Title: Automated kitchen waste segregation system via convolutional neural network

Authors: Teh Boon Hong; Sarah 'Atifah Saruchi; Ain Atiqa Mustapha; Nor Aziyatul Izni; Wan Zailah Wan Said; Noor Idayu Mohd Tahir

Addresses: EMMECOM (Asia) Pte. Ltd., 628942, Singapore; Department of Mechanical and Mechatronics, Faculty of Engineering, Technology and Built Environment, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia ' Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia ' Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia; Department of Mechanical and Mechatronics, Faculty of Engineering, Technology and Built Environment, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia ' Centre of Foundation Studies, Universiti Teknologi MARA, Cawangan Selangor, Kampus Dengkil, 43800, Dengkil, Selangor, Malaysia ' Department of Mechanical and Mechatronics, Faculty of Engineering, Technology and Built Environment, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia ' Department of Mechanical and Mechatronics, Faculty of Engineering, Technology and Built Environment, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia

Abstract: Composting is one of the efficient and practical methods to manage kitchen waste. The initial process of the composting system is the kitchen waste segregation between compostable and non-compostable categories. However, currently, the segregation process is carried out by human labour. Thus, to reduce the human labour burden, this study proposes an automated kitchen waste segregation system by deep learning method to classify kitchen waste into two groups: compostable and non-compostable. A convolutional neural network (CNN) model with different learning algorithms and several epochs is applied to perform the segregation. A prototype consisting of a camera, sensors, and motors is developed to validate the performance efficiency of the proposed model. Results show that the integration of CNN into the proposed kitchen waste segregation system manages to segregate the waste successfully without human involvement. This output is expected to contribute to supporting the waste management and composting campaign thus leading to a better environment.

Keywords: kitchen waste; composting; convolutional neural network; CNN; internet of things; IoT; automation.

DOI: 10.1504/IJCVR.2026.155194

International Journal of Computational Vision and Robotics, 2026 Vol.17 No.1, pp.99 - 119

Received: 09 Oct 2023
Accepted: 23 Jan 2024
Published online: 29 Jul 2026 *

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