Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/97803
標題: 應用機器視覺於咖啡生豆外觀瑕疵選別裝置之開發
Development of Auto-Sorting Device for Green Coffee Bean Defects with Machine Vision
作者: 標若安
Jo-An Piao
關鍵字: 咖啡生豆瑕疵
選別裝置
機器視覺
貝氏分類器
green coffee bean defects
sorting device
machine vision
Bayes classifier
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摘要: 咖啡生豆的採收、乾燥處理、脫殼、運送、儲存等環節均會產生瑕疵豆,異物與嚴重色彩瑕疵可利用色選機去除,細部瑕疵豆則倚賴人工進行選別,目前尚未有針對人工選別程度瑕疵豆的自動化選別技術。因此,本研究建構一套咖啡生豆瑕疵選別系統進行瑕疵豆的選別,其功能包括:自動進料、影像擷取與儲存、批次生豆色彩訓練、選別強度調整及選別出料,並應用此系統於水洗生豆之批次色彩訓練與選別。 本研究之選別系統針對每顆生豆擷取上視與下視影像,再利用影像處理技術萃取生豆之輪廓形態特徵與色彩特徵,及進行蟲蛀孔偵測。首先萃取16項輪廓形態特徵,以倒傳遞類神經網路進行正常輪廓與割傷破碎輪廓之分類,再以蟲蛀孔判別法則進行蟲蛀孔與深色斑點之偵測與判別。色彩特徵則由灰階影像、RGB色彩影像與CIELAB影像中萃取,並利用K-means聚類法分割CIELAB影像中色彩差異較大之色塊,再由RGB影像對應像素的位置計算色塊之RGB平均值。在訓練模式中,聚類色彩特徵用於建立批次貝氏分類器;選別模式中,聚類色彩特徵用於貝氏分類器中以估算生豆之發酵程度與黑豆程度。 本研究以七支生豆品項進行選別測試,再針對系統出料結果進行人工複檢。其中,本系統正確判別為瑕疵豆之重量,平均為人工複檢總瑕疵豆重量的79 %。本系統誤判為瑕疵豆的樣本重量平均為投入選別總重的1.6 %,於實際應用上可忽略此誤判數量,本系統生豆選別能有效降低人工選別的部分勞力成本。
In this study, an auto-sorting system was developed to detect washed green coffee beans with defects. Defects on green coffee beans are caused during the processes of harvesting, drying, hulling, and transporting. Beans with severe defects can be removed using a color-sorting machine. However, the sorting of high-quality coffee beans has to be conducted manually. Therefore, we developed a sorting system with an aim to reduce labor costs. The system performs functions such as auto-feeding, image capturing, color training and classification on a batch of green beans. In this study, image processing techniques were used to extract contour features and color features based on the appearance of green coffee beans. Moreover, a method that detects defects caused to the beans by insects ( insect defect detection method) was developed. Top and bottom images of each bean were captured using cameras. First, 16 shape features were extracted, and coffee beans were classified into chipped defective beans and not chipped beans by using a back-propagation neural network. Second, the insect defect detection method was used to detect holes and very dark spots on beans. Third, color features were extracted from 8-bit gray images and RGB color images. Cluster-color features were extracted by applying K-means clustering on images in the CIELAB color space and by retrieving mean values from the corresponding areas in RGB images. In the offline training mode, clustering color features were used to train a new Bayes classifier for detecting defective coffee beans in various batches of coffee beans. In the online sorting mode, clustering color features were used to estimate the level of partial sourness and partial blackness in beans by using a pretrained Bayes classifier.  The auto-sorting device was tested for seven batches of green coffee beans, and samples were rechecked manually. Based on the sorting results, the weight of defective beans obtained after sorting with the proposed device was on average 79% that of the defective beans obtained through manual sorting. The verified false alarm rate of defective beans was less than 1.6% for the total weight on average.
URI: http://hdl.handle.net/11455/97803
文章公開時間: 10000-01-01
Appears in Collections:生物產業機電工程學系

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