Automated Waste Segregation using Raspberry Pi and Deep Learning

Title

Automated Waste Segregation using Raspberry Pi and Deep Learning

Creator

Khan, Fazal; Priya, Sharon Roji C.; Burhanuddin, Ibrahim; Kurian, Joshua P.K.

Description

With rapid urbanization and increasing waste generation, efficient waste segregation has become a critical challenge for sustainable waste management. Traditional waste disposal methods rely heavily on manual sorting, which is inefficient, labor-intensive, and prone to errors, leading to improper recycling and environmental hazards. To address these problems, a clever waste segregation method is presented in this research. It automatically sorts waste into four categoriesglass, metal, plastic, and paper/cardboardusing computer vision and machine learning. A 720p webcam is used to collect images in real time, and the system is powered by a Raspberry Pi 4B with 4GB of RAM. A Convolutional Neural Network (CNN) model that was developed using the TrashNet dataset forms its basis. The model can correctly identify the waste in the photos due to an optimized training method that incorporates data augmentation, regularization strategies, and early stopping to prevent overfitting. An SG90 servo motor controls the lid, ensuring the garbage is placed in the appropriate compartment, while an MG996R servo motor swings the bin into place after the waste has been classified. The bin and lid go back to their initial places once the garbage has been dumped, preparing the system for usage again. Here, we are able to combine automated mobility, automated categorization, and real-time waste detection with embedded technologies, machine learning, and automation to separate waste with the least amount of human intervention. Furthermore, the system's scalability and adaptability make it appropriate for smart city initiatives, urban trash management, and wider industrial application. Consequently, this technology helps to tackle intelligent waste management problems, which facilitates the emergence of a sustainable and eco-friendly future. The system achieved a testing accuracy of 88.1%, showcasing its effectiveness and reliability. Grenze Scientific Society, 2025.

Source

16th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2025;Volume;2;pp.8620-8625

Date

01-01-2025

Publisher

Grenze Scientific Society

Subject

automation; computer vision; Convolutional Neural Network (CNN); environmental sustainability; machine learning; Real-time waste detection; smart waste management; TrashNet dataset; Waste segregation

Coverage

Khan F., Department of Computer Science and Engineering, Christ University, Bangalore, India; Priya S.R.C., Department of Computer Science and Engineering, Christ University, Bangalore, India; Burhanuddin I., Department of Computer Science and Engineering, Christ University, Bangalore, India; Kurian J.P.K., Department of Computer Science and Engineering, Christ University, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Format

online

Language

English

Type

Conference paper

Identifier

https://www.scopus.com/pages/publications/105034833401?origin=resultslist