A Low-Cost IoT-Based Gas Sensor Approach for Real-time Monitoring of Mango Fruit Ripening and Spoilage in Storage Conditions
C. M. Babu, G. Rajender, K. C. Kumar, C. Swathi, B. Anvesh, B. Ajay, G. Divyanjali
🤖 gxceed AI 要約
日本語
本研究は、マンゴーのエチレンガス濃度をリアルタイムで監視する低コストIoTセンサーシステムを開発。ケサール種とバンガナパリ種で検証し、エチレン濃度と熟成段階の相関を確認。最高濃度はそれぞれ213 ppmと241 ppm。冷蔵倉庫管理やサプライチェーンでの品質保証への応用が期待される。
English
This study presents a low-cost IoT gas sensor system (≈24 USD) for real-time monitoring of mango ripening via ethylene detection. Tested on Kesar and Banganapalli varieties, it recorded peak ethylene concentrations of 213 ppm and 241 ppm. The system enables early spoilage detection for warehouse and cold-chain management, reducing post-harvest losses.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本論文は脱炭素を直接扱うものではないが、食品ロス削減は間接的にGXに貢献する。日本でも青果物の物流効率化は課題であり、センサー技術の適用事例として参考になる可能性がある。
In the global GX context
While not directly about decarbonization, reducing food waste through better monitoring contributes to sustainability. The paper offers a practical low-cost sensor design applicable to global agricultural supply chains, relevant for companies seeking Scope 3 waste reduction.
👥 読者別の含意
🔬研究者:Provides a practical sensor implementation for ethylene monitoring in perishable supply chains.
🏢実務担当者:Useful for warehouse managers and cold-chain operators looking for affordable spoilage detection.
🏛政策担当者:Could inform policies on food loss reduction technology adoption.
📄 Abstract（原文）
Post-harvest losses of perishable fruits due to improper storage and delayed spoilage detection remain a major challenge in the food supply chain. This study presents the design and development of a low-cost (~INR 2000, ≈24 USD), IoT-enabled gas sensor system for real-time monitoring of mango fruit freshness based on ethylene emission. The system integrates an Arduino Uno microcontroller with MQ-3 gas sensor, a DHT11 temperature–humidity sensor, and an infrared (IR) module for automated detection and analysis. Mango varieties Kesar and Banganapalli were used as model fruits to evaluate the system performance. Experimental results demonstrated a clear correlation between ethylene concentration and ripening stages. The highest ethylene concentration was recorded as 213 ppm and 241 ppm for Kesar and Benganapalli mangoes respectively. The developed system provides continuous monitoring and displays real-time data, enabling early detection of spoilage. This approach offers a cost-effective solution for warehouse management, reducing post-harvest losses and improving food quality assurance. Industrial applications include real-time monitoring of fruit storage warehouses and cold-chain logistics systems to ensure optimal freshness and reduce economic losses.It can also be deployed in food export industries and supermarket supply chains for automated quality control and spoilage prevention.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.9734/ejnfs/2026/v18i52037first seen 2026-05-05 22:24:45
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