本文關(guān)鍵詞： 機器視覺 葉片含水量 特征提取 Matlab　出處：《山東農(nóng)業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著機器視覺技術(shù)的蓬勃發(fā)展,國內(nèi)外專家更傾向于應(yīng)用機器視覺技術(shù)進行信息的診斷、研究。本文利用機器視覺技術(shù),結(jié)合當(dāng)?shù)匦←湁勄?對小麥葉片的含水量進行預(yù)測研究。結(jié)果表明,該方法用于對葉片水分進行預(yù)測研究是切實可靠的,可以為后續(xù)的研究提供重要依據(jù)。本文采用機器視覺技術(shù)將在田間采集到的100幅小麥葉片樣本中與含水量相關(guān)性較大的葉片特征進行檢測提取出來,實現(xiàn)了提取特征的無損化和模塊處理的快速化,用BP神經(jīng)網(wǎng)絡(luò)建立含水量評判模型,通過該模型就可以實現(xiàn)葉片含水量的預(yù)測。為了提高該預(yù)測系統(tǒng)的精度,首先對圖像的預(yù)處理、圖像的分割以及圖像的特征提取等關(guān)鍵算法的選擇進行理論性的研究。在對圖像進行預(yù)處理的過程中,圖像增強部分選用了中值濾波算法,然后直接增強圖像與背景的對比度,達到目標(biāo)邊緣清晰同時目標(biāo)與背景的對比度明顯的效果。在圖像分割部分,由于圖像目標(biāo)與背景間的灰度級差別比較明顯,所以選用了Ostu最大類間方差法的圖像二值化運算。然后運用Matlab編寫程序?qū)D像進行特征提取,本研究分別提取了葉片圖像的顏色、紋理及外形算法作為評價的綜合指標(biāo),減少單個參數(shù)對判定的影響,提高綜合判定的精度。最后建立BP神經(jīng)網(wǎng)絡(luò),對該網(wǎng)絡(luò)進行訓(xùn)練和輸出仿真,并對該網(wǎng)絡(luò)進行預(yù)測驗證,預(yù)測結(jié)果的精度已經(jīng)達到了96%,達到預(yù)期的預(yù)測目標(biāo)。這一結(jié)果表明,基于機器視覺小麥葉片含水量的檢測是可行的,并且可以應(yīng)用到葉片含水量的實際預(yù)測中。
[Abstract]:With the rapid development of machine vision technology, experts at home and abroad tend to use machine vision technology to diagnose and study information. The prediction of water content in wheat leaves is studied. The results show that the method is effective and reliable for the prediction of water content in wheat leaves. In this paper, 100 wheat leaf samples collected in the field were extracted by machine vision technology, which had a high correlation with water content. In order to improve the accuracy of the prediction system, the model of water content evaluation can be established by BP neural network, and the prediction of leaf water content can be realized by the model. Firstly, the selection of key algorithms, such as image preprocessing, image segmentation and image feature extraction, is theoretically studied. In the process of image preprocessing, the median filter algorithm is used in the image enhancement part. Then the contrast between image and background is enhanced directly to achieve the effect that the edge of the target is clear and the contrast between the target and the background is obvious. In the image segmentation part, because the gray level difference between the image object and the background is obvious, Therefore, the binarization operation of the maximum inter-class variance method of Ostu is selected, and then the feature extraction of the image is carried out by using the Matlab program. In this study, the color, texture and shape algorithm of the leaf image are extracted as the comprehensive evaluation indexes, respectively. The influence of single parameter on the decision is reduced, and the accuracy of comprehensive judgment is improved. Finally, BP neural network is established, the network is trained and simulated, and the network is forecasted and verified. The accuracy of the predicted results has reached 96%, which indicates that the water content detection of wheat leaves based on machine vision is feasible and can be applied to the actual prediction of leaf water content.
【學(xué)位授予單位】：山東農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;S512.1

【參考文獻】

相關(guān)期刊論文 前10條

1 王娟;危常州;王肖娟;朱齊超;朱金龍;王金鑫;;采用灰板校正的計算機視覺預(yù)測棉花葉綠素含量[J];農(nóng)業(yè)工程學(xué)報;2013年24期

2 彭文;李慶武;霍冠英;朱建春;趙小方;;基于計算機視覺的植物水分脅迫狀況監(jiān)測方法[J];科學(xué)技術(shù)與工程;2013年09期

3 鄧金蓮;李成貴;;結(jié)合區(qū)域生長和SUSAN的圖像分割方法[J];電光與控制;2012年08期

4 聶桂軍;張振峰;夏玉敏;梁菊華;楊立力;呂蓮;;基于數(shù)學(xué)形態(tài)學(xué)與均值方差的指紋圖像分割算法[J];南京工業(yè)職業(yè)技術(shù)學(xué)院學(xué)報;2011年04期

5 陳兵旗;孫旭東;韓旭;劉燕德;;基于機器視覺的水稻種子精選技術(shù)[J];農(nóng)業(yè)機械學(xué)報;2010年07期

6 馬彥平;白由路;高祥照;盧艷麗;杜君;;基于數(shù)字圖像的玉米葉面積測量方法研究[J];中國農(nóng)學(xué)通報;2009年22期

7 侯瑞;吉海彥;饒震紅;申兵輝;;基于近紅外光譜的活體植物葉片水分檢測儀器[J];農(nóng)業(yè)工程學(xué)報;2009年S2期

8 厲謹;李立;;遺傳算法在數(shù)字圖像分割中的應(yīng)用[J];科技風(fēng);2009年11期

9 孫瑞東;于海業(yè);于常樂;張蕾;;基于圖像處理的黃瓜葉片含水量無損檢測研究[J];農(nóng)機化研究;2008年07期

10 史智興;程洪;李江濤;馮娟;;圖像處理識別玉米品種的特征參數(shù)研究[J];農(nóng)業(yè)工程學(xué)報;2008年06期

相關(guān)碩士學(xué)位論文 前1條

1 張曉飛;基于計算機視覺的棉花群體生長指標(biāo)檢測[D];北京郵電大學(xué);2007年

，

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