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Machine Learning Models for Detecting Emerging Contaminants in Seagrass Ecosystems via Drone Imagery

Mughair Aslam BhattiSzabist University, PakistanBakirov JumaDepartment of Preschool and Primary Education, Termez University of Economics and Service, Termez, UzbekistanIntizor AvazmetovaDepartment of Biology, Urgench State University, Urgench, UzbekistanAnorgul AshirovaDepartment of General Professional Sciences, Mamun University, Khiva, UzbekistanAvezova Umida MaksudovnaDepartment of Ecology and Life Safety, Urgench State University Named After Abu Rayhan Biruni, Uzbekistan

2026ng

ABI

Annotatsiya

Machine learning (ML) models are increasingly being applied to environmental monitoring, particularly for detecting emerging contaminants in marine ecosystems. Seagrass ecosystems, vital for biodiversity and carbon sequestration, are highly sensitive to pollutants, which can degrade their health and function. Traditional methods of contaminant detection often involve time-consuming fieldwork and limited spatial coverage. However, drone imagery, combined with ML techniques, offers a powerful tool for rapid, large-scale monitoring. ML algorithms can analyze high-resolution drone images to detect subtle changes in water quality, seagrass health, and potential contamination levels, including heavy metals, pesticides, and pharmaceuticals.

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DOI: 10.4018/979-8-3373-8872-4.ch009

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