AI-based seagrass morphology measurement

Abstract

Seagrass meadows are an essential part of the Great Barrier Reef ecosystem, providing various benefits such as filtering nutrients and sediment, serving as a nursery for fish and shellfish, and capturing atmospheric carbon as blue carbon. Understanding the phenotypic plasticity of seagrasses and their ability to acclimate their morphology in response to environ-mental stressors is crucial. Investigating these morphological changes can provide valuable insights into ecosystem health and inform conservation strategies aimed at mitigating seagrass decline. Measuring seagrass growth by measuring morphological parameters such as the length and width of leaves, rhizomes, and roots is essential. The manual process of measuring morphological parameters of seagrass can be time-consuming, inaccurate and costly, so researchers are exploring machine-learning techniques to automate the process. To automate this process, researchers have developed a machine learning model that utilizes image processing and artificial intelligence to measure morphological parameters from digital imagery. The study uses a deep learning model called YOLO-v6 to classify three distinct seagrass object types and determine their dimensions. The results suggest that the proposed model is highly effective, with an average recall of 97.5%, an average precision of 83.7%, and an average f1 score of 90.1%. The model code has been made publicly available on GitHub (https://github.com/sajalhalder/AI-ASMM).

Keywords: Deep learning; Image classification; Morphometrics; Object detection; Seagrass; Zostera muelleri.