Enhancing aquatic ecosystem monitoring through fish jumping behavior analysis and YOLOV5: Applications in freshwater fish identification

Ronghui Li; Kaibang Xiao; Senhai Lin; Zedong Wu

doi:10.1016/j.jenvman.2025.126413

Enhancing aquatic ecosystem monitoring through fish jumping behavior analysis and YOLOV5: Applications in freshwater fish identification

J Environ Manage. 2025 Jul 1:391:126413. doi: 10.1016/j.jenvman.2025.126413. Online ahead of print.

Authors

Ronghui Li¹, Kaibang Xiao², Senhai Lin¹, Zedong Wu¹

Affiliations

¹ College of Civil Engineering and Architecture, Guangxi University, Nanning, 530004, China; Key Laboratory of Disaster Prevention and Structural Safety of the Ministry of Education, College of Civil Engineering and Architecture, Guangxi University, Nanning, 530004, China.
² College of Civil Engineering and Architecture, Guangxi University, Nanning, 530004, China; Key Laboratory of Disaster Prevention and Structural Safety of the Ministry of Education, College of Civil Engineering and Architecture, Guangxi University, Nanning, 530004, China. Electronic address: xkb2811738419@163.com.

PMID: 40602254
DOI: 10.1016/j.jenvman.2025.126413

Abstract

Traditional fish monitoring methods suffer from limited continuity and significant uncertainty in tracking population distribution. This study develops recognition rules using the inherent variability in fish jumping behavior, influenced by habitat differences and physical traits. A comprehensive dataset is constructed on fish jumping behavior (FJB), relevant features are extracted and domain expertise is incorporated into the design process, using the YOLOV5 deep-learning model for target detection. An automatic fish species identification model, Fish-reco, is developed based on YOLOV5, which utilizes jumping behavior data, feature extraction, and recognition rules. Our results demonstrate the capability of the water splash data extraction model to effectively capture video clips depicting fish-water interactions in natural aquatic environments. Notably, both precision and recall exceed 96 % in the validation set. Additionally, a comprehensive feature library is established through feature engineering on 877 dataset samples, which encapsulates the jumping behaviors and resulting ripples of three freshwater fish species, including catfish, bighead carp, and carp, across various jumping stages. Finally, the robust performance of the FJB-based Fish-reco fish species identification model in classifying the above three common freshwater species is demonstrated. The recognition precision of carp, bighead carp, and catfish can reach 0.845, 0.92, and 0.995, respectively, and the mAP@50 can reach 0.918, 0.908, and 0.993. This study intuitively reflects the fish's physiological state and habitat by shifting the observation viewpoint from below to above the water surface, offering valuable insights for fishery resource assessment and water ecology evaluation.

Keywords: Aquatic ecosystem monitoring; Feature engineering; Fish jumping behavior; Species identification; YOLOv 5s.