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Scalable Step-by-Step Approach of Sustainable Bioplastic Production from Food Waste

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An Intelligent Waste-Sorting and Recycling Device Based on Improved EfficientNet.

Zhicheng Feng^1,2, Jie Yang^1,3, Lifang Chen⁴

¹Department of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China.

International Journal of Environmental Research and Public Health

|December 11, 2022

Summary

This study introduces an intelligent waste bin for automatic household waste sorting, improving recycling efficiency. The GECM-EfficientNet model achieves high accuracy in real-time waste classification, simplifying sorting for residents.

Keywords:

EfficientNet artificial intelligence image classification sustainable development waste sorting and recycling

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Area of Science:

Environmental Science
Computer Science
Artificial Intelligence

Background:

Urban waste generation is a significant environmental challenge.
Inefficient household waste sorting by residents hinders recycling and increases costs.
Accurate waste classification is crucial for effective waste management.

Purpose of the Study:

To develop an intelligent waste bin for automated waste sorting.
To create a highly accurate and real-time waste classification model.
To simplify waste sorting for residents using intelligent devices.

Main Methods:

Designed an intelligent waste bin for automatic sorting and recycling.
Proposed GECM-EfficientNet, an optimized EfficientNet model.
Incorporated streamlined MBConv, ECA, and CA modules with transfer learning.

Main Results:

GECM-EfficientNet achieved 94.54% accuracy on a custom dataset and 94.23% on TrashNet.
The model boasts only 1.23 M parameters.
Real-time classification achieved in 146 ms per recognition.

Conclusions:

The GECM-EfficientNet model enhances computational efficiency for waste classification.
The intelligent waste bin simplifies waste sorting, reducing the need for resident expertise.
This technology promotes better waste management and recycling practices.