國立臺灣大學生物產業機電工程學系劉昌群蕭介宗彭敬益洪梅珠沈明來Liu, Chang-ChunChang-ChunLiuShaw, Jai-TsungJai-TsungShawPoong, Keen-YikKeen-YikPoongHong, Mei-ChuMei-ChuHongShen, Ming-LaiMing-LaiShen2006-09-282018-07-102006-09-282018-07-102005-06http://ntur.lib.ntu.edu.tw//handle/246246/2006092712300227314427. 農業機械學刊第14 卷第2 期2005 年6 月. 近紅外線光譜的波長選擇對水稻品種. 鑑別的影響. 劉昌群. 1. ，蕭介宗. 2. ，彭敬益. 3. ，洪梅珠. 4. ，沈明來. 5. 1. 國立台灣大學生物產業機電工程學系研究生. 2. 國立台灣大學生物產業機電工程學系教授，Five varieties of paddy rice were examined using the reflectance spectra corresponding to a selected wavelength from 1100 to 2500 nm in 3-nm steps to determine the classification rate effect. Three hundred fifty-one variables were used to develop the discriminant analysis and neural network models. The average classification rates were 99.69% and 97.69%, respectively. Sixty-two variables were selected using stepwise discrimination to develop the discriminant analysis and neural network models. The average classification rates were 98.0% and 92.76%, respectively. Sixty-two variables were selected using the correlation matrix to develop the discriminant analysis and neural network models. The average classification rates were 90.15% and 84.26%, respectively. Sixty-two variables were selected by loading the first and second principal components to develop the discriminant analysis and neural network models. The average classification rates were 89.38% and 82.25%, respectively. The stepwise discrimination method was more effective in classifying the five varieties of paddy rice using near infrared spectra.application/pdf1965078 bytesapplication/pdfzh-TW近紅外線類神經網路水稻變數選擇鑑別Near infraredArtificial neural networkPaddy riceVariable selectionClassificationjournal articlehttp://ntur.lib.ntu.edu.tw/bitstream/246246/20060927123002273144/1/14-2-3.pdf