PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 15, 2026International Journal of Versatile Research and Analysis0 citationsOpen Access

E Nose Fruit for Ripening Detection

View Full Paper
MGMiss Shravani Abhay GardeMGMiss. Anuja Bhagwan GhatageMKMiss. Karuna Dilip Kamble

Key Points

  • The research aims to develop a non-destructive system for detecting fruit ripening stages using gas sensing technology.
  • Developed an electronic nose system using MQ-135, MQ-3, and VOC sensors.
  • Utilized an Arduino Uno microcontroller for data processing.
  • Classified fruits into unripe, ripe, and overripe based on VOC detection.
  • Displayed data on a 16×2 LCD screen.
  • The system accurately detects fruit ripeness stages in real-time.
  • Demonstrated faster and cost-effective detection compared to conventional methods.
  • Reduced post-harvest losses and improved decision-making in agriculture.

Abstract

Fruit ripening detection plays a crucial role in agriculture, storage, and supply chain management. Conventional methods such as visual inspection and chemical analysis are often subjective, time-consuming, and destructive. This research presents the development of an Electronic Nose (E-Nose) system for non-destructive and real-time detection of fruit ripening stages. The system utilizes gas sensors such as MQ-135, MQ-3, and a VOC sensor to detect volatile organic compounds (VOCs) like ammonia, ethanol, and ethylene released during fruit ripening. The sensor data is processed using an Arduino Uno microcontroller and displayed on a 16×2 LCD screen. The system classifies fruits into unripe, ripe, and overripe categories based on predefined thresholds. Experimental results demonstrate that the proposed system provides accurate, fast, and cost-effective ripeness detection. This technology can significantly reduce post-harvest losses and improve decision-making in agriculture and food industries.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Garde et al. (2026) studied this question.

synapsesocial.com/papers/69df2b2ce4eeef8a2a6b0163https://doi.org/10.56975/ijvra.v4i4.703819
Ask AI
Helpful
Bookmark
Share
View Full Paper