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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65390
Volume:
10 |
6 |
Jun 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
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.

Department of Electronics & Telecommunication
Sinhgad College of Engineering , Pune-41

Department of Electronics & Telecommunication
Sinhgad College of Engineering , Pune-41
Department of Electronics & Telecommunication
Sinhgad College of Engineering , Pune-41
Department of Electronics & Telecommunication
Sinhgad College of Engineering , Pune-41
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
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.
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Publication Date
6/24/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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