An Affordable and Sustainable Efficient Color Sorting System Using Arduino and TCS3200 Sensor
Authors
Mr. Mayur Chavda
Assistant Professor, Department of Mechatronics Engineering, ITM Vocational University, Vadodara (IN)
Anurag Giri
UG Student, Department of Mechatronics Engineering, ITM Vocational University, Vadodara (IN)
Sagar Sandhe
UG Student, Department of Mechatronics Engineering, ITM Vocational University, Vadodara (IN)
Rudra Joshi
UG Student, Department of Mechatronics Engineering, ITM Vocational University, Vadodara (IN)
Ms. Apexa Purohit
Assistant Professor, Department of Mechatronics Engineering, ITM Vocational University, Vadodara (IN)
Dr. Anil M. Bisen
Provost, Professor, Department of Mechanical Engineering, ITM Vocational University, Vadodara (IN)
Dr. Mayank Dev Singh
Associate Professor, Department of Mechatronics Engineering, ITM Vocational University, Vadodara (IN)
Dr. Jai Bahadur Balwanshi
Professor, Department of Mechanical Engineering, ITM Vocational University, Vadodara (IN)
Article Information
DOI: 10.51583/IJLTEMAS.2026.150100004
Subject Category: Inventory Management
Volume/Issue: 15/1 | Page No: 39-54
Publication Timeline
Submitted: 2026-01-22
Published: 2026-01-22
Abstract
In the era of rapid industrialization, automation plays a vital role in enhancing efficiency and accuracy in manufacturing processes. This project presents the design and implementation of an Arduino-based color sorting system that automates the sorting of objects based on their colors. The system uses an infrared (IR) sensor to detect the presence of objects on a conveyor belt and a TCS3200 color sensor to identify their color. The Arduino microcontroller processes the sensor data and controls a servo motor that directs objects to designated bins according to their detected color red, green, or blue. This automated solution offers an affordable and effective alternative to expensive industrial sorting machines, making it suitable for small-scale industries and educational purposes. The system also addresses challenges such as sensor calibration and ambient light variations to ensure accurate color detection and sorting. Testing under various conditions demonstrates the system’s reliability, accuracy, and efficiency, highlighting its potential for wider adoption in automation and prototyping.
Keywords
Automation, TCS3200 color sensor, infrared sensor, servo motor, conveyor belt, microcontroller, object detection, low-cost automation
Downloads
References
1. Johnson, P., & Lee, K. (2015). Machine vision for industrial sorting: Advances and challenges. IEEE Transactions on Automation Science and Engineering, 12(4), 1340–1350. https://doi.org/10.1109/TASE.2015.2441234 [Google Scholar] [Crossref]
2. Patel, S., & Kumar, R. (2017). Optical color sensors for industrial applications. Optical Engineering, 56(6), 066101. https://doi.org/10.1117/1.OE.56.6.066101 [Google Scholar] [Crossref]
3. Zhao, L., Sun, H., & Chen, J. (2020). Adaptive algorithms for color sensor calibration in automated systems. Sensors and Actuators A: Physical, 305, 111962. https://doi.org/10.1016/j.sna.2020.111962 [Google Scholar] [Crossref]
4. Wang, Y., & Li, Z. (2022). Machine learning in automated sorting: Techniques and applications. Journal of Intelligent Manufacturing, 33(7), 2001–2015. https://doi.org/10.1007/s10845-021-01812-7 [Google Scholar] [Crossref]
5. Brown, T., & Green, J. (2018). Arduino-based automation systems in engineering education. Journal of Engineering Education, 107(2), 123–130. https://doi.org/10.1002/jee.2021 [Google Scholar] [Crossref]
6. Alrushidy, I., Alshammari, A., & Alshehri, M. (2025). Design and fabrication of a color sorting machine based on computer vision. Journal of Science and Technology, 30(6), 1–10. https://doi.org/10.20428/jst.v30i6.2954 [Google Scholar] [Crossref]
7. Ong, Z. C. (2024). Towards Industry 4.0: Color-based object sorting using a robotic arm and real-time detection. Industrial Management Advances, 1(1), 1–12. https://doi.org/10.59429/ima.v1i1.125 [Google Scholar] [Crossref]
8. Sanwar, S., & Ahmed, M. I. (2023). Automated object sorting system using real-time image processing and robotic gripper control. Journal of Engineering Advancements, 3(3), 45–53. https://doi.org/10.38032/jea.2023.03.003 [Google Scholar] [Crossref]
9. Hou, L., Zhang, Y., Liu, Q., & Wang, J. (2024). Tomato sorting system based on machine vision technology. Electronics, 13(11), 2114. https://doi.org/10.3390/electronics13112114 [Google Scholar] [Crossref]
