LAMINA: a tool for rapid quantification of leaf size and shape parameters

Abstract

Background: An increased understanding of leaf area development is important in a number of fields: in food and non-food crops, for example short rotation forestry as a biofuels feedstock, leaf area is intricately linked to biomass productivity; in paleontology leaf shape characteristics are used to reconstruct paleoclimate history. Such fields require measurement of large collections of leaves, with resulting conclusions being highly influenced by the accuracy of the phenotypic measurement process.

Results: We have developed LAMINA (Leaf shApe deterMINAtion), a new tool for the automated analysis of images of leaves. LAMINA has been designed to provide classical indicators of leaf shape (blade dimensions) and size (area), which are typically required for correlation analysis to biomass productivity, as well as measures that indicate asymmetry in leaf shape, leaf serration traits, and measures of herbivory damage (missing leaf area). In order to allow Principal Component Analysis (PCA) to be performed, the location of a chosen number of equally spaced boundary coordinates can optionally be returned.

Conclusion: We demonstrate the use of the software on a set of 500 scanned images, each containing multiple leaves, collected from a common garden experiment containing 116 clones of Populus tremula (European trembling aspen) that are being used for association mapping, as well as examples of leaves from other species. We show that the software provides an efficient and accurate means of analysing leaf area in large datasets in an automated or semi-automated work flow.

Figure 1

Use of LAMINA to quantify leaf characteristics in the SwAsp collection. A Screenshot of LAMINA. B Example cropped image generated by LAMINA showing dimension measurements and serration detection. C Example cropped image generated by LAMINA. Cavities (holes) in the leaf lamina are marked in green, serrations are marked in blue and the depth of each serration is marked by a yellow line. Horizontal and vertical centre lines are drawn in red with sub-divisions marked in blue. Boundary coordinates are shown as white circles along the perimeter. D Regression analysis to compare data generated from ImageJ to LAMINA for a set of 50 random images. E Principal Component Analysis loadings plot of X and Y coordinates generated for the SwAsp dataset using LAMINA (50 boundary coordinates per leaf). The leaf in the centre is the value closest to the centre of the cloud and has been oriented to match the distribution of XY values in the loadings plot. Component one appears to represent leaf width (55 % variance) and component two leaf length (27 % variance).

Figure 2

Comparison of methods for quantifying leaf area in A. annua. A Comparison of leaf area quantification using a leaf area meter and LAMINA. B Comparison of leaf area data generated using ImageJ and LAMINA.

Figure 3

Example cropped images generated using LAMINA in a range of species. A Three example Artemisia annua leaves. Some regions are incorrectly identified as cavities, however the perimeter is correctly identified. B Example image from [21]. Serration detection pixel threshold = 50. C Example image from [23]. D Example Populus leaves from Umeå Plant Science Centre 2006 Calendar. E Example Image containing a range of leaves from common European tree species with contrasting leaf shapes. F Example use of serration detection to measure lobes in a senescing maple leaf. Serration detection pixel threshold = 75. G An example set of Arabidopsis thaliana leaves representing a developmental series. All images were analysed using the Greedy search threshold setting.