Holistic and component plant phenotyping using temporal image sequence.
Holistic and component plant phenotyping using temporal image sequence.
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DOI：
10.1186/s13007-018-0303-x
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发表时间：
2018
期刊：
影响因子：
5.1
通讯作者：
Awada T
中科院分区：
文献类型：
--
作者：
Das Choudhury S;Bashyam S;Qiu Y;Samal A;Awada T
基于图像的植物表型分析通过在相对较短的时间内分析大量的植物，有助于非侵入性地提取特征。它有可能通过将整个植物视为单一对象(整体表型)或单个组件(即叶和茎(组件表型))来计算高级表型，以研究植物的生物物理特性。玉米植株营养生长期的出苗时间、各时间点的叶片总数和个体叶片的生长情况是最能反映植株活力的重要表型表达。然而，基于图像的自动解决这一新问题的方法还有待探索。本文介绍了一组新的整体表型和成分表型。为了计算组成表型，检测单个叶和茎是必不可少的。因此，本文提出了一种新的方法，通过分析从侧面获取的二维可见光图像序列，利用基于图的方法来可靠地检测玉米植株的叶片和茎。对叶片的总数进行计数，并针对序列中的所有图像测量每片叶片的长度，以监控叶片的生长。为了评估算法的性能，我们引入了内布拉斯加州大学林肯分校的植物表型数据集(UNL-CPPD)，并提供了基本事实，以便于新算法的开发和统一比较。在UNL-CPPD上，玉米植株的组成表型受基因和环境(即温室)调控的时间变化得到了实验验证。统计模型被应用于温室环境影响的分析，并在公共数据集中展示了整体表型随时间变化的遗传规律，该数据集被称为Panicid Phenommap-1。本文的主要贡献是提出了一种新的基于计算机视觉的算法，用于自动检测单个叶片和茎，以计算新的成分表型，并公开发布基准数据集，即UNL-CPPD。通过详细的实验分析，论证了玉米整体型和组分表型受环境和遗传变异的时间变化，并讨论了它们在植物学背景下的意义。
Image-based plant phenotyping facilitates the extraction of traits noninvasively by analyzing large number of plants in a relatively short period of time. It has the potential to compute advanced phenotypes by considering the whole plant as a single object (holistic phenotypes) or as individual components, i.e., leaves and the stem (component phenotypes), to investigate the biophysical characteristics of the plants. The emergence timing, total number of leaves present at any point of time and the growth of individual leaves during vegetative stage life cycle of the maize plants are significant phenotypic expressions that best contribute to assess the plant vigor. However, image-based automated solution to this novel problem is yet to be explored. A set of new holistic and component phenotypes are introduced in this paper. To compute the component phenotypes, it is essential to detect the individual leaves and the stem. Thus, the paper introduces a novel method to reliably detect the leaves and the stem of the maize plants by analyzing 2-dimensional visible light image sequences captured from the side using a graph based approach. The total number of leaves are counted and the length of each leaf is measured for all images in the sequence to monitor leaf growth. To evaluate the performance of the proposed algorithm, we introduce University of Nebraska–Lincoln Component Plant Phenotyping Dataset (UNL-CPPD) and provide ground truth to facilitate new algorithm development and uniform comparison. The temporal variation of the component phenotypes regulated by genotypes and environment (i.e., greenhouse) are experimentally demonstrated for the maize plants on UNL-CPPD. Statistical models are applied to analyze the greenhouse environment impact and demonstrate the genetic regulation of the temporal variation of the holistic phenotypes on the public dataset called Panicoid Phenomap-1. The central contribution of the paper is a novel computer vision based algorithm for automated detection of individual leaves and the stem to compute new component phenotypes along with a public release of a benchmark dataset, i.e., UNL-CPPD. Detailed experimental analyses are performed to demonstrate the temporal variation of the holistic and component phenotypes in maize regulated by environment and genetic variation with a discussion on their significance in the context of plant science.
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影响因子：
5.1
作者：
Müller-Linow M;Pinto-Espinosa F;Scharr H;Rascher U
通讯作者：
Rascher U
影响因子：
5.1
作者：
Minervini, Massimo;Abdelsamea, Mohammed M.;Tsaftaris, Sotirios A.
通讯作者：
Tsaftaris, Sotirios A.
影响因子：
3.3
作者：
Scharr, Hanno;Minervini, Massimo;Tsaftaris, Sotirios A.
通讯作者：
Tsaftaris, Sotirios A.
影响因子：
1.7
作者：
Varma, Varun;Osuri, Anand M.
通讯作者：
Osuri, Anand M.
HTPHENO：高通量植物表型的图像分析管道。
影响因子：
3
作者：
Hartmann A;Czauderna T;Hoffmann R;Stein N;Schreiber F
通讯作者：
Schreiber F