Black Spot: a platform for automated and rapid estimation of leaf area from scanned images
Leaf area and its derivatives (e.g. specific leaf area) are widely used in ecological assessments, especially in the fields of plant–animal interactions, plant community assembly, ecosystem functioning and global change. Estimating leaf area is highly time-consuming, even when using specialized software to process scanned leaf images, because manual inputs are invariably required for scale detection and leaf surface digitisation. We introduce Black Spot Leaf Area Calculator (hereafter, Black Spot), a technique and stand-alone software package for rapid and automated leaf area assessment from images of leaves taken with standard flatbed scanners. Black Spot operates on comprehensive rule-sets for colour band ratios to carry out pixel-based classification which isolates leaf surfaces from the image background. Importantly, the software extracts information from associated image meta-data to detect image scale, thereby eliminating the need for time-consuming manual scale calibration. Black Spot’s output provides the user with estimates of leaf area as well as classified images for error checking. We tested this method and software combination on a set of 100 leaves of 51 different plant species collected from the field. Leaf area estimates generated using Black Spot and by manual processing of the images using an image editing software generated statistically identical results. Mean error rate in leaf area estimates from Black Spot relative to manual processing was −0.4 % (SD = 0.76). The key advantage of Black Spot is the ability to rapidly batch process multi-species datasets with minimal user effort and at low cost, thus making it a valuable tool for field ecologists.
KeywordsLeaf area Software Pixel-based classification Batch process Functional traits Multi-species datasets
We acknowledge Yadugiri VT, Chengappa SK, Vijay Kumar S, Siddharth Iyengar, Rutuja Dhamale, Priyanka Runwal, Atul Joshi and Harinandan PV for volunteering time to collect field samples, manually process images and test out versions of the software. We are grateful to Mahesh Sankaran, Jayashree Ratnam and Fiona Savory for several useful discussions. We are grateful to two anonymous reviewers for suggestions that have improved the quality of this manuscript. We thank the Rufford Small Grants Foundation and National Centre for Biological Sciences (NCBS) for funding fellowships, fieldwork and equipment. We acknowledge NCBS, and in particular Prasanta Baruah, for helping create the Black Spot web page and user support systems.
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