Grape Phenome High-throughput Acquisition and Analysis Methods：A Review

doi:10.16420/j.issn.0513-353x.2021-0243

Acta Horticulturae Sinica ›› 2022, Vol. 49 ›› Issue (8): 1815-1832.doi: 10.16420/j.issn.0513-353x.2021-0243

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Grape Phenome High-throughput Acquisition and Analysis Methods：A Review

WANG Yongjian¹, KONG Junhua¹, FAN Peige¹, LIANG Zhenchang¹, JIN Xiuliang², LIU Buchun³, DAI Zhanwu¹^,^*()

1. Beijing Key Laboratory of Grape Sciences and Enology,CAS Key Laboratory of Plant Resources,Institute of Botany,Chinese Academy of Sciences,Beijing 100093,China
2. Institute of Crop Sciences,Chinese Academy of Agricultural Sciences,Beijing 100081,China
3. Institute of Environment and Sustainable Development in Agriculture,Chinese Academy of Agricultural Sciences,Beijing 100081,China

Received:2021-06-10 Revised:2022-01-29 Online:2022-08-25 Published:2022-09-05
Contact: DAI Zhanwu E-mail:zhanwu.dai@ibcas.ac.cn

Abstract

Abstract:

Phenomics is a research hotspot in the postgenomic era,and needs to be supported by high-throughput phenotypic acquisition and data processing methods. Here we summarized the recent advances in phenotypic high-throughput acquisition and data analysis methods,especially on grapevine （Vitis vinifera L.）. Main reviewed methods include image,optical spectrum and point clouds measurements,three-dimensional reconstruction and machine learning analyzing methods etc. These methods can be divided into two main classes based on their objectives：1）construction of smart orchard for precision viticulture and 2）precision phenotypic analysis for grape breeding. Moreover,the phenome studies of grapevine were reviewed from different scales ranging from field scale,individual plant,to individual organs,and from different traits：yield,quality and growth status. The future applications of the dynamic phenotyping and three-dimensional morphological phenotypes,and their integration with functional-structure plant models in viticulture and grapevine breeding were also discussed.

Key words: grape, phonemics, high-throughput, smart orchard, breeding

CLC Number:

S663.1

WANG Yongjian, KONG Junhua, FAN Peige, LIANG Zhenchang, JIN Xiuliang, LIU Buchun, DAI Zhanwu. Grape Phenome High-throughput Acquisition and Analysis Methods：A Review[J]. Acta Horticulturae Sinica, 2022, 49(8): 1815-1832.

Figures/Tables 8

References 124

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传感器类型 Sensor type	尺度 Scale	表型指标 Phenotype trait	数据获取环境 Data acquisition environment	优势 Advantage	劣势 Disadvantage	参考文献 Reference
RGB相机 RGB camera	冠层,植株,器官 Canopy,plant,organ	投影面积,器官数量,形态结构,颜色纹理 Project area,organ quantity,morphological structure,color texture	可控环境,田间 Controlled environment,field	成本低;分辨率高;集成方便;适用于不同尺度 Cost-effective;high-resolution;easy integration;suitable for multi-scales	只能获取可见光波段;图像质量受环境影响;仅能获取相对值;二维图像 Only visible wavelengths can be captured;environmental impact on image quality;only get the relative value;2D image	Aquino et al.,2018a,2018b;Tello & Forneck,2018;Victorino,2019;Coviello et al.,2020;Hacking et al.,2020
光谱相机 Spectral Camera	冠层,植株,器官 Canopy,plant,organ	投影面积,器官识别,形态结构,颜色,纹理 Project area,organ identification,morphological structure,color,texture	可控环境,田间 Controlled environment,field	可获取非可见光波段图像;集成方便;适用于不同尺度 Obtain images in non-visible band;easy integration;suitable for multi-scales	成本较高;图像分辨率低于RGB相机;受环境影响;获取过程需要多次校正 High cost;image resolution is lower than RGB camera;affected by the environment;the acquisition process requires frequently calibrated	Rodríguez-Pulido et al.,2013;di Gennaro et al.,2019;王浩淼等,2021
深度相机 Depth camera	冠层 Canopy	植株分割,器官识别 Plant segmentation,organ identify	可控环境,田间 Controlled environment,field	三维成像;成本可控;集成方便 3D image;cost effective;easy integration	分辨率较低;受环境影响;仅能获取相对值 Low resolution;affected by the environments;only get the relative value	Lopes et al.,2017;Milella et al.,2019;Marani et al.,2021
三维扫描仪 3D scanner	冠层,器官 Canopy,organ	三维结构,空间分布 3D architecture,spatial distribution	可控环境,田间 Controlled environment,field	三维点云信息;绝对距离;精度高;受环境影响低 3D point clouds;Absolute distance;High-precision;environment-stable	成本较高;原始数据体量较大;数据获取及处理耗时长 High cost;large raw data;time consuming for data acquisition and processing	Schöler & Steinhage,2015;Tello et al.,2016; Mack et al.,2017;Rist et al.,2019
X射线断层扫描 X-ray tomography	器官 Organ	内部密度差异结构,三维结构 Internal density differential structure,3D architecture	可控环境 Controlled environment	三维内部结构信息;绝对距离;精度高 Internal 3D information;absolute distance;high precision	成本极高;原始数据体量大;数据获取及处理耗时长;样品预处理复杂 Very high cost;large raw data;time consuming for data acquisition and processing;complicated sample pretreatment.	Li et al.,2019