10. Aryeni, I., Maulidiah, H. M., & Nugroho, A. (2023). Real-time fruit color and size detection using computer vision for sorting systems. Journal of Applied Electrical Engineering, 7(2), 61–66. https://doi.org/10.30871/jaee.v7i2.6740 [Google Scholar] [Crossref]
11. Umaru, K., Abdullahi, M., & Musa, S. (2025). PLC-based speed control in automated color sorting systems. Journal of Engineering, Technology, and Applied Science, 7(1), 37–51. https://doi.org/10.36079/lamintang.jetas-0701.828 [Google Scholar] [Crossref]
12. Kumar, N., Sharma, P., & Verma, R. (2025). Development of a smart sorting and counting machine for industrial applications. Journal of Advanced Zoology, 44(4), 3270–3278. https://doi.org/10.53555/jaz.v44i4.3270 [Google Scholar] [Crossref]
13. Singh, A., & Mishra, R. (2019). Conveyor-based material handling systems: Design and analysis. International Journal of Mechanical Engineering, 6(2), 85–92. https://doi.org/10.1016/j.ijme.2019.02.004 [Google Scholar] [Crossref]
14. Lee, S., & Park, J. (2020). Vision-based object recognition for industrial automation. International Journal of Advanced Manufacturing Technology, 108(1), 237–248. https://doi.org/10.1007/s00170-020-05422-1 [Google Scholar] [Crossref]
15. Chen, X., & Huang, Y. (2018). Color feature extraction techniques for real-time vision systems. Pattern Recognition Letters, 112, 76–83. https://doi.org/10.1016/j.patrec.2018.07.014 [Google Scholar] [Crossref]
16. Rahman, M., & Islam, T. (2021). Design of low-cost automated sorting systems using embedded controllers. International Journal of Automation Technology, 15(3), 402–410. https://doi.org/10.20965/ijat.2021.p0402 [Google Scholar] [Crossref]
17. Gupta, V., & Bansal, P. (2022). Embedded systems for industrial automation and robotics. Microsystem Technologies, 28(9), 3057–3066. https://doi.org/10.1007/s00542-022-05431-9 [Google Scholar] [Crossref]
18. Kim, H., & Lee, D. (2019). Real-time image processing for object classification in manufacturing. Journal of Manufacturing Systems, 52, 13–21. https://doi.org/10.1016/j.jmsy.2019.04.003 [Google Scholar] [Crossref]
19. Rodrigues, J., & Silva, F. (2020). Automated inspection and sorting using computer vision systems. Measurement, 152, 107317. https://doi.org/10.1016/j.measurement.2019.107317 [Google Scholar] [Crossref]
20. Mehta, R., & Patel, D. (2021). Design and implementation of color-based sorting robots. International Journal of Robotics and Automation, 36(4), 215–223. https://doi.org/10.2316/J.2021.206-0385 [Google Scholar] [Crossref]
21. Liu, Y., & Zhou, X. (2018). Industrial applications of machine vision-based sorting. Sensors, 18(9), 3051. https://doi.org/10.3390/s18093051 [Google Scholar] [Crossref]
22. Verma, S., & Jain, A. (2023). Smart automation systems using vision sensors for manufacturing. Journal of Manufacturing Processes, 86, 124–132. https://doi.org/10.1016/j.jmapro.2023.01.012 [Google Scholar] [Crossref]
23. Torres, M., & Alvarez, J. (2022). Design of intelligent material sorting systems using artificial intelligence. Applied Sciences, 12(14), 7064. https://doi.org/10.3390/app12147064 [Google Scholar] [Crossref]
24. Design and Development of a Radio-Controlled Aircraft and Concept of Electric Vertical Takeoff and Landing (eVTOL). (2025). International Journal of Latest Technology in Engineering Management & Applied Science, 14(10), 386-391. https://doi.org/10.51583/IJLTEMAS.2025.1410000049 [Google Scholar] [Crossref]
25. Development and Evaluation of a Cost-Effective Desktop 3D Printing System for Rapid Prototyping. (2025). International Journal of Latest Technology in Engineering Management & Applied Science, 14(6), 500-508. https://doi.org/10.51583/IJLTEMAS.2025.140600054 [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Predictive Health Monitoring Systems for Electric Vehicle Powertrains Using Edge AI and CAN Bus Data
- Internship Portals: A Systematic Review of Current Platforms and Future Directions
- Towards Better Urban Mobility: A Comprehensive Assessment of Pedestrian Infrastructure in Naval, Biliran Province, Philippines
- Financial Stress and Mobility Patterns: Implication for Transportation Policy Among Jeepney Passengers
- Evaluating the Effectiveness of Marketing Strategies on Customer Visit Intentions: Evidence from Local Restaurants in Boac, Marinduque