Deep leaf: Mask R-CNN based leaf detection and segmentation from digitized herbarium specimen images

The generation of morphological traits of plants such as the leaf length, width, perimeter, area, and petiole length are fundamental features of herbarium specimens, thus providing high-quality data to investigate plant responses to ongoing climatic change and plant history evolution. However, the existing measurement methods are primarily associated with manual analysis, which is labor-intensive and inefficient. This paper proposes a deep learning-based approach, called Deep Leaf, for detecting and pixel-wise segmentation of leaves based on the improved state-of-the-art instance segmentation approach, Mask Region Convolutional Neural Network (Mask R-CNN). Deep Leaf can accurately detect each leaf in the herbarium specimen and measure the associated morphological traits. The experimental results indicate that our automated approach can segment the leaves of different families. Compared to manual measurement done by ecologists and botanist experts, the average relative error of leaf length is 4.6%, while the average relative error of leaf width is 5.7%.

@article{TRIKI202176,
title = {Deep leaf: Mask R-CNN based leaf detection and segmentation from digitized herbarium specimen images},
journal = {Pattern Recognition Letters},
volume = {150},
pages = {76-83},
year = {2021},
issn = {0167-8655},
doi = {https://doi.org/10.1016/j.patrec.2021.07.003},
url = {https://www.sciencedirect.com/science/article/pii/S0167865521002361},
author = {Abdelaziz Triki and Bassem Bouaziz and Jitendra Gaikwad and Walid Mahdi},
keywords = {Deep learning, Instance segmentation, Digitized herbarium specimen images (DHS), Plant leaves, Morphological traits},
abstract = {The generation of morphological traits of plants such as the leaf length, width, perimeter, area, and petiole length are fundamental features of herbarium specimens, thus providing high-quality data to investigate plant responses to ongoing climatic change and plant history evolution. However, the existing measurement methods are primarily associated with manual analysis, which is labor-intensive and inefficient. This paper proposes a deep learning-based approach, called Deep Leaf, for detecting and pixel-wise segmentation of leaves based on the improved state-of-the-art instance segmentation approach, Mask Region Convolutional Neural Network (Mask R-CNN). Deep Leaf can accurately detect each leaf in the herbarium specimen and measure the associated morphological traits. The experimental results indicate that our automated approach can segment the leaves of different families. Compared to manual measurement done by ecologist and botanist experts, the average relative error of leaf length is 4.6%, while the average relative error of leaf width is 5.7%.}
}