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-Lai2006-09-282018-07-102006-09-282018-07-102005-03http://ntur.lib.ntu.edu.tw//handle/246246/2006092712295021044417. 農業機械學刊第14 卷第1 期2005 年3 月. 參數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，本文通訊作者Considering different shape factors and color intensities of five varieties of paddy rice as parameters, four models were established by a back-propagation neural network and were used to study the validation and classification rates affected by choosing different parameters. With 60 parameters, the average validation and classification rates were 92.24% and 92.0% respectively. If the most effective 50 parameters were chosen by loading values in the first principal component, the average validation and classification rates were 91.77 % and 90.0% respectively. 30 parameters selected from the correlation coefficient matrix to build up the model, the average validation and classification rates were 89.18 % and 91.4% respectively. If the most effective 20 parameters were chosen from model training, the average validation and classification rates were 90.59 % and 91.8% respectively, which could be the best model for classifying due to its less parameters and better stability.application/pdf3061410 bytesapplication/pdfzh-TW機器視覺類神經網路水稻參數選擇鑑別Machine visionArtificial neural networkPaddy riceParameter selectionClassificationjournal articlehttp://ntur.lib.ntu.edu.tw/bitstream/246246/20060927122950210444/1/14-1-3.pdf