Classification of cracks of biological colonization on concrete surface using false colour HSV images, including near-infrared information

Author(s):  
Bruno Oliveira Santos ◽  
Jónatas Valença ◽  
Eduardo Júlio
Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.


2020 ◽  
Vol 73 (3) ◽  
pp. 358-367
Author(s):  
Júlio Cezar Rebés Azambuja Filho ◽  
Paulo Cesar de Faccio Carvalho ◽  
Olivier Jean François Bonnet ◽  
Denis Bastianelli ◽  
Magali Jouven

Author(s):  
Nidhi Rajesh Mavani ◽  
Jarinah Mohd Ali ◽  
Suhaili Othman ◽  
M. A. Hussain ◽  
Haslaniza Hashim ◽  
...  

AbstractArtificial intelligence (AI) has embodied the recent technology in the food industry over the past few decades due to the rising of food demands in line with the increasing of the world population. The capability of the said intelligent systems in various tasks such as food quality determination, control tools, classification of food, and prediction purposes has intensified their demand in the food industry. Therefore, this paper reviews those diverse applications in comparing their advantages, limitations, and formulations as a guideline for selecting the most appropriate methods in enhancing future AI- and food industry–related developments. Furthermore, the integration of this system with other devices such as electronic nose, electronic tongue, computer vision system, and near infrared spectroscopy (NIR) is also emphasized, all of which will benefit both the industry players and consumers.


2015 ◽  
Vol 8 (12) ◽  
pp. 2383-2391 ◽  
Author(s):  
Ellen Neyrinck ◽  
Stefaan De Smet ◽  
Liesbeth Vermeulen ◽  
Danny Telleir ◽  
Stefaan Lescouhier ◽  
...  

2007 ◽  
Vol 55 (22) ◽  
pp. 9128-9134 ◽  
Author(s):  
Tony Woodcock ◽  
Gerard Downey ◽  
J. Daniel Kelly ◽  
Colm O’Donnell

2018 ◽  
Vol 112 ◽  
pp. 85-92 ◽  
Author(s):  
Lívia Ribeiro Costa ◽  
Paulo Fernando Trugilho ◽  
Paulo Ricardo Gherardi Hein

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