Detection of potato tubers using an ultraviolet imaging-based machine vision system

Al-Mallahi, Ahmad; Kataoka, Takao; Okamoto, Hiroshi; Shibata, Yoichi

doi:10.1016/j.biosystemseng.2009.11.004

Cited by 42 publications

(16 citation statements)

References 16 publications

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“…Hyperspectral imaging, a technique combining the principles of spectroscopy and imaging, has been applied to subsurface defect detection in fruit and vegetables, such as apples (ElMasry, Wang, Vigneault, Qiao, & ElSayed, 2008;Lu, 2003;Xing & De Baerdemaeker, 2005;Xing, Saeys, & De Baerdemaeker, 2007), pears (Zhao, Ouyang, Chen, & Wang, 2010) and mushrooms (Gowen et al, 2008). In the case of potatoes the usefulness of hyperspectral imaging has been reported for the discrimination between potato tubers and clods (Al-Mallahi, Kataoka, & Okamoto, 2008;Al-Mallahi, Kataoka, Okamoto, & Shibata, 2010), the detection of hollow heart (Dacal-Nieto, Formella, Carrión, Vazquez-Fernandez, & Fernández-Delgado, 2011b) and the detection of common scab (Dacal-Nieto, Formella, Carrión, Vazquez-Fernandez, & Fernández-Delgado, 2011a). Thybo, Jespersen, Laerke, and Stødkilde-Jørgensen (2004) were able to identify internal bruises in potato slices of cultivar Saturna by applying magnetic resonance imaging.…”

Section: Introductionmentioning

confidence: 99%

Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging

et al. 2016

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Section: Introductionmentioning

confidence: 99%

Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging

et al. 2016

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“…In the past few decades, as computer vision technology has undergone high‐speed development, many machine vision systems have already been applied to the industry. For example, some machine vision systems are used for screening the high‐quality goods , and some machine vision systems are used for guiding the harvesting robots . Two good reviews are given in .…”

Section: Related Workmentioning

confidence: 99%

A vision system for canning with fish sensing using rule‐based matching and segmentation

Zhang

You

Miyoshi

et al. 2020

IEEJ Transactions Elec Engng

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We develop a machine vision system as a key component of a robot-assisted packaging system, which can guide the robot arms to pack the roast sauries into cans. For gripping strategy generation, the system is required not only to be able to detect the roast saury area but also estimate the geometric parameters. Besides, according to different canning requirements, it is also necessary to distinguish the type of fish parts. Facing these challenges, we propose a novel rule-based matching method combined with an improved efficient graph-based image segmentation (EGIS) method for sensing the fish part. Specifically, the matching method applies our originally designed rule-based similarity under a genetic algorithm framework combined with deterministic crowding technique, which is used to sensing one type of fish parts. On the other hand, we improve the EGIS by introducing a shape restriction to deal with leftover fish parts. The experiments are implemented for two different types of fish part in the real factory environment. The result of our method achieved a mean location accuracy of 93.5% with a practical average processing time of 2.6 s per image. Yi Zhang (Non-member) He received a B.E. in the computer science and technology from Northwest A&F University in 2015. He is now a Ph.D. candidate at graduate school of engineering, department of design and media technology, Iwate University and has received an M.E. His research interests include evolutionary algorithms, image processing, and machine learning. He is a student member of the IEEE.

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“…Although the application of UV imaging in the area of agricultural and food products is increasing (Al-Mallahi et al 2010, Momin et al 2011, UV imaging remains an emerging field of research, given the lack of suitable costeffective UV detectors currently available for food products. UV imaging offers remarkable potential for application in food agricultural product safety and authenticity.…”

Section: Ultraviolet Imagingmentioning

confidence: 99%

Agricultural Product Authenticity and Geographical Origin Traceability

Zhao

Nakano

2018

JARQ

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As the security of agricultural products has become a serious issue faced by people worldwide in recent decades, measures for an effective supervision system for agricultural product quality and safety are continuously being developed. In such a system, the assessment of agricultural product authenticity and geographical origin traceability could play a very important role. Recently, researchers have been focusing on some successful techniques, including the stable isotopic technique, compositional analysis technique, spectroscopic technique, and sensor technology. The benefits from advances made in spectroscopy and imaging technology have facilitated the development of imaging spectrometry techniques that offer such advantages as being nondestructive, rapid, and requiring minimal to no sample preparation. This paper discusses several nondestructive technologies used for the assessment of agricultural product authenticity and geographical origin traceability, with a special focus on the nondestructive technology of imaging using an electromagnetic spectrum for agricultural product safety and quality. It specifically discusses the technology of ultraviolet imaging, hyperspectral imaging, fluorescence spectrometry, nuclear magnetic resonance, and terahertz spectroscopy according to different wavelengths and frequency of this electromagnetic spectrum. Although the application of nondestructive measurements performed using the electromagnetic spectrum for identifying agricultural product authenticity and geographical origin traceability is increasing along with advanced technology and lower equipment costs, the accuracy of such measurements must still be improved and the advantages in practical applications need to be determined.

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Detection of potato tubers using an ultraviolet imaging-based machine vision system

Cited by 42 publications

References 16 publications

Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging

Non-destructive detection of blackspot in potatoes by Vis-NIR and SWIR hyperspectral imaging

A vision system for canning with fish sensing using rule‐based matching and segmentation

Agricultural Product Authenticity and Geographical Origin Traceability

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