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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM65390

Smart Ethylene Management System : IOT Based Prevention of Premature and Artificial Ripening in Fruits

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65390

Volume:

10 |

6 |

Jun 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

Pratik Balasaheb Chavan

Pratik Balasaheb Chavan

Department of Electronics & Telecommunication

Sinhgad College of Engineering , Pune-41

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Komal Shailesh Ayya

Komal Shailesh Ayya

Department of Electronics & Telecommunication

Sinhgad College of Engineering , Pune-41

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Anushka Balasaheb Gaikwad

Anushka Balasaheb Gaikwad

Department of Electronics & Telecommunication

Sinhgad College of Engineering , Pune-41

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Mentors

P.D

Prof .Prashant Dahale

Department of Electronics & Telecommunication

Sinhgad College of Engineering , Pune-41

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Abstract

Fruit ripening is a critical post-harvest process that directly impacts the quality, market value, and shelf life of fruits. Traditional ripening methods, such as manual application of ethylene gas or chemical agents like calcium carbide, often result in uneven ripening, poor quality, and potential health hazards. Additionally, improper control of environmental parameters such as temperature and humidity leads to over-ripening, spoilage, and economic losses. To address these challenges, the proposed project—Automated Fruit Ripening, Quality Detection, and Sorting System integrates Internet of Things (IoT) and Machine Learning (ML) technologies to create an intelligent, automated solution for fruit ripening management. Using an ESP32 microcontroller with embedded sensors, the system monitors key parameters including ethylene gas concentration, temperature, humidity, color, and weight. The data is processed through a trained ML model to classify the ripening stage as unripe, semi-ripe, ripe, or overripe. The results are displayed on an LCD and transmitted to a cloud platform for remote monitoring via a mobile application. This approach enhances precision, ensures fruit quality, reduces manual labor, and supports sustainable agricultural practices through automation and data-driven decision-making.

KEYWORDS

Internet of Things (IoT) Machine Learning (ML) ESP32 microcontroller ML model

Cite This Article

Pratik Balasaheb Chavan, Komal Shailesh Ayya, Anushka Balasaheb Gaikwad, Prof .Prashant Dahale, "Smart Ethylene Management System : IOT Based Prevention of Premature and Artificial Ripening in Fruits," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-7, Jun 2026.

Full Article/Conclusion

The IoT-based system for preventing faster ripening of fruits and vegetables successfully monitors and controls the key environmental factors ethylene gas concentration, temperature, humidity, and moisture that influence the ripening process. Through the integration of the ESP32 microcontroller with various sensors (MQ2 gas sensor, DHT sensor, moisture sensor, and load sensor), the system automates the management of fruit storage conditions in real-time. The project demonstrates that: Ethylene Gas Management: The system effectively detects increased ethylene levels using the MQ2 sensor and, in response, automatically activates the DC pump to spray an ethylene absorbent. This action helps slow down the ripening process and prolongs the shelf life of the produce.   Environmental Control: When temperature, humidity, or moisture exceed safe thresholds, the system triggers the DC fan to ensure proper air circulation, maintaining optimal conditions within the storage container. Real-Time Monitoring and Control: All sensor data are displayed on the LCD and transmitted to the Blynk IoT platform, allowing for remote monitoring and control via a smartphone. Alerts are generated in case of abnormal conditions, providing real-time updates to the user.   Overall, the system achieves its goal of reducing post-harvest losses due to premature ripening. By automating the monitoring and response to environmental changes, the system minimizes human intervention and enhances efficiency in the storage and transportation of fruits and vegetables. This solution offers a cost-effective, energy-efficient, and user-friendly approach to extending the shelf life of produce, making it highly suitable for use in agriculture and food storage industries.

References

[1]                    A. Ravindran, R. Anitha, and R. Ajith, “A Review on Non-Destructive Techniques for Evaluating Quality of Fruits,” International Journal of Engineering Research & Technology (IJERT), vol. 4, no. 9, pp. 1–6, 2015. [2]      V. Srividhya, K. Sujatha, and R. S. Ponmagal, “Ethylene Gas Measurement for Ripening of Fruits Using Image Processing,” Indian Journal of Science and Technology, vol. 9, no. 31, 2016. [3]      X. Zhang, Y. Li, T. Hong, S. Tegeltija, et al., “Response Characteristics Study of Ethylene Sensor for Fruit Ripening under Temperature Control,” Sensors, vol. 23, no. 11, p. 5203, 2023. [4]      M. Zhao, Z. You, H. Chen, X. Wang, Y. Ying, and Y. Wang, “Integrated Fruit Ripeness Assessment System Based on an Artificial Olfactory Sensor and Deep Learning,” Foods, vol. 13, no. 5, p. 793, 2024. [5]      R. Mohan and K. Sreekumar, “A Literature Review on Machine Vision-Based Approaches for Ripeness Detection of Fruits,” International Journal of Computer Applications (IJCA), vol. 182, no. 48, pp. 22–28, 2019. [6]      S. N. Karlapudi, R. P. Pusapati, and A. G. Nadimpalli, “Fruit Ripeness Detection Using CNN Model,” International Journal of Scientific Research in Engineering and Management (IJSREM), vol. 9, no. 1, pp. 12–18, 2025. [7]      V. Tejas Kumar, S. N. Sagar, and N. A. Patil, “Smart Fruit Ripeness Detection Integrating Image Processing and Temperature Sensing Technologies,” International Journal of Engineering Research & Technology (IJERT), vol. 12, no. 5, pp. 150–156, 2023. [8]      S. Tan and J. Yang, “Object Sensing for Fruit Ripeness Detection Using WiFi Signals,” arXiv preprint,  arXiv:2106.00860,  2021.  [Online]. Available: https://arxiv.org/abs/2106.00860 [9]      S. C. Bawlekar, “Convolutional Neural Network Based Ripeness Detection of Fruits,” Research & Reviews: Journal of Agricultural Science and Technology (RRJoAST), vol. 13, no. 2, pp. 40–47, 2024.  [10] S. S. Patil, V. V. Kholkumbe, M. A. Patil, N. B. Chavan, and D. A. Patil, “Automated Sensor-Based Artificial Ripening System Using Arduino and IoT,” International Journal of Research in Engineering, Science and Management (IJRESM), vol. 6, no. 5, pp. 123–128, 2023.

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Publication Date

6/24/2026

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Review Rounds: 2

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