Computer vision for potato inspection without singulation

Marchant, J.A.; Onyango, Christine M.; Street, Mandie

doi:10.1016/0168-1699(90)90022-h

Cited by 30 publications

(12 citation statements)

References 2 publications

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“…Greening, as an external defect, was successfully detected based on tuber surface color by Tao et al (1995a), Zhou et al (1998), Noordam et al (2000, and Dacal-Nieto et al (2009), with classification rates of defected tubers of 90%, 78.0%, 88.1%, and 86.2%, respectively. Sorting and grading tubers could be a difficult mission with the singulation problem as a result of the possible interference between different touching objects (Marchant et al, 1990;Al-Mallahi et al, 2010a). It was possible, however, to build grading systems for tubers based on size by developing several separating techniques, applied on the captured images, such as the blob splitting algorithm (Marchant et al, 1990), the 8-neighbor labeling algorithm (Al-Mallahi et al, 2010a), or based on intensity threshold (Dacal-Nieto et al, 2009).…”

Section: Systems For Nondestructive Sorting (Physical Separation) Of mentioning

confidence: 99%

“…Sorting and grading tubers could be a difficult mission with the singulation problem as a result of the possible interference between different touching objects (Marchant et al, 1990;Al-Mallahi et al, 2010a). It was possible, however, to build grading systems for tubers based on size by developing several separating techniques, applied on the captured images, such as the blob splitting algorithm (Marchant et al, 1990), the 8-neighbor labeling algorithm (Al-Mallahi et al, 2010a), or based on intensity threshold (Dacal-Nieto et al, 2009). Consequently, grading tubers into several standard grades, and eliminating misshapen tubers, was successfully conducted by Marchant et al (1990), Noordam et al (2000), andEl Masry et al (2012), with classification rates of 100%, 99.23%, and 98.8%, respectively.…”

Section: Systems For Nondestructive Sorting (Physical Separation) Of mentioning

confidence: 99%

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Rapid and/or nondestructive quality evaluation methods for potatoes: A review

Rady

Guyer

2015

Computers and Electronics in Agriculture

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Section: Systems For Nondestructive Sorting (Physical Separation) Of mentioning

confidence: 99%

Section: Systems For Nondestructive Sorting (Physical Separation) Of mentioning

confidence: 99%

Rapid and/or nondestructive quality evaluation methods for potatoes: A review

Rady

Guyer

2015

Computers and Electronics in Agriculture

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“…Therefore, there have been many attempts to get rid of or, at least, to shrink the size of the mechanisms to become more reasonable in cost and size [6,7].…”

mentioning

confidence: 99%

“…The lack of a compacted singulation mechanism is one of the reasons, which obstruct automating the removal of the clods and the damaged potatoes on the harvester. Marchant et al [7] developed a blob splitting algorithm which separated touching potatoes, arranged in lines, to upper and lower parts and investigated the boundaries of each part. The contact point between two potatoes was considered, using this algorithm, to be in the area where opposing cusps from the upper and lower parts were approaching if the distance between the cusps were greater than a threshold.…”

mentioning

confidence: 99%

An image processing algorithm for detecting in-line potato tubers without singulation

Al-Mallahi

Kataoka

Okamoto

et al. 2010

Computers and Electronics in Agriculture

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An algorithm which helps detecting in-line potato tubers without singulation when they are mixed with clods was developed. It is particularly useful for sorting and grading purposes whether at the packhouses or the potato harvester. The algorithm scans images which include clusters of tubers and clods from left to right until it encounters a cluster. Once encountered, a scan around the periphery of the cluster starts until returning to the original point where the scanning around the periphery started. At the end of the periphery scanning, the periphery pixels are erased and the new width of the cluster is compared with the previous width. Contouring the periphery continues until a sudden shrink in the width of the cluster occurs which indicates the detection of a contact point. The left-to-right scanning resumes from the contact point until it reaches the right end of the image repeating the periphery scanning every time a cluster is encountered. The algorithm was tested by detecting 380 contact points between tubers and 112 contact points between tubers and clods. Results showed that approximately 99% of the contact points were detected successfully. It was found that the only reason of error was the lack of cusp between a tuber and a clod when the clod did not have an oval shape. It could be concluded that the algorithm is able to detect any contact point between any oval shaped adjacent objects as long as they form a cusp between them, which is guaranteed between any adjacent tubers. Therefore, the algorithm can be applied at the packhouses to replace the long, bulky and expensive conveyors needed to create spaces between the in-line potato tubers so as to aid machine vision-based size grading. Moreover, the algorithm can be used at the potato harvester to help in discriminating between the touching tubers and clods so as to automate the removal of the clods during harvesting

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“…field tasks, such as guiding systems in sewing (Leemans and Destain, 2007) and agriculture-related tasks performed in laboratories, as in the case of the product inspection of vegetables (Marchant et al, 1990). This last kind of study includes works relating to cereal grain identification (Visen et al, 2002), and, in fact, machine vision is usually employed to detect weed seeds in cereal grains (Granitto et al, 2002).…”

Section: Sistema De Visión Artificial Para La Clasificación De Granosmentioning

confidence: 99%

A machine vision system for classification of wheat and barley grain kernels

Guevara-Hernández¹,

Gómez-Gil²

1970

Span. j. agric. res.

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This study presents in detail a machine vision system that classifies objects into two classes. The procedure for the classification comprises two stages: a training stage and a testing stage. A feature vector, which is a sorted list of features that maximize the classification power, is computed in the training stage. Object classification was accomplished in the testing stage by means of discriminant analysis (DA) and K-nearest neighbors (K-NN) algorithms. The system was applied to the classification of wheat and barley grain kernels. Results obtained allow the researchers to conclude that in the classification of wheat and grain kernels with the presented system: (i) a high classification accuracy can be obtained; (ii) the employment of morphologic, color, and texture feature types together offers better accuracy than the employment of only one feature type; (iii) the extraction of the maximum radius, the green mean, and the y mean of the gray level co-occurrence matrix (GLCM) for 90° allows the highest classification accuracy; and (iv) the employment of more than three features increases the computational cost and may also reduce the classification accuracy.

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Computer vision for potato inspection without singulation

Cited by 30 publications

References 2 publications

Rapid and/or nondestructive quality evaluation methods for potatoes: A review

Rapid and/or nondestructive quality evaluation methods for potatoes: A review

An image processing algorithm for detecting in-line potato tubers without singulation

A machine vision system for classification of wheat and barley grain kernels

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