Towards standardizing automated image analysis with artificial intelligence for biodiversity
Zhou, P.; Bu, Y.-X.; Fu, G.-Y.; Wang, C.-S.; Xu, X.-W.; Pan, X. (2024). Towards standardizing automated image analysis with artificial intelligence for biodiversity. Front. Mar. Sci. 11: 1349705. https://dx.doi.org/10.3389/fmars.2024.1349705
In: Frontiers in Marine Science. Frontiers Media: Lausanne. e-ISSN 2296-7745, more
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| Authors | | Top |
- Zhou, P.
- Bu, Y.-X.
- Fu, G.-Y.
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- Wang, C.-S.
- Xu, X.-W.
- Pan, X.
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| Abstract |
Marine biodiversity, which refers to the variety of life in the oceans and seas, plays a key role through ecosystem services. These services have diverse ecological functions and provide economic wealth and resources, including products from fisheries and aquaculture, active ingredients for pharmaceuticals, and contribute to cultural well-being (Goulletquer et al., 2014). The climate change and human activities, such as maritime transport, waste deposition and resource exploitation, could affect marine biodiversity. To understand and conserve the marine biodiversity, the international cooperation projects, such as Census of Marine Life (http://coml.org/), have been carried out to investigate the marine biodiversity and huge amounts of images (still images and videos) of specimens and their habitats have been collected. A few easy-to-access repositories have been established to store and manage the image data as well as the associated information, such as the Ocean Biogeographic Information System (OBIS, https://obis.org). OBIS integrates biological, physical, and chemical oceanographic data and focuses on geo-referenced marine biodiversity. To assess the environmental impact, surveys are needed to measure species richness and relative abundance. Compared to traditional survey methods, such as trawling, image-based underwater observations are less invasive to the environments and could achieve better spatial coverage within the monitored ecosystems. To provide statistical data for assessment of the variability, it is necessary to perform image analysis, such as recognition of the biological individuals from the acquired images, which is based on the morphological features. The analysis of the obtained images can be conducted with reference to the accumulating photographic atlas and handbooks on taxa (Desbruyères et al., 2006; Tilot, 2006; Simon-Lledó et al., 2019; Xu et al., 2020). However, manually analyzing many images based on morphological characteristics, such as image annotation and classification, is labor-intensive and time-consuming. Additionally, the expertise of domain knowledge is required, but the number of experts is limited. For these reasons, artificial intelligence (AI), especially deep learning (DL) techniques, recently have been applied to automated image analysis, including detection, recognition, and objective tracking, from megafauna to plankton and microalgae (Cheng et al., 2019; Zhuang et al., 2021; Zhou et al., 2023). Previously, what the AI-tools that can be used for analysis of marine field imagery, such as the kinds of information extracted, has been well reviewed (Belcher et al., 2023). The developed AI-based tools improved the efficiency compared with traditional manual strategies and were used for practical applications, such as automated annotation of marine life imagery (Zhang et al., 2023), and detection of megabenthic fauna in optical images from the Clarion-Clipperton Zone (CCZ) in the Central Pacific, where intensive mining exploration are undergoing (Mbani et al., 2023). The analysis results may be used as general baseline data to assess the impacts of post-implementation of an activity. To build and use a machine learning-based pipeline, researchers usually need define an analytical task, construct the training and test datasets of the task, select and train the model, evaluate and improve the model performance, and apply to new data of the task (Belcher et al., 2023). Standardizations in these steps could improve the efficiency, reproducibility, and reliability in building AI-based tools. |
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