本文選題：紅棗 + 機器視覺技術(shù)�。� 參考：《西北農(nóng)林科技大學》2016年碩士論文

【摘要】：紅棗不僅具有豐富的營養(yǎng)價值,而且具有很高的經(jīng)濟效益,紅棗出口在我國水果總出口中占有十分重要的地位,然而現(xiàn)有的紅棗分級方式多采用人工分級和機械式分級,人工分級導致分級質(zhì)量參差不齊、生產(chǎn)效率低下,機械分級易造成機械損傷,極大地影響了紅棗的采后附加值,降低了紅棗產(chǎn)業(yè)的經(jīng)濟效益。因此,實現(xiàn)紅棗快速高效檢測,以保證紅棗的分級質(zhì)量,提高經(jīng)濟效益是廣大果農(nóng)迫切需要解決的問題。本研究以紅棗為研究對象,運用機器視覺技術(shù)和近紅外光譜技術(shù)對紅棗的內(nèi)外部品質(zhì)進行檢測研究。主要研究工作如下:(1)完成了紅棗品質(zhì)在線檢測裝置的搭建,包括機器視覺系統(tǒng)、近紅外光譜系統(tǒng)和傳輸機構(gòu)。用機器視覺系統(tǒng)實現(xiàn)紅棗外部品質(zhì)的檢測,用近紅外光譜系統(tǒng)實現(xiàn)紅棗內(nèi)部品質(zhì)的檢測,傳輸機構(gòu)完成紅棗的輸送工作。(2)完成了基于機器視覺的紅棗外部品質(zhì)分級軟件的設(shè)計,包括圖像數(shù)據(jù)的讀取模塊、設(shè)置模塊、圖像處理模塊以及結(jié)果統(tǒng)計模塊,圖像處理模塊包括圖像預處理模塊、大小檢測模塊、病害檢測模塊和裂紋檢測模塊。采用灰度化、中值濾波以及二值化分割對紅棗圖片進行預處理,采用最小外接矩形法確定紅棗的大小,采用HSI顏色模型中的H分量來判別紅棗的病害區(qū)域,采用I分量來判別紅棗裂紋區(qū)域。并對外部品質(zhì)檢測軟件進行測試,分級正確率達到88.24%。(3)運用近紅外光譜技術(shù)檢測紅棗水分和總糖含量兩個品質(zhì)指標,分別建立了對應(yīng)的定量檢測模型,包括預處理方法、建模方法、特征波長以及多元線性回歸方程的確定,最終得到較高的相關(guān)系數(shù)和預測準確度。并開發(fā)了紅棗內(nèi)部品質(zhì)檢測與分級軟件,主要功能模塊包括光譜數(shù)據(jù)的讀取模塊、有效光譜信息提取及紅棗水分和總糖檢測模塊、分級設(shè)置模塊和系統(tǒng)界面模塊等。開發(fā)近紅外光譜處理軟件,主要包括光譜數(shù)據(jù)的載入、校正集和預測集的選擇、光譜預處理、校正模型的建立、保存,未知樣品預測結(jié)果顯示和保存等功能。
[Abstract]:Red jujube not only has rich nutritional value, but also has high economic benefit. The export of red date occupies a very important position in the total export of fruit in China. However, the existing classification methods of red jujube are mostly classified by artificial and mechanical classification. Artificial grading results in uneven grading quality, low production efficiency and mechanical damage, which greatly affects the post-harvest added value of jujube and reduces the economic benefit of jujube industry. Therefore, it is an urgent problem for the fruit farmers to realize the rapid and efficient detection of jujube, to ensure the classification quality of jujube and to improve the economic benefit. In this study, the internal and external quality of jujube was detected by machine vision and near infrared spectroscopy. The main research work is as follows: (1) the establishment of on-line testing device for jujube quality includes machine vision system, near infrared spectrum system and transmission mechanism. The machine vision system is used to detect the external quality of jujube, the near infrared spectrum system is used to detect the internal quality of red jujube, and the transmission mechanism is used to carry out the transporting work of red jujube. (2) the software for classifying the external quality of red jujube based on machine vision is designed. The image processing module includes image preprocessing module, size detection module, disease detection module and crack detection module. The image of jujube was preprocessed by gray scale, median filter and binary segmentation. The size of jujube was determined by the method of minimum external rectangle. The H component of HSI color model was used to distinguish the disease area of jujube. I component is used to judge the crack area of jujube. The external quality testing software was tested, and the classification accuracy reached 88.24.K3) two quality indexes, water content and total sugar content of jujube, were detected by near infrared spectroscopy, and the corresponding quantitative detection models, including pretreatment methods, were established, respectively. The method of modeling, the determination of characteristic wavelength and multivariate linear regression equation are used to obtain high correlation coefficient and prediction accuracy. The software for internal quality detection and classification of jujube was developed. The main function modules included reading module of spectral data, extracting effective spectral information, detecting moisture and total sugar of jujube, grading setting module and system interface module, etc. Near-infrared spectrum processing software is developed, including spectral data loading, selection of calibration and prediction sets, spectral preprocessing, establishment of calibration model, preservation, display and preservation of unknown sample prediction results, etc.
【學位授予單位】：西北農(nóng)林科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP317.4;S665.1

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