Automating lichen monitoring in ecological studies using instance segmentation of time-lapse images ...

• Main Authors: Naimi, Safwen, Koubaa, Olfa, Bouachir, Wassim, Bilodeau, Guillaume-Alexandre, Jeddore, Gregory, Baines, Patricia, Correia, David, Arsenault, Andre
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2023
• Subjects: Computer Vision and Pattern Recognition cs.CV; Machine Learning cs.LG; FOS Computer and information sciences; Newfoundland
• Online Access: https://dx.doi.org/10.48550/arxiv.2310.17080; https://arxiv.org/abs/2310.17080

Lichens are symbiotic organisms composed of fungi, algae, and/or cyanobacteria that thrive in a variety of environments. They play important roles in carbon and nitrogen cycling, and contribute directly and indirectly to biodiversity. Ecologists typically monitor lichens by using them as indicators to assess air quality and habitat conditions. In particular, epiphytic lichens, which live on trees, are key markers of air quality and environmental health. A new method of monitoring epiphytic lichens involves using time-lapse cameras to gather images of lichen populations. These cameras are used by ecologists in Newfoundland and Labrador to subsequently analyze and manually segment the images to determine lichen thalli condition and change. These methods are time-consuming and susceptible to observer bias. In this work, we aim to automate the monitoring of lichens over extended periods and to estimate their biomass and condition to facilitate the task of ecologists. To accomplish this, our proposed framework ... : 6 pages, 3 Figures, 8 Tables, Accepted for publication in IEEE International Conference on Machine Learning and Applications (ICMLA), copyright IEEE ...