Ripe or Rotten? Low-Cost Produce Quality Estimation using Reflective Green Light Sensing

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http://hdl.handle.net/10138/333296

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Zuniga Corrales , A , Flores , H & Nurmi , P 2021 , ' Ripe or Rotten? Low-Cost Produce Quality Estimation using Reflective Green Light Sensing ' , IEEE Pervasive Computing , vol. 20 , no. 3 , pp. 60 - 67 . https://doi.org/10.1109/MPRV.2021.3074474

Title: Ripe or Rotten? Low-Cost Produce Quality Estimation using Reflective Green Light Sensing
Author: Zuniga Corrales, Agustin; Flores, Huber; Nurmi, Petteri
Contributor organization: Department of Computer Science
Date: 2021-07
Language: eng
Number of pages: 8
Belongs to series: IEEE Pervasive Computing
ISSN: 1536-1268
DOI: https://doi.org/10.1109/MPRV.2021.3074474
URI: http://hdl.handle.net/10138/333296
Abstract: We develop an innovative low-cost approach for characterizing fresh produce by repurposing inexpensive commercial-off-the-shelf green light sensors for quality estimation. Our approach has been designed to support all stages of the supply chain while being inexpensive and easy to deploy. We validate our approach through extensive empirical benchmarks, showing that it can correctly distinguish organic produce from nonorganic items, establish unique fingerprints for different produce, and estimate the quality or ripeness of produce. Specifically, we demonstrate that changes in the reflected green light values correlate with the so-called transpiration coefficients of the produce. We also discuss the practicability of our approach and present application use cases that can benefit from our solution.
Subject: 113 Computer and information sciences
Sensors
Green products
Supply chains
Skin
Temperature measurement
Sensor phenomena and characterization
Power measurement
SYSTEM
TIME
Peer reviewed: Yes
Usage restriction: openAccess
Self-archived version: acceptedVersion


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