Advanced International Journal for Research

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Volume 7 Issue 3
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Heterogeneous Ensemble Meta-Learning for Automated Plastic Type Waste Classification using Deep Neural Networks

Author(s) Ratnesh Kumar Choudhary, Ansh Devendra Dulewale, Dishant Dilip Sevake, Riya Shrihari Ramteke, Shweta Homraj Rewatkar, Bharti Mathankar, Prachi Borade
Country India
Abstract A good segregation of plastic waste is essential towards environmental sustainability and recycling.Manual sorting is also time consuming and riddled with errors and this is a factor that requires theapplication of automated classification systems. This paper introduces a new heterogeneous ensemblemodel with three state-of-the-art deep learning models EfficientNetV2, Xception and VisionTransformer-based on automated plastic waste classification. In the suggested strategy, complementaryfeatures of plastic waste images are extracted at the base level and then synthesized by a LogisticRegression meta-learner at the top level toprovide ultimate classification. The framework is tested on adataset of 14,064 images of plastics of four materials namely the High-Density Polyethylene,Polyethylene Terephthalate, Polypropylene, and Polystyrene. EfficientNetV2, Xception, and VisionTransformer have individual models with accuracies of 92-95, 90-93, and 94.31 respectively. Theheterogeneous ensemble meta-learner was also shown to be better as it achieved 95-97% classificationaccuracy, which was a major enhancement over single architecture. These findings suggest that the useof meta-learning in order to combine different deep learning models is highly effective to learn diverseplastic waste features, which can be used as a strong answer to automated waste management systems.
Keywords Deep Learning, Ensemble Learning, Meta-learner, Plastic Waste Classification, Computer Vision, Vision Transformer (Vit), Xception, Efficientnetv2, Image Classification, Waste Management, Recycling, Stacking Ensemble.
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-08
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E-ISSN 3048-7641
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