Bean Split Ratio for Dry Bean Canning Quality and Variety Analysis

Long, Y. F.; Bassett, Amber; Cichy, Karen A.; Thompson, Addie; Morris, Daniel

doi:10.1109/cvprw.2019.00323

Cited by 6 publications

(5 citation statements)

References 35 publications

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“… Drain and rinse the beans with tap water after sensory evaluation. Take a high‐quality image of each sample with the camera box. Images can be used for further analysis such as automatic bean split detection with computer program (Long et al., 2019). …”

Section: Methodsmentioning

confidence: 99%

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A pilot‐scale dry bean canning and evaluation protocol

Wang

Wright

Uebersax

et al. 2021

Food Processing Preservation

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Dry beans are a nutrient dense food that generally requires long cooking times. Canned beans provide consumers safe and convenient access to this nutritious food. Canning quality is a measure of how well beans withstand the canning process and is affected by many factors including bean genotype, growing conditions, and post‐harvest seed handling. The evaluation of canning quality has long been an important consideration for variety improvement in bean breeding programs. This pilot‐scale dry bean canning and evaluation protocol provides a detailed step‐by‐step method of canning and quality evaluation for major U.S. dry bean market classes. The post‐canning process evaluation includes appearance rating by a trained sensory panel and objective measures of water uptake, color, and texture. In addition, virtual canning quality training and evaluation methods have been implemented for enhanced data quality, flexibility, and engagement in the process. Novelty impact statement This detailed, state‐of‐the‐art‐method for small‐scale dry bean canning and evaluation, with the option for virtual evaluation by panelists is useful for bean breeding programs. An in‐depth panelist training video is included via link on how to evaluate canning quality in each of the major market classes.

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Section: Methodsmentioning

confidence: 99%

“…(iii) Images can be used for further analysis such as automatic bean split detection with computer program (Long et al, 2019). 3).…”

Section: Canning Quality-related Traits Measurementmentioning

confidence: 99%

A pilot‐scale dry bean canning and evaluation protocol

Wang

Wright

Uebersax

et al. 2021

Food Processing Preservation

Self Cite

View full text Add to dashboard Cite

show abstract

“…The pyramid convolutional neural network described in Long et al. (2019) to detect bean seed coat splits was retrained for this color pixel segmentation task. To train the network, semantic labels for 24 images (including 17 images for training and seven for validation) of various bean types and colors within the YBC were created manually and augmented by rotation (at 30° intervals) and flipping.…”

Section: Methodsmentioning

confidence: 99%

“…Images of bean seeds from each of the two field replications were taken by using an image acquisition system described by Mendoza et al (2017) with the shutter speed set to 1/100 s. To extract bean color from captured images via machine learning, a binary pixel classification was defined in which pixels representative of bean color are one category, and the remaining pixels, that is, background, hilum ring, corona, and specular reflection areas, are the second category. The pyramid convolutional neural network described in Long et al (2019) to detect bean seed coat splits was retrained for this color pixel segmentation task. To train the network, semantic labels for 24 images (including 17 images for training and seven for validation) of various bean types and colors within the YBC were created manually and augmented by rotation (at 30˚intervals) and flipping.…”

Section: Cie L*a*b* Of Bean Seed Coatmentioning

confidence: 99%

Seed coat color genetics and genotype × environment effects in yellow beans via machine‐learning and genome‐wide association

et al. 2021

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Common bean (Phaseolus vulgaris L.) is consumed worldwide, with strong regional preferences for seed appearance characteristics. Colors of the seed coat, hilum ring, and corona are all important, along with susceptibility to postharvest darkening, which decreases seed value. This study aimed to characterize a collection of 295 yellow bean genotypes for seed appearance and postharvest darkening, evaluate genotype × environment (G × E) effects and map those traits via genome‐wide association analysis. Yellow bean germplasm were grown for 2 yr in Michigan and Nebraska and seed were evaluated for L*a*b* color values, postharvest darkening, and hilum ring and corona colors. A model to exclude the hilum ring and corona of the seeds, black background, and light reflection was developed by using machine learning, allowing for targeted and efficient L*a*b* value extraction from the seed coat. The G × E effects were significant for the color values, and Michigan‐grown seeds were darker than Nebraska‐grown seeds. Single‐nucleotide polymorphisms (SNPs) were associated with L* and hilum ring color on Pv10 near the J gene involved in mature seed coat color and hilum ring color. A SNP on Pv07 associated with L*, a*, postharvest darkening, and hilum ring and corona colors was near the P gene, the ground factor gene for seed coat color expression. The machine‐learning‐aided model used to extract color values from the seed coat, the wide variability in seed morphology traits, and the associated SNPs provide tools for future breeding and research efforts to meet consumers’ expectations for bean seed appearance.

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“…A score of 3 for appearance is considered acceptable, although expectations vary depending on the market class. Appearance requires many trained evaluators and is difficult to rate accurately and consistently, but image analysis may be a suitable alternative for canning quality evaluation in the near future 17 . After being evaluated by trained panelists, samples are rinsed and weighed to determine water uptake during canning.…”

Section: Introductionmentioning

confidence: 99%