Landfill Waste Segregation Using Transfer and Ensemble Machine Learning: A Convolutional Neural Network Approach

Angelika Sita Ouedraogo, Ajay Kumar () and Ning Wang
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Angelika Sita Ouedraogo: Biosystems and Agricultural Engineering, Oklahoma State University, 111 Agriculture Hall, Stillwater, OK 74078, USA
Ajay Kumar: Biosystems and Agricultural Engineering, Oklahoma State University, 111 Agriculture Hall, Stillwater, OK 74078, USA
Ning Wang: Biosystems and Agricultural Engineering, Oklahoma State University, 111 Agriculture Hall, Stillwater, OK 74078, USA

Energies, 2023, vol. 16, issue 16, 1-14

Abstract: Waste disposal remains a challenge due to land availability, and environmental and health issues related to the main disposal method, landfilling. Combining computer vision (machine learning) and robotics to sort waste is a cost-effective solution for landfilling activities limitation. The objective of this study was to combine transfer and ensemble learning to process collected waste images and classify landfill waste into nine classes. Pretrained CNN models (Inception–ResNet-v2, EfficientNetb3, and DenseNet201) were used as base models to develop the ensemble network, and three other single CNN models (Models 1, 2, and 3). The single network performances were compared to the ensemble model. The waste dataset, initially grouped in two classes, was obtained from Kaggle, and reorganized into nine classes. Classes with a low number of data were improved by downloading additional images from Google search. The Ensemble Model showed the highest prediction precision (90%) compared to the precision of Models 1, 2, and 3, 86%, 87%, and 88%, respectively. All models had difficulties predicting overlapping classes, such as glass and plastics, and wood and paper/cardboard. The environmental costs for the Ensemble network, and Models 2 and 3, approximately 15 g CO 2 equivalent per training, were lower than the 19.23 g CO 2 equivalent per training for Model 1.

Keywords: CNN; deep learning; ensemble learning; Inception–ResNet; EfficienNet; DenseNet; MSW; image classification; computational cost (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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