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Feature Inspection for Vegetable Seedlings with Machine Vision
To differentiate the growth days and to distinguish cotyledons from leaves of Chinese cabbage acrospires, this study incorporated machine vision in three experimental methods: 1. circular detection rule, and 2. area determination, and 3. Fourier descriptor
By circular detection rule, the cotyledon width of 40 acrospires was measured with days to investigate their relationships. The results showed that the circular detection rule was tenable. However, the circular detection rule was only applicable to cotyledon period. Therefore, the circular detection method was the judgment rule only in primary stage of acrospires. In the follow-up experiments, the accumulated leaf area of 10 to 17-day acrospires was totally transformed into pixels as a judgment basis of growth days. To assess the effect of this method, five of the samples were randomly chosen and investigated 10 times per day. Totally 80 times of investigation were performed in this study. The identification rate was 82.5% maximally by using circular detection rule. Besides, eight-adjacency chain code and outline searching were applied to distinguish cotyledon from leaf. The identification rate was 79.38% maximally. The outline coordinates of the cotyledons and leaves of acrospires were transformed by fast Fourier transformation to investigate the spectra variation with growth days. The cotyledons and leaves can be identified by their differences in outline spectra. Based on the outline spectra of cotyledons, the peak number in different frequency intervals was compared. The identification rate of intact leaves was 72.9% in channel 0-30, 66% in channel 0-40, and 57.2% in channel 0-50, respectively. The results indicated that the wider the interval of sampling, the lower the identification rate.
|Appears in Collections:||生物產業機電工程學系|
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