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Proof of Concept: A TinyML-Based Image Classifier for Detecting Microplastics and Waste in Simulated Marine Environments
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Researchers developed a proof-of-concept image classifier using Tiny Machine Learning (TinyML) implemented on an ESP32-CAM module to detect microplastics and cardboard waste in simulated marine environments, training a MobileNetV1-based transfer learning model on 229 images with 96x96 pixel scaling and normalization for low-cost autonomous monitoring applications.
This study presents the development of an image classifier designed to identify microplastics and cardboard waste, with potential application in marine environments, using Tiny Machine Learning (TinyML) implemented on an ESP32-CAM module. A total of 229 images were collected, classified into mi-croplastics and non-microplastics, and preprocessing techniques such as scaling (to $96 imes 96$ pixels) and normalization were applied to optimize the performance of the chosen model. The learning technique used was Transfer Learning, employing a model based on the MobileNetV1 architecture. The model was trained and validated, achieving an accuracy of 91.7% on the validation set under controlled conditions. This implementation demonstrates the initial feasibility of using TinyML on resource-constrained devices for plastic waste detection, laying the groundwork for future testing in real marine environments. The study provides a promising foundation for the development of environmental monitoring tools, although additional validation in field conditions is required to confirm its effectiveness in real aquatic ecosystems.
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A low-cost embedded system using an ESP32 microcontroller, LED light source, and light-dependent resistor combined with TinyML models successfully identified polyethylene and polypropylene microplastics in liquid samples through optical light-intensity variation. This portable, field-deployable sensor design represents a significant step toward accessible microplastic monitoring outside specialized laboratory settings.
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Researchers developed a lightweight convolutional neural network called the Slim-DL-Model for classifying microplastics in marine environment images, designed to overcome the computational demands of existing architectures like VGG16 and ResNet for real-time field applications. The model achieves competitive classification accuracy while significantly reducing computational requirements, enabling deployable microplastic monitoring systems.
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Researchers trained the lightweight YOLOv8 Nano model on over 3,200 environmental images to detect microplastics automatically, achieving 84% precision and exporting the model to TensorFlow Lite format for deployment on mobile and embedded devices in resource-constrained field settings.
Automated Microplastic Detection in Environmental Samples Using YOLOv8 Nano: A Lightweight Approach for Edge Deploymen
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Researchers trained the lightweight YOLOv8 Nano model on over 3,200 environmental images to detect microplastics automatically, achieving 84% precision and exporting the model to TensorFlow Lite format for deployment on mobile and embedded devices in resource-constrained field settings.
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Two AI models (MLP and GRU) were designed for microplastic detection in water from scattered optical signals and optimized via neural architecture search, then compiled with a custom TinyML compiler for deployment on resource-constrained edge devices—enabling low-power, distributed microplastic monitoring.
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