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Role of Machine Learning and Machine Vision on Microplastics
Summary
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.