Revolutionary IoT Cold Chain Monitoring for Freshness Preservation

  • Authors

    • Sarankumar R Professor, Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamilnadu, India
    • Chairman M Assistant Professor, Department of Electronics and Communication Engineering Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India
    • Sooriya Prabha S Associate Professor, Department of Electrical and Electronics Engineering, Mother Theresa Institute of Engineering and Technology, Palamaner, Andhra Pradesh, India
    • Manojkumar B Assistant Professor, Department of Electronics and Communication Engineering, KGiSL Institute of Technology, Coimbatore, Tamilnadu, India
    https://doi.org/10.14419/h8xbdg96

    Received date: June 19, 2025

    Accepted date: August 18, 2025

    Published date: September 2, 2025

  • Blynk IoT, ESP32microcontroller, Humidifier, Perishable Goods, Real-Time Monitoring, Sensors, Supply Chain Management, ThingSpeak Cloud platform
  • Abstract

    Cold chain development is essential for preserving perishable agricultural products and reducing post-harvest losses. An estimated one-third of the food produced for human consumption is lost or wasted worldwide, resulting in significant negative environmental and economic impacts. This article highlights cutting-edge technologies that improve cold chain efficiency, focusing on key elements of a successful cold chain, including distribution, transportation, and storage. A properly established cold chain can enhance food security, lower waste, and promote sustainable agricultural practices by guaranteeing the integrity of food products from farm to table.

  • References

    1. Feng, H., Fan, J., Ji, Y., Glamuzina, B., & Ma, R. (2024). Reliable Quality Traceability for Tilapia Cold Chain Using Blockchain and Machine Learning Techniques. Journal of food process engineering, 47(12), e70016. https://doi.org/10.1111/jfpe.70016
    2. Lin Bai ,Minghao Liu ,Ying Sun “Overview of Food Preservation and Traceability Technology in the Smart Cold Chain System”.29 July 2023. https://doi.org/10.3390/foods12152881
    3. Huaixia Shi,Qinglei Zhang, Jiyun Qin “Cold Chain Logistics and Joint Distribution: A Review of Fresh Logistics Modes”.22 July 2024. https://doi.org/10.3390/systems12070264
    4. Kresimir Buntak,Nikola Biskup,Matija Kovacic “The importance of risk management in the cold supply chain”,September 11, 2023. https://www.researchgate.net/profile/Matija-Kovacic/publication/379828432_The_importance_of_risk_management_in_the_cold_supply_chain/links/661d269443f8df018d0e3cc9/The-importance-of-risk-management-in-the-cold-supply-chain.pdf
    5. Kim, T. H., Kim, J. H., Kim, J. Y., & Oh, S. E. (2022). Egg freshness prediction model using real-time cold chain storage condition based on trans-fer learning. Foods, 11(19), 3082. https://doi.org/10.3390/foods11193082
    6. Loisel, J., Cornuéjols, A., Laguerre, O., Tardet, M., Cagnon, D., de Lamotte, O. D., & Duret, S. (2022). Machine learning for temperature predic-tion in food pallet along a cold chain: Comparison between synthetic and experimental training dataset. Journal of Food Engineering, 335, 111156. https://doi.org/10.1016/j.jfoodeng.2022.111156
    7. Mohd Hafidz Mahamad Maifiah, Anis Najiha Ahmad, Muhammad Affifuddin Iskandar, Md Siddique E Azam “Identifying Barriers to Efficient Cold Chain Management in the Halal Food Industry”. 15 February 2025. https://doi.org/10.55057/ijbtm.2025.7.1.8
    8. Kavididevi, V., Monikapreethi, S. K., Rajapriya, M., Juliet, P. S., Yuvaraj, S., & Muthulekshmi, M. (2024, July). IoT-Enabled Reinforcement Learning for Enhanced Cold Chain Logistics Performance in Refrigerated Transport. In 2024 2nd International Conference on Sustainable Compu-ting and Smart Systems (ICSCSS) (pp. 379-384). IEEE. https://doi.org/10.1109/ICSCSS60660.2024.10624822
    9. Babu, D. R., Sengupta, R., Rao, K. N., Desai, U., & Chauhan, S. (2024). Machine Learning-Based Remote Monitoring and Predictive Analytics System for Apple Harvest Storage: A Statistical Model Based Approach. In Computational Intelligence in Internet of Agricultural Things (pp. 49-77). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-67450-1_3
    10. Taguta, J., Nturambirwe, J. F. I., & Nyirenda, C. N. (2025, May). Comparative Evaluation of Machine Learning Models for Predicting Fresh Pro-duce Cold Chain Temperature: A Case of South African Apples. In 2025 IST-Africa Conference (IST-Africa) (pp. 1-10). IEEE. https://doi.org/10.23919/IST-Africa67297.2025.11060484
    11. Wentao Huang , Xuepei Wang, Junchang Zhang , Jie Xia , Xiaoshuan Zhang , " Improvement of blueberry freshness prediction based on machine learning and multi-source sensing in the cold chain logistics " Food Control Volume 145, March 2023, 109496. https://doi.org/10.1016/j.foodcont.2022.109496
    12. Khanuja, G. S., Sharath, D. H., Nandyala, S., & Palaniyandi, B. (2018). Cold chain management using model based design, machine learning algo-rithms and data analytics (No. 2018-01-1201). SAE Technical Paper. https://doi.org/10.4271/2018-01-1201
    13. Kale, S. D., & Patil, S. C. (2020). Need for predictive data analytics in cold chain management. In Advances in VLSI and Embedded Systems: Se-lect Proceedings of AVES 2019 (pp. 115-129). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-15-6229-7_9
    14. Aljohani, A. (2023). Predictive analytics and machine learning for real-time supply chain risk mitigation and agility. Sustainability, 15(20), 15088. https://doi.org/10.3390/su152015088
  • Downloads

  • How to Cite

    R, S. ., M , C. ., S , S. P., & B, M. (2025). Revolutionary IoT Cold Chain Monitoring for Freshness Preservation. International Journal of Basic and Applied Sciences, 14(5), 76-82. https://doi.org/10.14419/h8xbdg96