Access the full text.
Sign up today, get DeepDyve free for 14 days.
Frida Femling, Adam Olsson, F. Alonso-Fernandez (2018)
Fruit and Vegetable Identification Using Machine Learning for Retail Applications2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)
J. Tyo, Dennis Goldstein, D. Chenault, J. Shaw (2006)
Review of passive imaging polarimetry for remote sensing applications.Applied optics, 45 22
(2021)
Revision of conference proceedings manuscripts for journal submission
Riley Logan, Bryan Scherrer, Jacob Senecal, Neil Walton, A. Peerlinck, John Sheppard, J. Shaw (2020)
Hyperspectral imaging and machine learning for monitoring produce ripeness, 11421
A. Peirs, J. Lammertyn, K. Ooms, B. Nicolai (2001)
Prediction of the optimal picking date of different apple cultivars by means of VIS/NIR-spectroscopyPostharvest Biology and Technology, 21
Riley Logan, J. Shaw (2020)
Measuring the polarization response of a VNIR hyperspectral imager, 11412
D. Slaughter, J. Abbott (2015)
Analysis of Fruits and Vegetables
Jacob Senecal, John Sheppard, J. Shaw (2019)
Efficient Convolutional Neural Networks for Multi-Spectral Image Classification2019 International Joint Conference on Neural Networks (IJCNN)
G. Polder, G. Heijden, I. Young (2003)
Tomato sorting using independent component analysis on spectral imagesReal Time Imaging, 9
O. Gupta, Anshuman Das, Joshua Hellerstein, R. Raskar (2018)
Machine learning approaches for large scale classification of produceScientific Reports, 8
Nashwa El-Bendary, Esraa Elhariri, A. Hassanien, A. Badr (2015)
Using machine learning techniques for evaluating tomato ripenessExpert Syst. Appl., 42
G. Polder, G. Heijden, I. Young (2000)
Hyperspectral image analysis for measuring ripeness of tomatoes.
J. Buzby, Hodan Farah-Wells, J. Hyman (2014)
The Estimated Amount, Value, and Calories of Postharvest Food Losses at the Retail and Consumer Levels in the United StatesAgricultural & Natural Resource Economics eJournal
M. Melikoğlu, C. Lin, C. Webb (2013)
Analysing global food waste problem: pinpointing the facts and estimating the energy contentCentral European Journal of Engineering, 3
Laura Grunenfelder, L. Hiller, N. Knowles (2006)
Color indices for the assessment of chlorophyll development and greening of fresh market potatoesPostharvest Biology and Technology, 40
Wilson Castro, Jimy Oblitas, Miguel De-la-Torre, C. Cotrina, Karen Bazán, H. Avila-George (2019)
Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color SpacesIEEE Access, 7
D. Laval-Martin, Joélle Quennemet, R. Monéger (1975)
Pigment evolution in Lycopersicon esculentum fruits during growth and ripeningPhytochemistry, 14
V. Prasanna, T. Prabha, R. Tharanathan (2007)
Fruit Ripening Phenomena–An OverviewCritical Reviews in Food Science and Nutrition, 47
R. Pandey, Sapan Naik, Roma Marfatia (2013)
Image Processing and Machine Learning for Automated Fruit Grading System: A Technical ReviewInternational Journal of Computer Applications, 81
J. Gustavsson, C. Cederberg, U. Sonesson, R. Otterdijk, A. Meybeck (2011)
Global food losses and food waste: extent, causes and prevention
P. Rajkumar, Ning-ning Wang, G. EImasry, G. Raghavan, Y. Gariépy (2012)
Studies on banana fruit quality and maturity stages using hyperspectral imagingJournal of Food Engineering, 108
D. Heiss-Czedik (1997)
An Introduction to Genetic Algorithms.Artificial Life, 3
Harshad Vaviya, V. Vishwakarma, Ajaykumar Yadav, N. Shah (2019)
Identification of Artificially Ripened Fruits Using Machine LearningSSRN Electronic Journal
M. Kim, Y. Chen, P. Mehl (2001)
HYPERSPECTRAL REFLECTANCE AND FLUORESCENCE IMAGING SYSTEM FOR FOOD QUALITY AND SAFETYTransactions of the ASABE, 44
(2019)
Using a genetic algorithm with histogrambased feature selection in hyperspectral image classification
F. Iandola (2016)
Exploring the Design Space of Deep Convolutional Neural Networks at Large ScaleArXiv, abs/1612.06519
J. Abbott (1999)
Quality measurement of fruits and vegetablesPostharvest Biology and Technology, 15
Ron Kohavi, George John (1997)
Wrappers for Feature Subset SelectionArtif. Intell., 97
Esraa Elhariri, Nashwa El-Bendary, A. Hussein, A. Hassanien, A. Badr (2014)
