Computer vision-based analysis of foods: A non-destructive colour measurement tool to monitor quality and safety

2013 ◽  
Vol 94 (7) ◽  
pp. 1259-1263 ◽  
Author(s):  
Burçe Ataç Mogol ◽  
Vural Gökmen
Keyword(s):  
Computer Vision ◽  
Measurement Tool ◽  
Quality And Safety ◽  
Colour Measurement ◽  
Non Destructive

Computer vision coupled with laser backscattering for non-destructive colour evaluation of papaya during drying

2017 ◽  
Vol 11 (4) ◽  
pp. 2142-2150 ◽  
Author(s):  
Patchimaporn Udomkun ◽  
Marcus Nagle ◽  
Dimitrios Argyropoulos ◽  
Alexander Nimo Wiredu ◽  
Busarakorn Mahayothee ◽  
...  
Keyword(s):  
Computer Vision ◽  
Non Destructive

scholarly journals AlexNet convolutional neural network to classify the types of Indonesian coffee beans

2021 ◽  
Vol 905 (1) ◽  
pp. 012059
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
F I Ilmi ◽  
M R Fauzy ◽  
R Damayanti ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Sensitivity Analysis ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Agricultural Products ◽  
Coffee Bean ◽  
Coffee Beans ◽  
Non Destructive

Abstract Various types of Indonesian coffee are already popular internationally. Recently, there are still not many methods to classify the types of typical Indonesian coffee. Computer vision is a non-destructive method for classifying agricultural products. This study aimed to classify three types of Indonesian Arabica coffee beans, i.e., Gayo Aceh, Kintamani Bali, and Toraja Tongkonan, using computer vision. The classification method used was the AlexNet convolutional neural network with sensitivity analysis using several variations of the optimizer such as SGDm, Adam, and RMSProp and the learning rate of 0.00005 and 0.0001. Each type of coffee used 500 data for training and validation with the distribution of 70% training and 30% validation. The results showed that all AlexNet models achieved a perfect validation accuracy value of 100% in 1,040 iterations. This study also used 100 testing-set data on each type of coffee bean. In the testing confusion matrix, the accuracy reached 99.6%.

Rapid and Non-Invasive Techniques

2022 ◽  
pp. 41-66
Author(s):  
Muhammad Haseeb Ahmad ◽  
Amna Sahar ◽  
Muhammad Imran ◽  
Muhammad Kamran Khan ◽  
Rabia Shabir Ahmad ◽  
...  
Keyword(s):  
Food Safety ◽  
Quality And Safety ◽  
Safety Issues ◽  
Non Invasive ◽  
Food Items ◽  
Business Meetings ◽  
Safety Parameters ◽  
Invasive Techniques ◽  
Modern Era ◽  
Non Destructive

In this modern era of digitalization and consumer awareness regarding food safety issues, it has become important to build proper strategies that can ensure the quality and safety of the food items from farm to forks. People love to eat at restaurants not only during business meetings but also with their family for fun and entertainment. The choice and safety of the food is vital to attract the consumer in this competitive environment. Previously, conventional methods have been employed for assurance of quality and safety parameters of the food. But in this modern era, there are many potential alternatives that can serve the purpose rapidly and non-destructively. Hence, this chapter describes the rapid and non-destructive methodologies such as fluorescence, NIRS, MIR, and Raman spectroscopy that can be used for the food safety evaluations.

Non-destructive and contactless quality evaluation of table grapes by a computer vision system

2019 ◽  
Vol 156 ◽  
pp. 558-564 ◽  
Author(s):  
Dario Pietro Cavallo ◽  
Maria Cefola ◽  
Bernardo Pace ◽  
Antonio Francesco Logrieco ◽  
Giovanni Attolico
Keyword(s):  
Computer Vision ◽  
Quality Evaluation ◽  
Vision System ◽  
Computer Vision System ◽  
Table Grapes ◽  
Non Destructive

Technical Note: Detection of delamination in tyres using eddy currents

1997 ◽  
Vol 211 (1) ◽  
pp. 79-82 ◽  
Author(s):  
X. E. Gros
Keyword(s):  
Eddy Currents ◽  
Structural Integrity ◽  
Low Cost ◽  
Technical Note ◽  
Quality And Safety ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Safety Standards ◽  
Current Testing ◽  
Non Destructive

Non-destructive testing (NDT) is a useful tool to assess the structural integrity of components in order to maintain quality and safety standards. A low-cost electromagnetic technique based on eddy currents induced into a material appeared promising for the inspection of composite materials. Experiments were carried out in order to assess the potential of eddy currents in detecting delamination in rubber tyres. Infrared thermography was used to verify inspection results achieved with eddy currents. Non-destructive examination results are presented in this paper; these confirm that eddy current testing is an economically viable alternative for the inspection of steel reinforced truck tyres.

scholarly journals Flesh of pumpkin from ecological farming as part of fruit suitable for non-destructive cultivar classification using computer vision

2021 ◽  
Author(s):  
Ewa Ropelewska ◽  
Wioletta Popińska ◽  
Kadir Sabanci ◽  
Muhammet Fatih Aslan
Keyword(s):  
Computer Vision ◽  
Outer Surface ◽  
Color Space ◽  
Discriminative Models ◽  
Ecological Farming ◽  
Cultivar Classification ◽  
The Individual ◽  
Non Destructive ◽  
Total Accuracy ◽  
Color Channels

AbstractThe aim of this study was to build the discriminative models for distinguishing the different cultivars of flesh of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ based on selected textures of the outer surface of images of cubes. The novelty of research involved the use of about 2000 different textures for one image. The highest total accuracy (98%) of discrimination of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ was determined for models built based on textures selected from the color space Lab and the IBk classifier and some of the individual cultivars were classified with the correctness of 100%. The total accuracy of up to 96% was observed for color space RGB and 97.5% for color space XYZ. In the case of color channels, the total accuracies reached 91% for channel b, 89.5% for channel X, 89% for channel Z.

Computer vision system (CVS): a powerful non-destructive technique for the assessment of red mullet (Mullus barbatus) freshness

2017 ◽  
Vol 243 (12) ◽  
pp. 2225-2233 ◽  
Author(s):  
Silvia Tappi ◽  
Pietro Rocculi ◽  
Alessandra Ciampa ◽  
Santina Romani ◽  
Federica Balestra ◽  
...  
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Red Mullet ◽  
Mullus Barbatus ◽  
Non Destructive
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