| Project/Area Number |
02660254
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
農業機械
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| Research Institution | SHIMANE UNIVERSITY |
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Principal Investigator |
IWAO Toshio SHIMANE UNIV., AGRIC.FAC., PROFESSOR, 農学部, 教授 (70032547)
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| Co-Investigator(Kenkyū-buntansha) |
FUJIURA Tateshi SHIMANE UNIV., AGRIC.FAC., PROFESSOR, 農学部, 教授 (00026585)
竹山 光一 島根大学, 農学部, 助教授 (20032625)
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| Project Period (FY) |
1990 – 1992
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| Project Status |
Completed (Fiscal Year 1992)
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| Budget Amount *help |
¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 1992: ¥100,000 (Direct Cost: ¥100,000)
Fiscal Year 1991: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1990: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | machine vision / near-infrared image / gray-level / discriminat analysis / superslice algolism / damaged fruit / sorting / spectral reflectance / 領域・エッド併用法 / 青果物 / 傷 / 分光反射特性 / 濃度値 / 選果システム / 人工損傷 / 自然損傷 |
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| Research Abstract |
In the fruits packinghousep the postharvest handling and packaging of fruits has been extensively automated, with the exception of the sorting operation, which continues to be a manual effort. Consequently, automation of the fruit defect sorting has potential for improving product quality, in addition to reducing packinghouse labor costs.
This study dealt with the spectral reflectance characteristics of fruitsurface defects in order to the development of a machine vision sorting system for fruit defects. And the types of fruit defects were bruises, cut, brown rots. copressed and impacted damages.
Detecting condition of peach defects in the visible wavelength region(290 780) were complicated by the variation in color over the surface of the peach. But blush and ground color curves had about same values of spectral reflectance drew the clear distinction between normal and damage of peach surface.
The study on damage fruits inspection dealt with a trial of developing an image analysis algolithms based on NTSC r, g, b chromatictity in color image and the gray level of infrared image to identify defects.
The detecting algolism of defects of peach was developed on the difference of the gray-level between normal and defects, utilizing the method of discriminat analysis and superlices algolithm. Two method had to be investigated in detecting the defects such as worm, scar, burises and aracle. Any one can exactl split the defects from the peach image.
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