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Detection and Classification of Microplastics in Water Source Using Svm
Summary
Researchers developed a machine learning system using Support Vector Machines (SVM) to automatically identify and classify microplastics in water samples based on their size, shape, and light-reflection properties captured through high-resolution imaging. The automated approach enables faster, more consistent microplastic monitoring compared to manual inspection, supporting real-time pollution tracking.
With detrimental effects on human health and marine ecosystems, microplastic pollution of water sources has emerged as a major environmental concern.Using Support Vector Machine (SVM) techniques, this project offers a novel method for identifying and categorizing microplastics.To enable precise and effective identification of microplastic particles based on their physical and spectral characteristics, the methodology combines cutting-edge imaging technologies with machine learning.Preprocessing methods are used to enhance image clarity and separate microplastic particles after high-resolution imaging is used to evaluate water samples.To accurately categorize the different forms of microplastics, an SVM classifier is trained using key properties such as size, shape, and texture.Because of its great precision and dependability, the suggested system is a useful instrument for monitoring and analysis in real time.This initiative facilitates the creation of efficient mitigation plans and improves the capacity to monitor the sources of pollution by automating the classification process.The findings support sustainable water management techniques and advance our knowledge of the behaviour of microplastics in aquatic ecosystems.