Digital Image Correlation for Full-Field High Resolution Assessment of Leaf Growth

Changes in shape and size of the leaves are driven by several transient growth parameters. Being able to assess resulting changes at a high temporal and spatial resolution is a necessary tool for studying biochemical principles of leaf development, and for construction of leaf growth models. In this short communication, a technique based on the use of 2D digital image correlation (DIC) is presented to track leaf growth. A speckle pattern with orange fluorescent paint was applied on three leaves of the sycamore maple (Acer pseudoplatanus) at an arbitrary point in growth. For ten consecutive days, images of the growing leaves were analyzed with DIC, revealing leaf growth patterns. The patterns were similar for three leaves, and results corresponded both with literature data of leaf growth and with leaf area measurements using manual segmentation of leaf images. We demonstrate that DIC can be applied for tracking leaf growth. Based on the results, a detailed macro-model for leaf growth can be developed or environmental effects on leaf growth can be assessed, amongst other possible applications.