Deep learning based classification of microplastic in edible food using optical microscopy images

Title

Deep learning based classification of microplastic in edible food using optical microscopy images

Creator

Harde, Neha; B, Tulasi

Description

Microplastics (MPs), a prevalent pollution in food, water, and ecosystems around the world, have become a serious environmental and health concern. The traditional detection and classification techniques are labor-intensive by nature and do not support extensive, large-scale monitoring. The main emphasis of this study is to generate a novel image dataset via a simple extraction method that will be useful for classification applications in high-consumption edible food by integrating with the deep-learning model. This study compares the efficacy of several Deep learning (DL) architectures, including MobileNetV2, ResNet101V2, ResNet50V2, InceptionV3, EfficientNetB0, and a baseline Convolutional Neural Network (CNN) in classification into three groups: threads, beads, and fragments. The best performance was recorded by MobileNetV2, ResNet101V2, and ResNet50 V2, all with 98 percent test accuracy and weighted F1-scores of 0.986 and 0.983, respectively, which is a strong and consistent MPs classification. The outcome indicates that the DL models, especially ResNet101V2 and MobileNetV2, outperform the baseline CNN in terms of classification accuracy (98%). The present study provides strong, scalable opportunities for Artificial Intelligence (AI) based solutions for the assessment and reduction of MPs contamination globally in edible food. The Author(s) 2026.

Source

Discover Artificial Intelligence;Volume;6;Issue;1;Article No.;478;

Date

01-01-2026

Publisher

Springer Nature

Subject

Automated detection; Deep learning; Food contamination; Image classification; MP

Coverage

Harde N., CHRIST (Deemed to be University), Karnataka, Bangalore, India; B T., CHRIST (Deemed to be University), Karnataka, Bangalore, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 27310809;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.1007/s44163-026-01246-8

https://www.scopus.com/pages/publications/105040515934?origin=resultslist