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An Investigation of Nile Tilapia (Oreochromis niloticus) Movement Trajectories Under Ammonia Stress Using Image Processing Techniques

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Pubblicato in:Life vol. 15, no. 7 (2025), p. 1004-1019
Autore principale: Arslan, Muhammed Nurullah
Altri autori: Güray, Tonguç, Balci, Beytullah Ahmet, Sari Tuba
Pubblicazione:
MDPI AG
Soggetti:
Physiology
Quality management
Behavior
Software
Ammonium
Optical flow (image analysis)
Aquaculture
Image processing
Water quality
Automation
Statistical analysis
Ammonium chloride
Fish
Tilapia
Cameras
Clustering
Ammonia
Water quality management
Algorithms
Visual programming languages
Oreochromis niloticus
Environmental
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Abstract:This study examined the behavioral responses of Nile Tilapia (Oreochromis niloticus), a key aquaculture species, to ammonia stress using non-invasive image processing techniques. The experiment was conducted under controlled laboratory conditions and involved four groups exposed to ammonium chloride concentrations (0, 100, 200, and 400 mg·lt−1). Movement trajectories of individual fish were recorded over 10 h using high-resolution cameras positioned above and beside glass tanks. Images were processed with the Optical Flow Farneback algorithm in Python, implemented in Visual Studio Code with OpenCV and NumPy libraries, achieving a 91.40% accuracy rate in tracking fish positions. The results revealed that increasing ammonia levels restricted movement areas while elevating movement irregularity and activity. The 0 mg·lt−1 group utilized the glass tank homogeneously, covering 477 m. In contrast, the 100 mg·lt−1 group showed clustering in specific areas (796 m). At 200 mg·lt−1, clustering intensified, particularly along the glass tank’s left edge (744 m), and at 400 mg·lt−1, fish exhibited severe restriction near the water surface with markedly increased activity (928 m). Statistical analyses using Kruskal–Wallis and Dunn tests confirmed significant differences between the 400 mg·lt−1 group and others. No difference was observed between the 0 mg·lt−1 and 100 mg·lt−1 group, indicating tolerance to lower concentrations. The study highlights the importance of ammonia levels in water quality management and reveals the potential of image processing techniques for automation and stress monitoring in aquaculture.
ISSN:2075-1729
DOI:10.3390/life15071004
Fonte:Biological Science Database