Authors : Vanithadevi K.; Dhanya Shree J.; Jeevitha R.; Deepika R.; Sofia M.

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/2sd95bte

DOI : https://doi.org/10.38124/ijisrt/26apr895

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This project introduces an advanced AI- integrated IoT framework for real-time food quality monitoring and intelligent preservation during storage and transportation. The system combines multiple sensing, vision, and control components to automate decision-making based on environmental and spoilage indicators. The primary sensorsDHT22 (temperature & humidity), pH sensor (surface acidity for spoilage), and MQ-135 gas sensor (CO₂, ammonia)interface with an ESP32 microcontroller for edge-level data acquisition. Sensor data is cross-validated with a curated dataset of fruit and perishable conditions. Based on pre-trained AI models, the system classifies food items as Fresh, Warning, or Spoiled. Simultaneously, a Raspberry Pi module processes visual data from an onboard camera, capturing periodic images of food. These images are pre- processed (e.g., colour correction, noise reduction) and analyzed via CNN-based image classification to detect discoloration, Mold, or deformities. A pneumatic spray system is triggered conditionally to apply food-safe preservatives, such as Sodium Benzoate, and a UV-C light sterilization module is activated when microbial contamination risk is high. The system also pushes data to the ThingSpeak cloud for visualization, alert generation, and remote decision control. Real-time alerts are sent via email/SMS to the supply chain manager. The combined use of sensor fusion, machine learning, edge computing, and vision-based diagnostics reduces spoilage, enhances transparency, and ensures high-quality food delivery in cold chain logistics and smart agriculture.

Keywords : Food Spoilage Detection, Pneumatic Sprayer, Raspberry Pi, ESP32, IoT, Gas Sensor.

References :

1. Krishnan, P., Ebenezar, U. S., Ranitha, R., Purushotham, N., & Balakrishnan, T. S. (2024, March). AI-driven intelligent IoT systems for real-time food quality monitoring and analysis. In 2024 International conference on trends in quantum computing and emerging business technologies (pp. 1-5).
2. IEEE.Kim, Wan-Soo, Won-Suk Lee, and Yong-Joo Kim. "A review of the applications of the internet of things (IoT) for agricultural automation." Journal of Biosystems Engineering 45, no. 4 (2020): 385-400.
3. Kolikipogu, R., Shivaputra, Muniyandy, E., Maroor, J.P., Lakshmi, G.V.R., Konduri, B. and Naveenkumar, R., 2025. Improving food safety by IoT-based climate monitoring and control systems for food processing plants. Remote Sensing in Earth Systems Sciences, 8(2), pp.387-399.
4. Padhiary, Mrutyunjay, Sunny V. Tikute, DebapamSaha, Javed Akhtar Barbhuiya, and Laxmi Narayan Sethi. "Development of an IOT-based semi-autonomous vehicle sprayer." Agricultural Research 14, no. 1(2025): 229-239.
5. Vedantam, K.S., Jain, S.K., Panwar, N.L., Sunil, J., Wadhawan, N. and Kumar, A., 2024. Emergence of Internet of Things technology     in food and agricultural sector: A review. Journal of Food Process Engineering, 47(8), p.e14698.
6. Mishra, Nikita, S. K. Jain, N. Agrawal, N. K. Jain, Nikita Wadhawan, and N. L. Panwar. "Development of drying system by using
7. Aamer, A.M., Al-Awlaqi, M.A. and Rausyan Fikri, M., 2025. Smart food logistics: design and test of an IoT- based food traceability system. International Journal of Logistics Research and Applications, pp.1-20.
8. Radogna, A.V., Latino, M.E., Menegoli, M., Prontera, C.T., Morgante, G., Mongelli, D., Giampetruzzi, L., Corallo, A., Bondavalli, A. and Francioso, L., 2022. A monitoring framework with integrated sensing technologies for enhanced food safety and traceability. Sensors, 22(17), p.6509.
9. Nagarajan, Senthil Murugan, Ganesh Gopal Deverajan, Puspita Chatterjee, Waleed Alnumay, and V. Muthukumaran. "Integration of IoT based routing process for food supply chain management in sustainable smart cities." Sustainable Cities and Society 76 (2022): 103448.
10. Weiming, Su, and A. Yahaya. "An IoT- Driven architectural framework for a food quality monitoring and safety management 2system." Front Soc Sci Technol 6 (2024): 74-78.