| Project/Area Number |
10660243
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
農業機械学
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| Research Institution | Okayama University |
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Principal Investigator |
KONDO Naoshi Faculty of Agriculture, Okayama University, Assistant Professor, 農学部, 助教授 (20183353)
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| Co-Investigator(Kenkyū-buntansha) |
後藤 丹十郎 岡山大学, 農学部, 助手 (40195938)
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| Project Period (FY) |
1998 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 1999: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 1998: ¥2,100,000 (Direct Cost: ¥2,100,000)
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| Keywords | Chrysanthemum / Cut flower / Quality evaluation / Image processing / Neural network / Binary image / キク / 選花 |
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| Research Abstract |
The grading or quality evaluation of chrysanthemum cut flowers is traditionally performed by experts trained in the grading based on their skilled sensibility. In this study, an attempt was made to draw some quantitative criteria from the traditional evaluation process by scoring the quality of chrysanthemum cut flowers. The results revealed that there were significant differences between two sets of scores given by two experts respectively. There were also large difference between the first evaluation and the second one. The measurements were taken for cut flower length, length between flower and the uppermost node, main stem diameter, curvature of main stem, average internode length, area of leaves and stems, and sizes of leaves in order to investigate the relationship between physical features of cut flowers and experts' decision criteria. It seemed that the most of measured physical features were related to experts' decision criteria. No straight applications of these physical features to the grading parameters seem to be possible because there was not enough statistical substantiality. There must be some complex combinations between physical features that make experts decide the quality of individual cut flower. One of the well known functions of neural network is a classifier that can handle this type of problem. Base on the results, several features were selected for input parameters of neural networks whose output parameter was a human evaluation score. The neural networks were trained by KNT (Kalman Neuro Training) method. From the results, it was observed that output value satisfactorily agreed the human evaluation score. The error was less than the human error resulted from the human double check procedure. It was also confirmed that the evaluation by the neural networks with several appropriate features was effective.
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