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Role of Machine Learning and Machine Vision on Microplastics
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Scientists are teaching computers to spot and identify microplastics in samples of water, soil, and food much faster than humans can by eye, this review paper explains how these AI tools work and where they still fall short. Faster, more accurate detection matters because it could help researchers track how much plastic contamination is getting into our environment and food supply, which is an important step toward understanding potential health risks. The technology still needs better cameras, shared databases, and standardized methods before it can be reliably used everywhere.
The identification, categorization, and quantification of microplastics are labor-intensive and time-consuming processes. Recently, the amalgamation of machine learning (ML) and machine vision (MV) technologies has surfaced as a revolutionary method for enhancing the detection and examination of microplastics. This chapter examines the function of ML and MV in microplastics research, highlighting unsupervised methods (such as k -means clustering) and supervised techniques (including support vector machines, random forests, and convolutional neural networks). These models can identify polymer kinds, differentiate microplastics from natural particles, and anticipate deterioration stages, rendering them effective instruments for automated monitoring systems. The sensitivity and resolution of imaging systems, encompassing camera specs and lighting conditions, profoundly influence detection accuracy. Inconsistent data collection techniques, restricted availability to high-quality reference libraries for training, and the absence of standardized picture datasets impede reproducibility and model generalizability. Moreover, data exchange among institutions continues to be impeded by privacy concerns, format incompatibility, and a scarcity of open-access platforms. This chapter underscores the increasing significance of AI-driven instruments in microplastic research, while accentuating the necessity for collaborative initiatives in the establishment of shared datasets, the advancement of camera technologies, and the formulation of standardized procedures. Addressing the existing gaps is crucial for maximizing the promise of machine learning and machine vision in tackling the worldwide issue of microplastic contamination.
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Application of Artificial Intelligence to Improve Microplastic Identification
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This review examines how artificial intelligence -- including machine learning and computer vision approaches -- can improve the identification, classification, and measurement of microplastics during environmental sampling and analysis. AI-assisted identification addresses a major bottleneck in microplastic research by reducing the time and expertise required to process large sample volumes, enabling faster and more consistent data generation needed to assess human health exposure at scale.
Prospective Application of Artificial Intelligence Towards the Detection, and Classifications of Microplastics with Bibliometric Analysis
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Using bibliometric analysis to map the microplastics research landscape, this study proposes integrating AI techniques — including machine learning with FTIR spectroscopy, holographic imaging, and 3D modeling — to overcome the labor-intensive limitations of traditional MP identification and classification methods. Scalable, AI-driven detection tools are critical for accelerating MP monitoring and research, enabling the high-throughput data collection needed to understand population-level exposure and inform regulatory standards.
Laser induced fluorescence and machine learning: a novel approach to microplastic identification
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Researchers combined laser-induced fluorescence with machine learning to identify microplastics in water in real time, achieving 97.6% accuracy in distinguishing plastics from other organic matter and 88.3% accuracy in identifying specific plastic types — a promising step toward automated ocean monitoring.
Defining Irregular Microplastics: A Machine Learning Approach for Morphometric Characterization
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Researchers applied a machine learning decision-tree classifier to hyperspectral images of 129 nano- and microplastic particles and identified five morphometric descriptors—circularity, roundness, solidity, long diameter, and perimeter-area ratio—that together distinguish irregular particles from spherical and fibrous forms with 96% accuracy, providing a standardized definition for the most environmentally prevalent but least-defined shape class.
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