An automated inspection station for machine-vision grading of potatoes

Heinemann, P.; Pathare, Niranjan P.; Morrow, Charles T.

doi:10.1007/bf01246635

Cited by 57 publications

(16 citation statements)

References 8 publications

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“…Some studies related to machine vision inspection of raw potatoes have been reported in the literature. For instance, automated inspection stations for machine vision grading of potatoes in size and shape have been reported; however, these systems cannot fulfill the potato industry requirements for high throughput and real-time speed (Tao et al 1990;Deck et al 1992;Grenander and Manbeck 1993;Heinemann et al 1996).…”

Section: Introductionmentioning

confidence: 99%

Computer Vision Classification of Potato Chips by Color

Pedreschi¹,

Mery

Bunger

et al. 2011

J Food Process Engineering

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In this research the automatic classification of commercial potato chips by computer vision was studied. The general objective was to design a tool that would be able to classify objectively potato chips according to their color in different categories. For this purpose, sensory measurements of color in 100 potato chips were correlated with the corresponding objective measurements obtained by computer vision system. Potato chips with and without ruffles of different brands were used for training and validation experiments. Sensory evaluations were done with a special chart that classifies potato chips in seven color categories. Simultaneously, the color of the same potato chips classified by the sensory panel, was determined objectively by a computer vision system in L*, a*, b* units. A linear regression model was good enough to predict potato chip sensory color values from the corresponding instrumental measurements by computer vision. The linear model after following the process of crossed validation crossed presented an error of ∼4% for smooth chips (without ruffles) and ∼7% for chips with ruffles. PRACTICAL APPLICATIONS The automatic classification methodology presented for potato chips is general and has a wide range of potential uses. It could be applied not only to other potato cultivars and frying conditions but also to other less heterogeneous raw materials and unit operations different than potato and frying, respectively. The computer vision system used in this research could as well be very useful in the food industry as a large amount of information can now be obtained from measurements at the pixel level that allows a better characterization of foods and thus improves quality control.

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Section: Introductionmentioning

confidence: 99%

Computer Vision Classification of Potato Chips by Color

Pedreschi¹,

Mery

Bunger

et al. 2011

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…Many works have also been conducted in potato sorting and grading industries. Heinemann, Pathare, and Morrow (1996) developed an automated prototype inspection station according to the United States Department of Agriculture (USDA) inspection standards for potato grading. Al-Mallahi, Kataoka, Okamoto, and Shibata (2010) developed an image processing algorithm to detect in-line potato tubers without singulation when they are mixed with clods.…”

Section: Introductionmentioning

confidence: 99%

Visual detection of sprouting in potatoes using ensemble‐based classifier

Ming

Shen

et al. 2018

J Food Process Engineering

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This paper introduces novel methods for detection of sprouting in potatoes using machine vision. An ensemble‐based classifier was trained to detect sprouting considering the diversity of both feature extraction and classifiers. Two categories, namely manual feature extraction and automatic feature extraction, were utilized to enhance the diversity of feature extraction. In experiments, the proposed ensemble‐based classifier without multiple channels CNN (MC‐CNN) outperformed mainstream methods and achieved state‐of‐the‐art prediction rate of 0.916 and f‐measure of 0.905 under lower standard deviation. Furthermore, with the help of three different CNN classifiers, there was already an obvious improvement in the performance of ensemble‐based classifier with MC‐CNN. The prediction rate and f‐measure increased about 4∼5%, comparing to that without MC‐CNN. The results indicate that this approach has a better capability to combine the features for detection of sprouting in potatoes, in which the diversity of both feather extraction and classifier had been enhanced. Practical Applications Discrimination and classification of potato sprouting or not, which uses ensemble‐based classifier system with combine the best features.

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“…Computerized vision technology has been developed for sorting market peaches (Miller and Delwiche 1989). Advanced instrumentation and software techniques for color detection and shape classification of potatoes and Golden Delicious apples have also been documented (Heinemann et al 1996). A working and monochrome-based vision system which biochromatically separates stained from the unstained nuts has been reported (Pearson 1996).…”

Section: Introductionmentioning

confidence: 99%

Color Vision System for Ripeness Inspection of Oil Palm Elaeis Guineensis

Abdullah¹,

Guan²,

Mohamed

et al. 2002

J Food Processing Preservation

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In checking harvesting discipline and quality control for oil palm fruits, color has presumably been an important guide to whether the oil content has reached a maximum where the fruit bunch is ready for cutting. However, establishing a single and harmonious standard base on color is a very contentious issue in the oil palm industry because of the subjective nature of the human vision of color. This was further complicated due to the lack of information on fruit color upon which to base a definite ripeness criterion. We demonstrated in this paper that this problem can be solved using machine vision technology. Methods used were to treat color in HSI (Hue, Saturation and Intensity) color space and applied multivariate discriminant analysis. These have proven to be highly effective for color evaluation and image processing. The vision system was trained to classify oil palms into four quality grades according to PORIM (Palm Oil Research Institute of Malaysia) inspection standards. These are the unripe, the underripe, the optimally ripe and the overripe classes. Depending upon the quality feature evaluated, misclassification by the vision system varied from 5 to 12% but averaged at about 8%. Machine vision disagreement ranged from 2 to 19%.

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An automated inspection station for machine-vision grading of potatoes

Cited by 57 publications

References 8 publications

Computer Vision Classification of Potato Chips by Color

Computer Vision Classification of Potato Chips by Color

Visual detection of sprouting in potatoes using ensemble‐based classifier

Color Vision System for Ripeness Inspection of Oil Palm Elaeis Guineensis

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