An experimental machine vision system for sorting sweet tamarind

Jarimopas, Bundit; Jaisin, Nitipong

doi:10.1016/j.jfoodeng.2008.05.007

Cited by 76 publications

(31 citation statements)

References 6 publications

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“…During the processing of other kinds of nuts, the development of computerized systems and equipment for sorting has also been studied (JARIMOPAS;JAISINI, 2008;HAFF;PEARSON, 2007). According to De Mello and Scussel (2007), among the factors that may be considered for the use of automatic sorting are: weight, length, width, thickness, mc, Aw, and chromaticity.…”

mentioning

confidence: 99%

Brazil nut sorting for aflatoxin prevention: a comparison between automatic and manual shelling methods

Pacheco

Martins

2013

Food Sci. Technol

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confidence: 99%

Brazil nut sorting for aflatoxin prevention: a comparison between automatic and manual shelling methods

Pacheco

Martins

2013

Food Sci. Technol

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“…Circularity, aspect ratio and compactness are examples of simple mathematical combinations of size measurements. More sophisticated shape analyses can be performed independent of size measurements; these can be classified (Zhang and Lu, 2004) into region-based (Heinemann et al, 1995) and contourbased (Abdullah et al, 2006;Menesatti et al, 2008;Jarimopas and Jaisin, 2008;Riyadi et al, 2008;Costa et al, 2009). Table 2 summarizes classification accuracies reported by several researchers using different shape description techniques.…”

Section: Size and Shapementioning

confidence: 99%

Sensors for product characterization and quality of specialty crops—A review

Ruiz-Altisent

Ruiz-García

Moreda

et al. 2010

Computers and Electronics in Agriculture

192

112

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a b s t r a c tThis review covers developments in non-invasive techniques for quality analysis and inspection of specialty crops, mainly fresh fruits and vegetables, over the past decade up to the year 2010. Presented and discussed in this review are advanced sensing technologies including computer vision, spectroscopy, X-rays, magnetic resonance, mechanical contact, chemical sensing, wireless sensor networks and radiofrequency identification sensors. The current status of different sensing systems is described in the context of commercial application. The review also discusses future research needs and potentials of these sensing technologies. Emphases are placed on those technologies that have been proven effective or have shown great potential for agro-food applications. Despite significant progress in the development of non-invasive techniques for quality assessment of fruits and vegetables, the pace for adoption of these technologies by the specialty crop industry has been slow.

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“…to segment the object of interest from the background), image features are extracted that summarise important qualities of the object, then a pattern recognition system is used to categorise the input data. For example, [3] introduced a system for sorting sweet tamarind, by measuring the size and shape of tamarind pods as well as detecting defects in the form of broken pods. Thresholded intensity values were used to distinguish blemishes from non-blemishes.…”

Section: B Related Workmentioning

confidence: 99%

Boosting minimalist classifiers for blemish detection in potatoes

Barnes

Duckett

Cielniak

2009

2009 24th International Conference Image and Vision Computing New Zealand

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Abstract-This paper introduces novel methods for detecting blemishes in potatoes using machine vision. After segmentation of the potato from the background, a pixel-wise classifier is trained to detect blemishes using features extracted from the image. A very large set of candidate features, based on statistical information relating to the colour and texture of the region surrounding a given pixel, is first extracted. Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and nonblemishes. With this approach, different features can be selected for different potato varieties, while also handling the natural variation in fresh produce due to different seasons, lighting conditions, etc. The results show that the method is able to build "minimalist" classifiers that optimise detection performance at low computational cost. In experiments, minimalist blemish detectors were trained for both white and red potato varieties, achieving 89.6% and 89.5% accuracy respectively.

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An experimental machine vision system for sorting sweet tamarind

Cited by 76 publications

References 6 publications

Brazil nut sorting for aflatoxin prevention: a comparison between automatic and manual shelling methods

Brazil nut sorting for aflatoxin prevention: a comparison between automatic and manual shelling methods

Sensors for product characterization and quality of specialty crops—A review

Boosting minimalist classifiers for blemish detection in potatoes

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