籽粒产量,但单茎穗的茎秆和麦穗形态参数的组合应用表现出最佳的拓展模型拟合结果。利用茎穗一体的数字图像处理所得的复合型形态参数可以准确预测单穗籽粒产量,从而表明利用机器视觉技术观测小麦的生长过程并实时预测产量的可行性。【结论】机器视觉技术能提供远高于常规农艺性状的高通量指标集,为解析各类农艺性状之间的联系及产量的通径分析提供更多的途径,但也造成高维指标集和有价值信息提取的技术困难。应用于田间小麦群体的机器视觉技术应具备多尺度智能化自适应的技术架构,同时应具备基于场景、群体、个体和器官的多空间尺度和苗期、分蘖期、拔节期等多生理时间尺度的统计性数字表型发现和计算能力,同时,机器视觉各技术研发环节和各技术模块都需要农艺学深度参与和校准,而配备标准表型数据库更是保障高通量技术实用性和可靠性的基础。

关键词: 机器视觉, 单茎穗, 高通量, 表型指标, 单穗籽粒产量

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

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.

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