Micro-Objects Classification for Microplastic Pollution Detection using Holographic Images
Naga Sai Munagala, V. Manoj Kumar, S. Srividhya, Gandhimathi Usha, R. Jeyalakshmi · 2024
Microplastic pollution poses a significant environmental threat, necessitating advanced methods for effective control and mitigation. This paper focuses on the development of a novel approach for micro-objects classification, specifically targeting microplastics, utilizing holographic images. Holography provides a three-dimensional representation of microscopic particles, enhancing the accuracy and efficiency of classification algorithms. In this paper, initially the acquisition of holographic images of water samples containing micro-objects, with a particular emphasis on microplastics are collected. State-of-the-art image processing and machine learning techniques are employed to develop a robust classification system capable of distinguishing between various micro-objects with high precision. Next, the Key objectives include the design and implementation of classification algorithm, leveraging holographic data to differentiate microplastics from other particles. The proposed work aims to enhance the efficiency of microplastic identification, enabling more effective monitoring and control of pollution in aquatic environments.The outcomes of this paper hold promise for advancing microplastic pollution research and environmental monitoring practices. The integration of holographic images into the classification process offers a unique perspective, providing valuable insights into the characteristics and distribution of microplastics. Ultimately, this research contributes to the development of innovative solutions for addressing the pervasive issue of microplastic pollution.