Computer-Assisted Color Classification of Peanut Pods1

Boldor, D.; Sanders, Timothy H.; Swartzel, K. R.; Šimunović, Josip

doi:10.3146/pnut.29.1.0008

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…Image analysis is usually objective, reliable, robust, and resource-efficient. Over the past two decades, machine-vision based methods have been used in several studies in peanut phenotyping, including kernel size and kernel damage (Dowell, 1992), peanut maturity (Ghate et al, 1993;Colvin et al, 2014), pod color (Boldor et al, 2002), and projected pod area (Aydin, 2007), to name a few. Ghate et al (1993) developed a machine-vision maturity classification method based on detection of surface texture differences among kernels.…”

mentioning

confidence: 99%

Fine Phenotyping of Pod and Seed Traits in Arachis Germplasm Accessions Using Digital Image Analysis

Gill

Chu

et al. 2015

Peanut Science

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Reliable and objective phenotyping of peanut pod and seed traits is important for cultivar selection and genetic mapping of grade components. To develop useful and efficient methods to quantitatively define peanut pod and seed traits, a group of peanut germplasm with high levels of phenotypic variation was included in this study. Digital image analysis was employed to quantify peanut pod volume using projected 2-dimensional pod area as an effective surrogate parameter. Strong correlation between pod volume measured by water displacement and image analysis was established (R 2 5 0.891). Pod volume, pod density and kernel percent were observed to vary significantly among the germplasm lines. To define testa color, hue angle analysis was performed on a subset of germplasm with solid testa color. It was determined that hue angle can sufficiently differentiate genotypic differences based on testa color variation.

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confidence: 99%

Fine Phenotyping of Pod and Seed Traits in Arachis Germplasm Accessions Using Digital Image Analysis

Gill

Chu

et al. 2015

Peanut Science

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“…In India, peanut is one of the most important oilseed crops, which is high in nutritional value and superior vegetable protein. The import and export trade of peanut is main decided by its appearance quality, size and shape [1]. Manual grading of peanuts is time consuming and fatigue job.…”

Section: Introductionmentioning

confidence: 99%

Mobile App for Grading of Peanuts using Fuzzy Inference System

Karegowda,

2019

IJEAT

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The peanut a globally adopted vegetable protein with appreciable nutritive contents. The grading is one of the significant way to ensure that quality of the peanuts regulate the appropriate price in market. The proposed work is carried out in two phases. As part of first phase segmentation is done using canny edge detection followed by extraction of four Shape Features (SF): Area (Ar), Major Axis (MajAxis), Minor Axis (MinAxis), Perimeter (PeriMt). As part of second phase, Fuzzy Inference System (FIS) has been adopted for grading of four categories of peanuts. .The interval scaled shape features obtained in phase one are converted to Linguistic Terms (LingTerms). The fuzzy data base rules are attained using Association Rules with the constraints that the consequent can have only class label. The proposed work is developed as mobile app which takes peanut image as input and processes the image using FIS and provides the output in terms of pie chart. The pie chart provides the percentage of different qualities of peanuts (low/medium/good/fine) in the input image. The proposed work resulted in an accuracy of 95% when compared with the ground truth. The work can be applied in automation process for grading of food grains using mobile apps, which helps the lay customer to know about the quality of peanuts. The work can also be used for auto separation of peanuts for packing based on grade of the peanuts.

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“…It has been proposed to use mechanical or electronic techniques to identify hull color to reduce human bias and ambiguity. Other techniques have been tried that range from chemical and mechanical manipulation of kernel or hulls to the estimation of peanut maturity using plant age, plant parts, meteorological data, or combination of these to estimate time to harvest or peanut maturity (Boldor et al, 2002;Chapin and Thomas, 2005;Colvin et al, 2014;Grimm et al, 1998;Rowland et al, 2006;Sanders et al, 1980;Sanders et al, 1982a;Sanders et al, 1982b;Sorensen et al, 2015).…”

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X-ray Technology to Determine Peanut Maturity1

Sorensen

Butts²,

Lamb³

2020

Peanut Science

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Indeterminate growth of peanut ( Arachis hypogaea L.) creates indecision for best digging date for maturity and economic return. The current standard to determine peanut maturity is the Hull Scrape method. This method uses human observations to place hull scraped peanuts on a color maturity profile board. Human observations may lack precision and repeatability from individual to individual. X-ray technology has the capability of viewing peanut kernels through the hull to possibly ascertain density and maturity. The objective was to determine if x-ray could be used as a quick, non-destructive, and repeatable method to determine peanut maturity of runner, spanish, and virginia market types. Fresh dug peanut pods had 25 percent greater peanut area and gray scale values compared with hull scraped pods (runner and virginia only) and showed no difference in x-ray value between immature and fully mature peanut. Dried peanut showed a linear response of x-ray value versus peanut maturity (hull color). Virginia market type had much higher x-ray values followed by runners, then spanish. The relationship between peanut maturity and x-ray value peaked at the Orange class for runners (Georgia-06G, Georgia-13M), and Spanish (AT9899) while virginia (Georgia-11J) tended to peak at the Brown class. This research demonstrated that x-ray technology may be used to measure peanut density and possible maturity but needs further examination past Orange and Brown maturity class. Final x-ray values determined by this proprietary x-ray equipment may not be transferable due to specific x-ray power, detector precision, background color/scatter, and other electronic nuances.

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Computer-Assisted Color Classification of Peanut Pods1

Cited by 11 publications

References 10 publications

Fine Phenotyping of Pod and Seed Traits in Arachis Germplasm Accessions Using Digital Image Analysis

Fine Phenotyping of Pod and Seed Traits in Arachis Germplasm Accessions Using Digital Image Analysis

Mobile App for Grading of Peanuts using Fuzzy Inference System

X-ray Technology to Determine Peanut Maturity1

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