Research Article: Automating Digital Leaf Measurement: The Tooth, the Whole Tooth, and Nothing but the Tooth

Date Published: August 1, 2012

Publisher: Public Library of Science

Author(s): David P. A. Corney, H. Lilian Tang, Jonathan Y. Clark, Yin Hu, Jing Jin, Carles Lalueza-Fox.


Many species of plants produce leaves with distinct teeth around their margins. The presence and nature of these teeth can often help botanists to identify species. Moreover, it has long been known that more species native to colder regions have teeth than species native to warmer regions. It has therefore been suggested that fossilized remains of leaves can be used as a proxy for ancient climate reconstruction. Similar studies on living plants can help our understanding of the relationships. The required analysis of leaves typically involves considerable manual effort, which in practice limits the number of leaves that are analyzed, potentially reducing the power of the results. In this work, we describe a novel algorithm to automate the marginal tooth analysis of leaves found in digital images. We demonstrate our methods on a large set of images of whole herbarium specimens collected from Tilia trees (also known as lime, linden or basswood). We chose the genus Tilia as its constituent species have toothed leaves of varied size and shape. In a previous study we extracted leaves automatically from a set of images. Our new algorithm locates teeth on the margins of such leaves and extracts features such as each tooth’s area, perimeter and internal angles, as well as counting them. We evaluate an implementation of our algorithm’s performance against a manually analyzed subset of the images. We found that the algorithm achieves an accuracy of 85% for counting teeth and 75% for estimating tooth area. We also demonstrate that the automatically extracted features are sufficient to identify different species of Tilia using a simple linear discriminant analysis, and that the features relating to teeth are the most useful.

Partial Text

Characterizing the margin of leaves, including their teeth, is important for several areas of botanical research. These include modeling the climate and identifying species, both of which we discuss here.

We now describe our algorithm and two sets of experiments to evaluate it. We compare the algorithm’s estimates of tooth count and tooth area with manual estimates and then use the extracted characters to perform basic species identification.

In this work, we have demonstrated that it is possible to automatically locate and measure leaf margin teeth from images of herbarium specimens, extending our previous work in this area [13]. We have also shown that after identifying teeth, it is possible to automatically extract characters from them such as tooth size and shape. As far as we are aware, this is the first time such automation of the analysis of leaf margins from herbarium images has been demonstrated. We believe that the accuracy is sufficient to demonstrate the potential benefits of automation in tasks such as climate modeling and species identification.