發(fā)布時(shí)間：2018-02-11 11:25

  本文關(guān)鍵詞： 機(jī)器視覺(jué) 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(jī)器視覺(jué)技術(shù)的蓬勃發(fā)展,國(guó)內(nèi)外專家更傾向于應(yīng)用機(jī)器視覺(jué)技術(shù)進(jìn)行信息的診斷、研究。本文利用機(jī)器視覺(jué)技術(shù),結(jié)合當(dāng)?shù)匦←湁勄?對(duì)小麥葉片的含水量進(jìn)行預(yù)測(cè)研究。結(jié)果表明,該方法用于對(duì)葉片水分進(jìn)行預(yù)測(cè)研究是切實(shí)可靠的,可以為后續(xù)的研究提供重要依據(jù)。本文采用機(jī)器視覺(jué)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(jìn)行檢測(cè)提取出來(lái),實(shí)現(xiàn)了提取特征的無(wú)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(píng)判模型,通過(guò)該模型就可以實(shí)現(xiàn)葉片含水量的預(yù)測(cè)。為了提高該預(yù)測(cè)系統(tǒng)的精度,首先對(duì)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(jìn)行理論性的研究。在對(duì)圖像進(jìn)行預(yù)處理的過(guò)程中,圖像增強(qiáng)部分選用了中值濾波算法,然后直接增強(qiáng)圖像與背景的對(duì)比度,達(dá)到目標(biāo)邊緣清晰同時(shí)目標(biāo)與背景的對(duì)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(jí)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(yùn)算。然后運(yùn)用Matlab編寫(xiě)程序?qū)D像進(jìn)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(píng)價(jià)的綜合指標(biāo),減少單個(gè)參數(shù)對(duì)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(duì)該網(wǎng)絡(luò)進(jìn)行訓(xùn)練和輸出仿真,并對(duì)該網(wǎng)絡(luò)進(jìn)行預(yù)測(cè)驗(yàn)證,預(yù)測(cè)結(jié)果的精度已經(jīng)達(dá)到了96%,達(dá)到預(yù)期的預(yù)測(cè)目標(biāo)。這一結(jié)果表明,基于機(jī)器視覺(jué)小麥葉片含水量的檢測(cè)是可行的,并且可以應(yīng)用到葉片含水量的實(shí)際預(yù)測(cè)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(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;S512.1

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