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Visual Observation to Detect Macroplastic Object in River: A Review of Current Knowledge
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This review examines visual observation methods for detecting macroplastic objects in rivers, using a systematic approach to assess current research trends, methodologies, and future directions in riverine plastic monitoring. Researchers found that visual observation is a widely used and adaptable method for measuring plastic quantity, composition, and distribution, though standardization gaps limit cross-study comparisons and effective mitigation strategy design.
Currently, the world is facing the problem of plastic pollution in water bodies. Plastic waste has become an abundant pollutant in the marine, coastal and river environments, making it a major threat to aquatic life. Visual Observation in plastic monitoring is a popular method used to measure quantity, composition, and distribution, identify emerging trends, and design preventive measures or mitigation strategies. This study attempts to review recent studies regarding visual observation for detecting macroplastic objects in terms of current research trends and methodologies and suggests promising future research directions. This study used a systematic method with a bibliometric approach and qualitative content analysis to identify and review 108 articles on detecting litter objects in the water. The study results show that automatic object detection is starting to become a trend in visual Observation by relying on artificial intelligence (AI) with UAV devices and cameras that are processed using Machine Learning and Deep Learning methods which provide promising accuracy results.
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