传感器类型 Sensor type	尺度 Scale	表型指标 Phenotype trait	数据获取环境 Data acquisition environment	优势 Advantage	劣势 Disadvantage	参考文献 Reference
RGB相机 RGB camera	冠层,植株,器官 Canopy,plant,organ	投影面积,器官数量,形态结构,颜色纹理 Project area,organ quantity,morphological structure,color texture	可控环境,田间 Controlled environment,field	成本低;分辨率高;集成方便;适用于不同尺度 Cost-effective;high-resolution;easy integration;suitable for multi-scales	只能获取可见光波段;图像质量受环境影响;仅能获取相对值;二维图像 Only visible wavelengths can be captured;environmental impact on image quality;only get the relative value;2D image	Aquino et al.,2018a,2018b;Tello & Forneck,2018;Victorino,2019;Coviello et al.,2020;Hacking et al.,2020
光谱相机 Spectral Camera	冠层,植株,器官 Canopy,plant,organ	投影面积,器官识别,形态结构,颜色,纹理 Project area,organ identification,morphological structure,color,texture	可控环境,田间 Controlled environment,field	可获取非可见光波段图像;集成方便;适用于不同尺度 Obtain images in non-visible band;easy integration;suitable for multi-scales	成本较高;图像分辨率低于RGB相机;受环境影响;获取过程需要多次校正 High cost;image resolution is lower than RGB camera;affected by the environment;the acquisition process requires frequently calibrated	Rodríguez-Pulido et al.,2013;di Gennaro et al.,2019;王浩淼等,2021
深度相机 Depth camera	冠层 Canopy	植株分割,器官识别 Plant segmentation,organ identify	可控环境,田间 Controlled environment,field	三维成像;成本可控;集成方便 3D image;cost effective;easy integration	分辨率较低;受环境影响;仅能获取相对值 Low resolution;affected by the environments;only get the relative value	Lopes et al.,2017;Milella et al.,2019;Marani et al.,2021
三维扫描仪 3D scanner	冠层,器官 Canopy,organ	三维结构,空间分布 3D architecture,spatial distribution	可控环境,田间 Controlled environment,field	三维点云信息;绝对距离;精度高;受环境影响低 3D point clouds;Absolute distance;High-precision;environment-stable	成本较高;原始数据体量较大;数据获取及处理耗时长 High cost;large raw data;time consuming for data acquisition and processing	Schöler & Steinhage,2015;Tello et al.,2016; Mack et al.,2017;Rist et al.,2019
X射线断层扫描 X-ray tomography	器官 Organ	内部密度差异结构,三维结构 Internal density differential structure,3D architecture	可控环境 Controlled environment	三维内部结构信息;绝对距离;精度高 Internal 3D information;absolute distance;high precision	成本极高;原始数据体量大;数据获取及处理耗时长;样品预处理复杂 Very high cost;large raw data;time consuming for data acquisition and processing;complicated sample pretreatment.	Li et al.,2019

