LAMINA: a tool for rapid quantification of leaf size and shape parameters
1 Research Group for Chemometrics, Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden
2 CNAP Artemisia Research Project, Centre for Novel Agricultural Products, Department of Biology, PO Box 373, York, YO10 5YW, UK
3 Department of Forest Sciences, 2424, Main Mall, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
4 Umeå Plant Science Centre, Department of Plant Physiology, Umeå University, SE-901 87 Umeå, Sweden
BMC Plant Biology 2008, 8:82 doi:10.1186/1471-2229-8-82Published: 22 July 2008
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.
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.
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.