ECO-HYBRID: Sustainable Waste Classification Using Transfer Learning with Hybrid and Enhanced CNN Models

Sharanya Shetty, Saanvi Kallianpur, Roshan Fernandes (), Anisha P. Rodrigues and Vijaya Padmanabha
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Sharanya Shetty: Department of Computer Science and Engineering, NMAM Institute of Technology (NMAMIT), Nitte (Deemed to be University), Nitte 574 110, Karnataka, India
Saanvi Kallianpur: Department of Computer Science and Engineering, NMAM Institute of Technology (NMAMIT), Nitte (Deemed to be University), Nitte 574 110, Karnataka, India
Roshan Fernandes: Department of Cyber Security, NMAM Institute of Technology (NMAMIT), Nitte (Deemed to be University), Nitte 574 110, Karnataka, India
Anisha P. Rodrigues: Department of Computer Science and Engineering, NMAM Institute of Technology (NMAMIT), Nitte (Deemed to be University), Nitte 574 110, Karnataka, India
Vijaya Padmanabha: Department of Mathematics and Computer Science, Modern College of Business and Science, Bawshar, Muscat 133, Oman

Sustainability, 2025, vol. 17, issue 19, 1-27

Abstract: Effective waste management is important for reducing environmental harm, improving recycling operations, and building urban sustainability. However, accurate waste classification remains a critical challenge, as many deep learning models struggle with diverse waste types. In this study, classification accuracy is enhanced using transfer learning, ensemble techniques, and custom architectures. Eleven pre-trained convolutional neural networks, including ResNet-50, EfficientNet variants, and DenseNet-201, were fine-tuned to extract meaningful patterns from waste images. To further improve model performance, ensemble strategies such as weighted averaging, soft voting, and stacking were implemented, resulting in a hybrid model combining ResNet-50, EfficientNetV2-M, and DenseNet-201, which outperformed individual models. In the proposed system, two specialized architectures were developed: EcoMobileNet, an optimized MobileNetV3 Large-based model incorporating Squeeze-and-Excitation blocks for efficient mobile deployment, and EcoDenseNet, a DenseNet-201 variant enhanced with Mish activation for improved feature extraction. The evaluation was conducted on a dataset comprising 4691 images across 10 waste categories, sourced from publicly available repositories. The implementation of EcoMobileNet achieved a test accuracy of 98.08%, while EcoDenseNet reached an accuracy of 97.86%. The hybrid model also attained 98.08% accuracy. Furthermore, the ensemble stacking approach yielded the highest test accuracy of 98.29%, demonstrating its effectiveness in classifying heterogeneous waste types. By leveraging deep learning, the proposed system contributes to the development of scalable, sustainable, and automated waste-sorting solutions, thereby optimizing recycling processes and minimizing environmental impact.

Keywords: convolutional neural networks; deep learning; ensemble learning; hybrid models; sustainable waste classification; transfer learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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