表型提取方法 Phenotyping extraction method	原始数据类型 Raw data type	表型指标 Phenotyping trait	分析平台 Analysis platform	参考文献 Reference
图像分割 Image segmentation	RGB图像 RGB Image	LAI、果穗数、芽数、修剪量、孔隙度、冠层投影面积、果穗紧实度、果粒数、颜色纹理、花粉数 LAI,cluster quantity,bud quantity,pruning weight,porosity,canopy projection area,cluster compactness,berry quantity,color texture,pollen quantity	Matlab;ImageJ;OpenCV	Aquino et al.,2018b;Victorino et al.,2019;Hacking et al.,2020;Schneider et al.,2020
机器学习 Machine learning	RGB图像 RGB image	器官识别、叶片病害识别 Organ identification,leaf disease identification	AlexNet;MobileNets; ShuffleNet V1;ShuffleNet V2	Botterill et al.,2017;Coviello et al.,2020;Tang et al.,2020
光谱分析 Spectrum analysis	光谱图像 Spectral image	叶片氮含量、病虫害、种子质量 Leaf nitrogen content,pests and diseases,seed quality	Matlab;ImageJ;OpenCV…	Rodríguez-Pulido et al.,2013 di Gennaro et al.,2019;
三维重建 3D reconstruction	RGB图像 RGB image	冠层体积、果穗体积、果穗三维形态、分支结构 Canopy volume,cluster volume,cluster 3D architecture,branch architecture	Agisoft Photoscan; structure-from-motion （SFM）	Kicherer et al.,2017;Schneider et al.,2020
点云处理 Point cloud processing	点云数据 Point cloud data	果穗三维空间结构、果粒数、果穗紧实度 3D cluster spatial architecture,berry quantity,cluster compactness	CloudCompare;Artec Studio;Geomagic Studio;...	Schöler & Steinhage,2015;Mack et al.,2017;Rist et al.,2019

表型提取方法 Phenotyping extraction method	原始数据类型 Raw data type	表型指标 Phenotyping trait	分析平台 Analysis platform	参考文献 Reference
图像分割 Image segmentation	RGB图像 RGB Image	LAI、果穗数、芽数、修剪量、孔隙度、冠层投影面积、果穗紧实度、果粒数、颜色纹理、花粉数 LAI,cluster quantity,bud quantity,pruning weight,porosity,canopy projection area,cluster compactness,berry quantity,color texture,pollen quantity	Matlab;ImageJ;OpenCV	Aquino et al.,2018b;Victorino et al.,2019;Hacking et al.,2020;Schneider et al.,2020
机器学习 Machine learning	RGB图像 RGB image	器官识别、叶片病害识别 Organ identification,leaf disease identification	AlexNet;MobileNets; ShuffleNet V1;ShuffleNet V2	Botterill et al.,2017;Coviello et al.,2020;Tang et al.,2020
光谱分析 Spectrum analysis	光谱图像 Spectral image	叶片氮含量、病虫害、种子质量 Leaf nitrogen content,pests and diseases,seed quality	Matlab;ImageJ;OpenCV…	Rodríguez-Pulido et al.,2013 di Gennaro et al.,2019;
三维重建 3D reconstruction	RGB图像 RGB image	冠层体积、果穗体积、果穗三维形态、分支结构 Canopy volume,cluster volume,cluster 3D architecture,branch architecture	Agisoft Photoscan; structure-from-motion （SFM）	Kicherer et al.,2017;Schneider et al.,2020
点云处理 Point cloud processing	点云数据 Point cloud data	果穗三维空间结构、果粒数、果穗紧实度 3D cluster spatial architecture,berry quantity,cluster compactness	CloudCompare;Artec Studio;Geomagic Studio;...	Schöler & Steinhage,2015;Mack et al.,2017;Rist et al.,2019

Grape Phenome High-throughput Acquisition and Analysis Methods：A Review

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