Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers

Ariana, Diwan P.; Lu, Renfu; Guyer, Daniel E.

doi:10.1016/j.compag.2006.04.001

Cited by 200 publications

(91 citation statements)

References 18 publications

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“…AUC is a measure of a classifi er's performance and can be used for comparing the results of different classifi cation schemes. Its maximum value is one, which corresponds to the ROC curve crossing the [0.1] point on the plot (100% Se and 0% false alarm rate), whereas the value of 0.5 indicates very poor model quality [33]. All the computations were performed using Statistica® 12 (StatSoft, Inc., Tulsa, OK, USA).…”

Section: Methodsmentioning

confidence: 99%

Identification of Cows Susceptible to Mastitis based on Selected Genotypes by Using Decision Trees and A Generalized Linear Model

et al. 2016

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The aim of the present study was to: 1) check whether it would be possible to detect cows susceptible to mastitis at an early stage of their utilization based on selected genotypes and basic production traits in the fi rst three lactations using ensemble data mining methods (boosted classifi cation tress -BT and random forest -RF), 2) fi nd out whether the inclusion of additional production variables for subsequent lactations will improve detection performance of the models, 3) identify the most signifi cant predictors of susceptibility to mastitis, and 4) compare the results obtained by using BT and RF with those for the more traditional generalized linear model (GLZ). A total of 801 records for Polish Holstein-Friesian Black-and-White cows were analyzed. The maximum sensitivity, specifi city and accuracy of the test set were 72.13%, 39.73%, 55.90% (BT), 86.89%, 17.81%, 59.49% (RF) and 90.16%, 8.22%, 58.97% (GLZ), respectively. Inclusion of additional variables did not have a signifi cant effect on the model performance. The most signifi cant predictors of susceptibility to mastitis were: milk yield, days in milk, sire's rank, percentage of Holstein-Friesian genes, whereas calving season and genotypes (lactoferrin, tumor necrosis factor alpha, lysozyme and defensins) were ranked much lower. The applied models (both data mining ones and GLZ) showed low accuracy in detecting cows susceptible to mastitis and therefore some other more discriminating predictors should be used in future research.

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Section: Methodsmentioning

confidence: 99%

Identification of Cows Susceptible to Mastitis based on Selected Genotypes by Using Decision Trees and A Generalized Linear Model

et al. 2016

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“…Later, Ariana et al (2006) applied the same imaging system to capture hyperspectral images from pickling cucumbers at 0-3, and 6 days after they were subjected to dropping or rolling under load which simulated damage caused by mechanical harvesting and handling systems. PCA, band ratio, and band difference were applied in the image processing to segregate bruised cucumbers from normal cucumbers.…”

Section: Bruise Detectionmentioning

confidence: 99%

Principal Component Analysis in the Development of Optical and Imaging Spectroscopic Inspections for Agricultural / Food Safety and Quality

Liu¹

2012

Principal Component Analysis - Engineering Applications

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“…In the reports of Mehl et al (2004), Xing et al (2005Xing et al ( , 2007, Ariana et al (2006), and Qin et al (2008, 2009), owing to the obvious differences between the abnormality (i.e. bruise, rot, scab, canker, greasy spot, insect damage) regions and those of normal tissue of fruit (i.e.…”

Section: Segmentation Of Bruise Regionmentioning

confidence: 99%

“…X-ray imaging has Supported by the National Natural Science Foundation of China, Grant No. 30771243. been used to inspect internal quality, such as water core, bruise of apple (Shahin et al 1999(Shahin et al , 2002bKim et al 2000), weevil-infested mango (Thomas & Kannan 1995), and rotten onion (Shahin et al 2002a), etc. In the recent years, hyperspectral imaging has been investigated for the estimation of quality and safety of poultry (Liu et al 2003;Park et al 2006Park et al , 2007, fruits (Lu et al 1999;Lu 2003;Mehl et al 2004;Xing & Baerdemaeker 2005;Xing et al 2005Xing et al , 2007Noh & Lu 2007;Qin et al 2009), vegetables (Liu et al 2005;Ariana et al 2006), and milk (Qin & Lu 2007). Lu et al (1999) have studied hyperspectral imaging for detecting bruises on three cultivars of apples in the spectral region between 450 nm and 900 nm.…”

mentioning

confidence: 99%

Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

LüQiang¹,

Tang²,

Cai³

et al. 2011

Czech J. Food Sci.

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Lü Q., Tang M.-j., Cai j.-r., Zhao j.-w., Vittayapadung S. (2011): Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits. Czech j. Food Sci., 29: 595-602.It is necessary to develop a non-destructive technique for kiwifruit quality analysis because the machine injury could lower the quality of fruit and incur economic losses. Bruises are not visible externally owing to the special physical properties of kiwifruit peel.we proposed the hyperspectral imaging technique to inspect the hidden bruises on kiwifruit. The Vis/NIr (408-1117 nm) hyperspectral image data was collected. Multiple optimal wavelength (682, 723, 744, 810, and 852 nm) images were obtained using principal component analysis on the high dimension spectral image data (wavelength range from 600 nm to 900 nm). The bruise regions were extracted from the component images of the five waveband images using rBF-SVM classification. The experimental results showed that the error of hidden bruises detection on fruits by means of hyperspectral imaging was 12.5%. It was concluded that the multiple optimal waveband images could be used to constructs a multispectral detection system for hidden bruises on kiwifruits.

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Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers

Cited by 200 publications

References 18 publications

Identification of Cows Susceptible to Mastitis based on Selected Genotypes by Using Decision Trees and A Generalized Linear Model

Identification of Cows Susceptible to Mastitis based on Selected Genotypes by Using Decision Trees and A Generalized Linear Model

Principal Component Analysis in the Development of Optical and Imaging Spectroscopic Inspections for Agricultural / Food Safety and Quality

Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits

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