Journal of Electrical Engineering and Automation
IRO Journals
Volume 8 — Issue 1 — March 2026

Arduino-Based Automated Waste Segregation System for Efficient Recycling

https://doi.org/10.36548/jeea.2026.1.002
Pages: 25-39
PDF
Jegadeeshwari P
Department of Electronics and communication Engineering, Dr.M.G.R Educational and Research Institute, Chennai, India
Nagadurgarao M.
Department of Electronics and communication Engineering, Dr.M.G.R Educational and Research Institute, Chennai, India
Raviteja N.
Department of Electronics and communication Engineering, Dr.M.G.R Educational and Research Institute, Chennai, India
Balakrishna P.
Department of Electronics and communication Engineering, Dr.M.G.R Educational and Research Institute, Chennai, India
March 2026
Volume 8 — Issue 1
Journal of Electrical Engineering and Automation
PDF

How to Cite

P, Jegadeeshwari, Nagadurgarao M., Raviteja N., and Balakrishna P. 2026. “Arduino-Based Automated Waste Segregation System for Efficient Recycling”. Journal of Electrical Engineering and Automation 8 (1): 25-39. https://doi.org/10.36548/jeea.2026.1.002.
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Keywords

— Arduino Uno
— Waste Segregation
— Smart Waste Management
— Moisture Sensor
— Ultrasonic Sensor
— Embedded Systems
— Automated Recycling
— Environmental Sustainability
Published: 23-03-2026

Abstract

Now-a-days, the environment faces many challenges relating to effective waste management, particularly with the rapid population growth and increasing levels of solid waste produced in urban areas. The waste misclassification of generation contributes to the ineffectively recycling and eventually contributes to pollution. Traditional systems for sorting waste are largely dependent on manual methods that are labour-intensive and may not produce the desired result. This paper presents solutions to the previously mentioned issues by presenting a prototype for an automated waste sorting system using an Arduino platform. The system will enable the separation of wet and dry waste through sensor technology. The moisture content of the waste will be determined from moisture sensors identify the type of waste. Data from these sensors is sent and processed by an Arduino Uno microcontroller. When a waste material is determined to have a certain classification, the waste will automatically be sorted into the appropriate bin via one of the servo motor mechanisms., Ultrasonic fill level sensors exist to measure bin contents and buzzer alerts activate when bins are full to prevent waste overflow. The design demonstrated that the waste classification satisfied the classification and completed testing by reducing the amount of time spent performing manual tasks. The existing system is also cost effective and easily expanded for the use of any number of applications including residential spaces, educational facilities and small-scale waste collection businesses.

References

  1. Hegde, Saumya, N. Sumith, Twensica Pinto, Shivam Shukla, and Vijay Patidar. "Optimizing solid waste management: A holistic approach by informed carbon emission reduction." IEEE Access 12 (2024): 121659-121674.
  2. Kiyokawa, Takuya, Jun Takamatsu, and Shigeki Koyanaka. "Challenges for future robotic sorters of mixed industrial waste: a survey." IEEE Transactions on Automation Science and Engineering 21, no. 1 (2022): 1023-1040.
  3. Hossen, Md Mosarrof, Molla E. Majid, Saad Bin Abul Kashem, Amith Khandakar, Mohammad Nashbat, Azad Ashraf, Mazhar Hasan-Zia, Ali K. Ansaruddin Kunju, Saidul Kabir, and Muhammad EH Chowdhury. "A reliable and robust deep learning model for effective recyclable waste classification." IEEE Access 12 (2024): 13809-13821.
  4. Mazanov, Gleb, Anna Iliushina, Sergey Nesteruk, Andrey Pimenov, Anton Stepanov, Nadezhda Mikhaylova, and Andrey Somov. "Hyperspectral data driven solid waste classification." IEEE Access (2025).
  5. Thielmann, Pelle, Yu Zhou, Bruno Mirbach, Didier Stricker, and Jason Rambach. "A Review of Computer Vision for Industrial-Grade Waste Classification." IEEE Access (2025).
  6. Procházka, Robert, Jan Valíček, Marta Harničárová, Milena Kušnerová, Hakan Tozan, Cristina Borzan, Milan Kadnár, Zuzana Palková, Roman Gálik, and Karolína Slamová. "Collection of plastic packaging of various types: Sorting of fractions of plastic waste using both automated and manual modes." IEEE Access 12 (2024): 44244-4426.
  7. Sosunova, Inna, and Jari Porras. "IoT-enabled smart waste management systems for smart cities: A systematic review." IEEE Access 10 (2022): 73326-73363.
  8. Tiwari, Sakshi, Snigdha Bisht, and Kanchan Sharma. "Intelligent waste management using wasteIQNet with hierarchical learning and Meta-Optimization." IEEE access (2025).
  9. Cicceri, Giovanni, Dario C. Guastella, Giuseppe Sutera, Francesco Cancelliere, Mario Vitti, Giovanni Randazzo, Salvatore DiStefano, and Giovanni Muscato. "An intelligent hierarchical cyber-physical system for beach waste management: The BIOBLU case study." IEEE Access 11 (2023): 134421-134445.
  10. Mewada, Dishant, Cathaoir Agnew, Eoin M. Grua, Ciarán Eising, Patrick Denny, Mark Heffernan, Ken Tierney, Pepijn van de Ven, and Anthony Scanlan. "Contamination detection from highly cluttered waste scenes using computer vision." IEEE Access 12 (2024): 129434-129446.
  11. Alourani, Abdullah, M. Usman Ashraf, and Mohammed Aloraini. "Smart waste management and classification system using advanced IoT and AI technologies." PeerJ Computer Science 11 (2025): e2777.
  12. Jerbi, Houssem, VG Anisha Gnana Vincy, Sondess Ben Aoun, Rabeh Abbassi, and Mourad Kchaou. "Optimizing waste management in smart Cities: An IoT-Based approach using dynamic bald eagle search optimization algorithm (DBESO) and machine learning." Journal of Urban Management (2025).
  13. Patel, Shrey, Harsh Oza, Yash Sharma, Tirth Patel, Smita Agrawal, and Parita Oza. "IoT-Enabled Smart Waste Management using Renewable Energy." Procedia Computer Science 259 (2025): 1973-1982.
  14. Lakhouit, Abderrahim. "Revolutionizing urban solid waste management with AI and IoT: A review of smart solutions for waste collection, sorting, and recycling." Results in Engineering 25 (2025): 104018.
  15. Alnanih, Reem, Lamiaa Elrefaei, and Ayman Al-Ahwal. "Advancing Sustainability Through an IoT-Driven Smart Waste Management System with Software Engineering Integration." Sustainability 17, no. 21 (2025): 9803