Three-dimensional image analysis of the shape of soybean seed

Sakai, Naoki; Yonekawa, Satoshi

doi:10.1016/0260-8774(92)90052-8

Cited by 22 publications

(10 citation statements)

References 8 publications

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“…Iwamoto and Chuma (1981), Fon et al (1990), Chen et al (1992), Hahn (2002) Miller (1993, Laing et al (1995) Sarkar and Wolfe (1985), Marchant et al (1989), Marchant (1990), Miller (1990), Aleixos (1999), Forbes and Tattersfield (1999b), Bato et al (2000), Forbes (2000), Aleixos et al (2002), Sabliov et al (2002), Blasco et al (2003), etc. Hryniewicz et al (2005), Sun et al (2007) Chalidabhongse et al (2006, Imou et al (2006), Lee et al (2006) Chen et al (1989, Sakai and Yonekawa (1992), Thomson and Pomeranz (1991), Hatou et al (1996), Lee et al (2001) tested with the air pycnometer, cannot be measured using the ultrasonic inverted tripod.…”

Section: Tablementioning

confidence: 99%

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Non-destructive technologies for fruit and vegetable size determination – A review

Moreda

Ortiz-Cañavate

García-Ramos

et al. 2009

Journal of Food Engineering

133

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Here, we review different methods for non-destructive horticultural produce size determination, focusing on electronic technologies capable of measuring fruit volume.The usefulness of produce size estimation is justified and a comprehensive classification system of the existing electronic techniques to determine dimensional size is proposed.The different systems identified are compared in terms of their versatility, precision and throughput. There is general agreement in considering that online measurement of axes, perimeter and projected area has now been achieved. Nevertheless, rapid and accurate volume determination of irregular-shaped produce, as needed for density sorting, has only become available in the past few years. An important application of density measurement is soluble solids content (SSC) sorting. If the range of SSC in the batch is narrow and a large number of classes are desired, accurate volume determination becomes important. A good alternative for fruit three-dimensional surface reconstruction, from which volume and surface area can be computed, is the combination of height profiles from a range sensor with a two-dimensional object image boundary from a solid-state camera (brightness image) or from the range sensor itself (intensity image). However, one of the most promising technologies in this field is 3-D multispectral scanning, which combines multispectral data with 3-D surface reconstruction.

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Section: Tablementioning

confidence: 99%

“…The disadvantage was that the method was time-consuming -it took 5 s to classify each tomato. Sakai and Yonekawa (1992) developed a structured light system for measuring the volume of soybean seeds. They projected a plane of light onto the soybean sample, which was supported by a needle.…”

Section: Active 3-d Machine Vision Techniquesmentioning

confidence: 99%

Non-destructive technologies for fruit and vegetable size determination – A review

Moreda

Ortiz-Cañavate

García-Ramos

et al. 2009

Journal of Food Engineering

133

View full text Add to dashboard Cite

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“…The approximation of the volume depends heavily on the number of viewing positions and on the concavities of the object under examination. Sakai et al [7] developed a structured light system for measuring the soybean seed surface area and volume. They developed regression equations relating seed mass to surface area and volume.…”

Section: B Digital Methodsmentioning

confidence: 99%

Area measurement of seed from distorted images for quality seed selection

Chaugule¹,

Mali²

2013

2013 Nirma University International Conference on Engineering (NUiCONE)

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Measurement of seed area is an important trait in studies of developmental physiology. However, direct measurement of this feature is difficult, not very precise and most of the time is destructive. The objective of the study presented in this paper is to develop a computer vision technique to determine the projected area of a seed for quality seed selection, irrespective of how the image is distorted. When a seed area is measured it is difficult to keep camera's optical axis vertical with seed plane. So the plane of camera is not superposed with image plane, and the seed image is distorted because of some geometric distortions. This may affect the precision of seed area measurement. Therefore the geometric distortion must be corrected before calculating the area. For improving the inconsistent feature problem caused by the distorted images, feature extraction strategies are proposed. The seed area is one of the features used for discriminant analysis. It is determined by using image processing and analysis technique using a conversion factor, for improving the inconsistent feature problem caused by different distortions. The imaging system developed, acquires and stores images of paddy seeds. Image digitization methodology was adapted and compared with the traditional manual method. For a digital imaging system to be able to predict the quality of the image, from large varieties of the image quality metrics available, six metrics are used. Correlation coefficient and Root Mean Square Error were used to compare the two methods. The method based on image digitization was found to be faster than the manual method.

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“…Other methods have been developed to measure the surface area of raw agricultural commodities. Sakai and Yonekawa (1991) developed a structured light system for measuring the surface area and volume of soybean seed. They developed regression equations relating seed mass to surface area and seed mass to volume.…”

Section: Introductionmentioning

confidence: 99%