HUANG Zhenli, WEN Hao, QI Hongfang, YE Guanzhong, LI Haiying, WANG Luhai, FU Shengyun, ZHONG Nanchang, LIU Jun. Artificial intelligence fish-monitoring links Gymnocypris przewalskii migration to river environment[J]. Journal of fisheries of china. DOI: 10.11964/jfc.20241114755
Citation: HUANG Zhenli, WEN Hao, QI Hongfang, YE Guanzhong, LI Haiying, WANG Luhai, FU Shengyun, ZHONG Nanchang, LIU Jun. Artificial intelligence fish-monitoring links Gymnocypris przewalskii migration to river environment[J]. Journal of fisheries of china. DOI: 10.11964/jfc.20241114755

Artificial intelligence fish-monitoring links Gymnocypris przewalskii migration to river environment

  • The naked carp (Gymnocypris przewalskii) is a keystone and anadromous species in Qinghai Lake, but its annual reproductive migration to rivers is not well understood due to insufficient monitoring tools. Here, we introduced a ″fish optical video + artificial intelligence″ technology, developed an artificial intelligence fish detection (AI-Fish) algorithm, and developed intelligent fish monitoring equipment for shallow rivers. We also established a standard growth model for G. przewalskii to estimate the age of fish and applied it to a 30-metre-wide section of the lower Quanji River. From 2021 to 2024, monitoring data revealed the migration dynamics and environmental interactions of G. przewalskii in Qinghai Lake. The annual migration comprises three phases: early (upstream migration), middle (breeding), and late (downstream migration). River flow and water temperature primarily influence migration timing, with a critical flow rate of 2.5 m³/s promoting upstream migration in the Quanji River, peaking at 10 m³/s. Upstream migration aligns with daily water temperature rhythms, peaking in the afternoon (15:00-16:00) and night (20:00-21:00), with a daily maximum of 362,000 individuals. Notably, not all migrants spawn, exhibiting 'non-reproductive accompanying migration.' Migration is geographically limited by natural barriers and altitude. Big data analysis of over 700 000 individuals showed a normally distributed age structure, dominated by 3-7 year old of ages , with distinct male and female distributions influenced by sex ratio. Our results indicate that the developed equipment effectively can be used for all-weather and real-time monitoring of the migratory population of G. przewalskii in Qinghai Lake, with an identification accuracy of more than 90%, and can obtain biological parameters such as the number of fish, direction of migration, full length, and age structures. By establishing a monitoring network in the main rivers of Qinghai Lake, we can quickly and accurately collect comprehensive 'big data' on the migrating population each year, providing robust scientific support for their future conservation.
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