Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/96448
DC FieldValueLanguage
dc.contributor黃國益zh_TW
dc.contributorKuo-Yi Huangen_US
dc.contributor.author連豪勝zh_TW
dc.contributor.authorHao-Sheng Lienen_US
dc.contributor.other生物產業機電工程學系所zh_TW
dc.date2017zh_TW
dc.date.accessioned2019-01-17T07:48:28Z-
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dc.identifier.urihttp://hdl.handle.net/11455/96448-
dc.description.abstract本研究完成一套稻米品種潔淨度自動化辨識系統,其功能包括自動進出料、計數、擷取影像、特徵萃取、品種辨識及建置影像資料庫,並應用此系統針對紅米與六種不同種的稻米進行品種辨識,平均系統效能為113顆/分、平均辨識率為96%。 本研究利用影像處理技術針對稻米種子萃取其形態與色彩特徵,作為貝氏分類器、倒傳遞類神經網路及支持向量機的輸入向量,針對103、104及105年度的台梗9號、台南11號與台梗14號之原原種稻米種子進行訓練與測試,其平均辨識率分別為98.61%、94.33%及93.94%。由結果顯示,本研究開發之稻米品種潔淨度自動化辨識系統可有效地辨識稻米種子品種。zh_TW
dc.description.abstractThis paper presents a novel machine vision-based auto-recognizing system for paddy seed purification. The system comprises an inlet-outlet mechanism, machine vision hardware and software, and control system for recognizing seed varieties. Five varieties and red paddy seeds can be recognized using the system. The average speed of recognition is as high as 113 seeds/min. The results show the accuracies of classification to be 96.0%. The proposed method can estimate the shape and color features of seeds that are provided as input vector of Bayesian, back-propagation neural network, and support vector machine classifiers in order to classify Taichung Sen 10, Taikong 9, Tainan 11, Taikong 14, and Taichung 192 paddy seeds. The results show the accuracies of recognition to be 98.2%, 90.2%, 92.3%, 90.0%, 91.3% as 'yes' and 'no', respectively. The average accuracies are 98.61%, 94.33%, 93.94% to recognize paddy seeds for 2014, 2015, and 2016 years. The experimental results indicate that paddy seeds can be recognized efficiently using the developed system.en_US
dc.description.tableofcontents目錄 摘要 i Abstract ii 目錄 iii 圖目錄 v 表目錄 viii 第一章 緒論 1 1.1 前言 1 1.2 研究動機 1 1.3 研究目標 2 1.4 論文架構 2 第二章 文獻探討 3 2.1 稻米種子介紹 3 2.2 文獻回顧 8 第三章 理論分析 11 3.1 色彩模型 11 3.2 Otsu演算法 13 3.3 Freeman鏈碼邊界描述 14 3.4 凸包演算法 15 3.5 橢圓傅立葉描述子 16 3.6 倒傳遞類神經網路 18 3.7 貝氏分類器 21 3.8 支持向量機 23 第四章 紅米種子辨識系統之初探 28 4.1 稻米種子透光度光源試驗 29 4.2 紅米辨識系統 31 4.3 探討紅米辨識系統之實驗結果 34 第五章 稻米品種潔淨度自動化辨識系統之架構 36 5.1 實驗樣本 36 5.2 實驗設備 38 5.3 稻米種子特徵萃取 45 5.4 稻米種子品種辨識 51 5.5 稻米種子品種辨識軟體 53 5.6 稻米品種潔淨度自動化辨識系統之介紹 55 第六章 結果與討論 57 6.1 稻米種子自動化進出料裝置 57 6.2 稻米品種潔淨度自動化辨識系統 61 6.3 年度分類器 65 第七章 結論與未來展望 76 7.1 結論 76 7.2 未來展望 77 參考文獻 78 附錄A 稻米品種潔淨度自動化辨識系統之硬體規格 81zh_TW
dc.language.isozh_TWzh_TW
dc.rights同意授權瀏覽/列印電子全文服務,2020-08-11起公開。zh_TW
dc.subject稻米種子zh_TW
dc.subject機器視覺zh_TW
dc.subject機器學習zh_TW
dc.subjectpaddy seeden_US
dc.subjectmachine visionen_US
dc.subjectmachine learningen_US
dc.title稻米品種潔淨度自動化辨識系統之研製zh_TW
dc.titleDevelopment of an Auto-Identification System for Paddy Seed Purificationen_US
dc.typethesis and dissertationen_US
dc.date.paperformatopenaccess2020-08-11zh_TW
dc.date.openaccess2020-08-11-
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