Bell pepper ripeness classification based on support vector machine2014 International Conference on Engineering and Technology (ICET)
(2012)
Wasted: how America is losing up to 40 percent of its food from farm to fork to landfill
K. Venkat (2011)
The Climate Change and Economic Impacts of Food Waste in the United StatesInternational Journal on Food System Dynamics, 2
P. Luning, Theo Rijk, H. Wichers, J. Roozen (1994)
Gas chromatography, mass spectrometry, and sniffing port analyses of volatile compounds of fresh bell peppers (Capsicum annuum) at different ripening stages.Journal of Agricultural and Food Chemistry, 42
(2019)
Utilizing distributions of variable influence for feature selection in hyperspectral images
Jihoon Yang, Vasant Honavar (1998)
Feature Subset Selection Using a Genetic AlgorithmIEEE Intell. Syst., 13
Nayeli Rivera, J. Gómez-Sanchís, J. Chanona-Pérez, J. Carrasco, M. Millán-Giraldo, D. Lorente, S. Cubero, J. Blasco (2014)
Early detection of mechanical damage in mango using NIR hyperspectral images and machine learningBiosystems Engineering, 122
P. Nugent, J. Shaw, P. Jha, Bryan Scherrer, A. Donelick, Vipan Kumar (2018)
Discrimination of herbicide-resistant kochia with hyperspectral imagingJournal of Applied Remote Sensing, 12
M. Eismann (2014)
Natural Synergy of Conferences and JournalsOptical Engineering, 53
J. Bower, J. Cutting (2011)
Avocado Fruit Development and Ripening Physiology
S. Cubero, N. Aleixos, E. Moltó, J. Gómez-Sanchís, J. Blasco (2011)
Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and VegetablesFood and Bioprocess Technology, 4
Kaiming He, X. Zhang, Shaoqing Ren, Jian Sun (2015)
Deep Residual Learning for Image Recognition2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
K. Hall, Juen Guo, Michael Dore, C. Chow (2009)
The Progressive Increase of Food Waste in America and Its Environmental ImpactPLoS ONE, 4
Bryan Scherrer, John Sheppard, P. Jha, J. Shaw (2019)
Hyperspectral imaging and neural networks to classify herbicide-resistant weedsJournal of Applied Remote Sensing, 13
J. Gómez-Sanchís, J. Martín-Guerrero, E. Soria-Olivas, M. Martínez-Sober, J. Benedito, J. Blasco (2012)
Detecting rottenness caused by Penicillium genus fungi in citrus fruits using machine learning techniquesExpert Syst. Appl., 39
M. Li, D. Slaughter, J. Thompson (1997)
Optical chlorophyll sensing system for banana ripeningPostharvest Biology and Technology, 12
Gao Huang, Zhuang Liu, Kilian Weinberger (2016)
Densely Connected Convolutional Networks2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Abstract.A method of monitoring produce freshness with hyperspectral imaging and machine learning is described as a way to reduce food waste in grocery stores. The method relies on hyperspectral reflectance images in the visible–near-infrared spectral range from 387.12 to 1023.5 nm with a 2.12-nm spectral resolution. The images were recorded in a laboratory with the imager viewing produce samples illuminated by broadband halogen lights, but we also recorded and discussed the implications of the illumination spectrum of lights found in a variety of grocery stores. A convolutional neural network was used to perform freshness classification for potatoes, bananas, and green peppers. Additionally, a genetic algorithm (GA) was used to determine the wavelengths carrying the most useful information for age classification, with an eye toward a future multispectral imager. Hyperspectral images were processed to explore the use of RGB images, GA-selected multispectral images, and full-spectrum hyperspectral images. The GA-based feature selection method outperformed RGB images for all tested produce, outperformed hyperspectral imagery for bananas, and matched hyperspectral imagery performance for green peppers. This feature selection method is being used to develop a low-cost multispectral imager for use in monitoring produce in grocery stores.
Journal of Applied Remote Sensing – SPIE
Published: Jul 1, 2021
Keywords: food waste; food quality; food safety; hyperspectral imaging; machine learning
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.