Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review DOI Creative Commons
Tymoteusz Miller, Grzegorz Michoński, Irmina Durlik

et al.

Biology, Journal Year: 2025, Volume and Issue: 14(5), P. 520 - 520

Published: May 8, 2025

Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, conservation planning. This systematic review follows the PRISMA framework to analyze AI applications freshwater studies. Using structured literature search across Scopus, Web of Science, Google Scholar, we identified 312 relevant studies published between 2010 2024. categorizes into assessment, ecological risk evaluation, strategies. A bias assessment was conducted using QUADAS-2 RoB 2 frameworks, highlighting methodological challenges, such measurement inconsistencies model validation. The citation trends demonstrate exponential growth AI-driven with leading contributions from China, United States, India. Despite growing use this field, also reveals several persistent including limited data availability, regional imbalances, concerns related generalizability transparency. Our findings underscore AI’s potential revolutionizing but emphasize need for standardized methodologies, improved integration, interdisciplinary collaboration enhance insights efforts.

Language: Английский

A fish counting model based on pyramid vision transformer with multi-scale feature enhancement DOI Creative Commons

Jiaming Xin,

Yiying Wang, Dashe Li

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103025 - 103025

Published: Jan. 1, 2025

Language: Английский

Citations

1
Advancing Fisheries Research and Management with Computer Vision: A Survey of Recent Developments and Pending Challenges DOI Creative Commons
Jesse Eickholt, Jonathan Gregory,

Kavya Vemuri

et al.

Fishes, Journal Year: 2025, Volume and Issue: 10(2), P. 74 - 74

Published: Feb. 12, 2025

The field of computer vision has progressed rapidly over the past ten years, with noticeable improvements in techniques to detect, locate, and classify objects. Concurrent these advances, improved accessibility through machine learning software libraries sparked investigations applications across multiple domains. In areas fisheries research management, efforts have centered on localization fish classification by species, as such tools can estimate health, size, movement populations. To aid interpretation for management tasks, a survey recent literature was conducted. contrast prior reviews, this focuses employed evaluation metrics datasets well challenges associated applying context. Misalignment between commonly used mischaracterizes efficacy emerging tasks. Aqueous, turbid, variable lighted deployment settings further complicate use generalizability reported results. Informed inherent challenges, culling surveillance data, exploratory data collection remote settings, selective passage traps are presented opportunities future research.

Language: Английский

Citations

0
Fish Detection in Fishways for Hydropower Stations Using Bidirectional Cross-Scale Feature Fusion DOI Creative Commons
Junming Wang, Yankun Gong, Wupeng Deng

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2743 - 2743

Published: March 4, 2025

Fishways can effectively validate the effectiveness and rationality of their construction, optimize operational modes, achieve intelligent scientific management through fish species detection. Traditional detection methods for fishways are unsuitable due to inefficiency disruption ecological environment. Therefore, combining cameras with target technology provides a better solution. However, challenges include limited computational power onsite equipment, complexity model deployment, low accuracy, slow speed, all which significant obstacles. This paper proposes accurate efficient Firstly, backbone network integrates FasterNet-Block, C2f, an multi-scale EMA attention mechanism address dispersion problems during feature extraction, delivering real-time object across different scales. Secondly, Neck introduces novel architecture enhance fusion by integrating RepBlock BiFusion modules. Finally, performance is demonstrated based on Fish26 dataset, in cost, parameter count significantly optimized 1.7%, 23.4%, 24%, respectively, compared state-of-the-art model. At same time, we installed devices specific fishway deployed proposed method within these devices. We collected data four passing create dataset train The results practical application superior capabilities, rapid ability achieved while minimizing resource usage. validated equipment deployment real-world engineering environments. marks shift from traditional manual fishways, promoting water utilization protection

Language: Английский

Citations

0
A Real-Time Fish Detection System for Partially Dewatered Fish to Support Selective Fish Passage DOI Creative Commons
Jonathan Gregory, Scott Miehls, Jesse Eickholt

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1022 - 1022

Published: Feb. 9, 2025

Recent advances in fish transportation technologies and deep machine learning-based classification have created an opportunity for real-time, autonomous sorting through a selective passage mechanism. This research presents case study of novel application that utilizes learning to detect partially dewatered exiting Archimedes Screw Fish Lift (ASFL). A MobileNet SSD model was trained on images volitionally passing ASFL. Then, this integrated with network video recorder monitor from the Additional models were also using similar scanning device test feasibility approach classification. Open source software edge computing design principles employed ensure system is capable fast data processing. The findings demonstrate such ASFL can support real-time detection. contributes goal automated collection viable path towards realizing optical sorting.

Language: Английский

Citations

0
Efficient tuna detection and counting with improved YOLOv8 and ByteTrack in pelagic fisheries DOI Creative Commons
Yuanchen Cheng, Zichen Zhang, Yuqing Liu

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103116 - 103116

Published: April 1, 2025

Language: Английский

Citations

0
Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review DOI Creative Commons
Tymoteusz Miller, Grzegorz Michoński, Irmina Durlik

et al.

Biology, Journal Year: 2025, Volume and Issue: 14(5), P. 520 - 520

Published: May 8, 2025

Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, conservation planning. This systematic review follows the PRISMA framework to analyze AI applications freshwater studies. Using structured literature search across Scopus, Web of Science, Google Scholar, we identified 312 relevant studies published between 2010 2024. categorizes into assessment, ecological risk evaluation, strategies. A bias assessment was conducted using QUADAS-2 RoB 2 frameworks, highlighting methodological challenges, such measurement inconsistencies model validation. The citation trends demonstrate exponential growth AI-driven with leading contributions from China, United States, India. Despite growing use this field, also reveals several persistent including limited data availability, regional imbalances, concerns related generalizability transparency. Our findings underscore AI’s potential revolutionizing but emphasize need for standardized methodologies, improved integration, interdisciplinary collaboration enhance insights efforts.

Language: Английский

Citations

0