T. Haucke, Katja Herzog, Pierre Barré, Rebecca Höfle, R. Töpfer, V. Steinhage
{"title":"利用光分离和自动RGB图像分析改进葡萄果实表面的光学表型","authors":"T. Haucke, Katja Herzog, Pierre Barré, Rebecca Höfle, R. Töpfer, V. Steinhage","doi":"10.5073/VITIS.2021.60.1-10","DOIUrl":null,"url":null,"abstract":"Grape resilience towards Botrytis cinerea (B. cinerea) infections (Botrytis bunch rot) is an important concern of breeders and growers. Beside grape bunch architecture, berry surface characteristics like berry bloom (epicuticular wax) as well as thickness and permeability of the berry cuticle represent further promising physical barriers to increase resilience towards Botrytis bunch rot. In previous studies, two efficient sensor-based phenotyping methods were developed to evaluate both berry surface traits fast and objectively: (1) light-separated RGB (red-green-blue) image analysis to determine the distribution of epicuticular wax on the berry surface; and (2) electrical impedance characteristics of the grape berry cuticle based on point measurements. The present proof-of-concept study aiming at the evaluation of light-separated RGB images for both phenotyping applications, phenotyping wax distribution pattern and berry cuticle impedance values. Within the selected grapevine varieties like 'Riesling', 'Sauvignon Blanc' or 'Calardis Blanc' five contributions were achieved: (1) Both phenotyping approaches were fused into one prototypic unified phenotyping method achieving a wax detection accuracy of 98.6 % and a prediction of electrical impedance with an accuracy of 95 %. (2) Both traits are derived using only light-separated images of the grapevine berries. (3) The improved method allows the detection and quantification of additional surface traits of the grape berry surface such as lenticels (punctual lignification) and the berry stem that are also known as being able to affect the grape susceptibility towards Botrytis. (4) The improved image analysis tools are further integrated into a comprehensive workbench allowing end-users, like breeders to combine phenotyping experiments with transparent data management offering valuable services like visualizations, indexing, etc. (5) Annotation work is supported by a sophisticated annotation tool of the image analysis workbench. 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引用次数: 2
摘要
葡萄对葡萄葡萄枯萎病(Botrytis cinerea, B. cinerea)感染(Botrytis bunch rot)的抗逆性是育种者和种植者关注的重要问题。除了葡萄串结构外,浆果表面特征,如浆果开花(表皮蜡)以及浆果角质层的厚度和通透性,是提高抗葡萄腐病抗灾能力的另一个有希望的物理障碍。在先前的研究中,开发了两种高效的基于传感器的表型方法来快速客观地评估浆果表面性状:(1)分光RGB(红绿蓝)图像分析,确定浆果表面表皮蜡的分布;(2)基于点测的葡萄果实角质层电阻抗特性。目前的概念验证研究旨在评估光分离RGB图像的表型应用,表型蜡分布模式和浆果角质层阻抗值。在选定的葡萄品种中,如“雷司令”,“长相思”或“白葡萄酒”,实现了五个贡献:(1)两种表型方法融合为一个原型统一表型方法,实现蜡检测精度为98.6%,电阻抗预测精度为95%。(2)这两个性状都是仅利用葡萄莓的光分离图像得出的。(3)改进后的方法可以检测和量化葡萄果实表面的其他表面性状,如皮孔(准时木质化)和浆果茎,这些也被认为能够影响葡萄对葡萄孢菌的敏感性。(4)改进后的图像分析工具进一步集成到一个综合工作台中,允许育种者等最终用户将表型实验与透明的数据管理相结合,提供可视化、索引等有价值的服务。(5)注释工作由图像分析工作台的复杂注释工具支持。光分离图像的使用使不同光学浆果表面特征的快速和非侵入性表型分析成为可能,这节省了耗时的劳动,并且还允许浆果样品在后续研究中重复使用,例如葡萄孢菌感染研究。
Improved optical phenotyping of the grape berry surface using light-separation and automated RGB image analysis
Grape resilience towards Botrytis cinerea (B. cinerea) infections (Botrytis bunch rot) is an important concern of breeders and growers. Beside grape bunch architecture, berry surface characteristics like berry bloom (epicuticular wax) as well as thickness and permeability of the berry cuticle represent further promising physical barriers to increase resilience towards Botrytis bunch rot. In previous studies, two efficient sensor-based phenotyping methods were developed to evaluate both berry surface traits fast and objectively: (1) light-separated RGB (red-green-blue) image analysis to determine the distribution of epicuticular wax on the berry surface; and (2) electrical impedance characteristics of the grape berry cuticle based on point measurements. The present proof-of-concept study aiming at the evaluation of light-separated RGB images for both phenotyping applications, phenotyping wax distribution pattern and berry cuticle impedance values. Within the selected grapevine varieties like 'Riesling', 'Sauvignon Blanc' or 'Calardis Blanc' five contributions were achieved: (1) Both phenotyping approaches were fused into one prototypic unified phenotyping method achieving a wax detection accuracy of 98.6 % and a prediction of electrical impedance with an accuracy of 95 %. (2) Both traits are derived using only light-separated images of the grapevine berries. (3) The improved method allows the detection and quantification of additional surface traits of the grape berry surface such as lenticels (punctual lignification) and the berry stem that are also known as being able to affect the grape susceptibility towards Botrytis. (4) The improved image analysis tools are further integrated into a comprehensive workbench allowing end-users, like breeders to combine phenotyping experiments with transparent data management offering valuable services like visualizations, indexing, etc. (5) Annotation work is supported by a sophisticated annotation tool of the image analysis workbench. The usage of light-separated images enables fast and non-invasive phenotyping of different optical berry surface characteristics, which saves time-consuming labor and additionally allows the reuse of the berry samples for subsequent investigations, e.g. Botrytis infection studies.