Horgan Graham W, Song Yu, Glasbey Chris A, van der Heijden Gerie W A M, Polder Gerrit, Dieleman J Anja, Bink Marco C A M, van Eeuwijk Fred A
Biomathematics and Statistics Scotland, Rowett Institute of Nutrition and Health, Aberdeen, AB21 9SB, UK.
Biomathematics and Statistics Scotland, Kings Buildings, Edinburgh, EH9 3JZ, UK.
Funct Plant Biol. 2015 May;42(5):486-492. doi: 10.1071/FP14070.
High-throughput automated plant phenotyping has recently received a lot of attention. Leaf area is an important characteristic in understanding plant performance, but time-consuming and destructive to measure accurately. In this research, we describe a method to use a histogram of image intensities to automatically measure plant leaf area of tall pepper (Capsicum annuum L.) plants in the greenhouse. With a device equipped with several cameras, images of plants were recorded at 5-cm intervals over a height of 3m, at a recording distance of less than 60cm. The images were reduced to a small set of principal components that defined the design matrix in a regression model for predicting manually measured leaf area as obtained from destructive harvesting. These regression calibrations were performed for six different developmental times. In addition, development of leaf area was investigated by fitting linear relations between predicted leaf area and time, with special attention given to the genotype by time interaction and its genetic basis in the form of quantitative trait loci (QTLs). The experiment comprised parents, F1 progeny and eight genotypes of a recombinant inbred population of pepper. Although the current trial contained a limited number of genotypes, an earlier identified QTL related to leaf area growth could be confirmed. Therefore, image analysis, as presented in this paper, provides a powerful and efficient way to study and identify the genetic basis of growth and developmental processes in plants.
高通量自动化植物表型分析最近受到了广泛关注。叶面积是理解植物性能的一个重要特征，但准确测量既耗时又具有破坏性。在本研究中，我们描述了一种利用图像强度直方图自动测量温室中高杆辣椒（Capsicum annuum L.）植株叶面积的方法。使用配备多个摄像头的设备，在小于60厘米的记录距离下，以5厘米的间隔在3米的高度上记录植株图像。这些图像被简化为一小组主成分，这些主成分在回归模型中定义了设计矩阵，用于预测通过破坏性收获获得的人工测量叶面积。针对六个不同的发育阶段进行了这些回归校准。此外，通过拟合预测叶面积与时间之间的线性关系来研究叶面积的发育，特别关注基因型与时间的相互作用及其以数量性状位点（QTL）形式存在的遗传基础。该实验包括辣椒的亲本、F1后代和重组自交群体的八个基因型。尽管当前试验包含的基因型数量有限，但一个先前鉴定出的与叶面积生长相关的QTL得以证实。因此，本文所介绍的图像分析为研究和鉴定植物生长发育过程的遗传基础提供了一种强大而有效的方法。
