本文選題：小麥條銹病 + 圖像分割��； 參考：《西北農(nóng)林科技大學(xué)》2017年碩士論文

【摘要】：小麥條銹病一直是威脅我國小麥優(yōu)質(zhì)、高產(chǎn)的重要病害,由于對其早期準(zhǔn)確預(yù)測預(yù)報技術(shù)的缺乏,使得小麥條銹病發(fā)病普遍而且嚴(yán)重,而夏孢子菌源數(shù)是影響小麥條銹病發(fā)病和傳播的直接因素。為實(shí)現(xiàn)對田間空氣中小麥條銹菌夏孢子數(shù)量的遠(yuǎn)程實(shí)時快速測定,本研究對實(shí)時在線圖像獲取技術(shù)和顯微圖像處理技術(shù)展開研究,主要研究內(nèi)容及結(jié)論如下:(1)對小麥條銹菌孢子顯微圖像的預(yù)處理、分割和形態(tài)學(xué)處理方法進(jìn)行了研究。利用實(shí)驗(yàn)室條件下所獲得的小麥條銹菌孢子顯微圖像為對象進(jìn)行了試驗(yàn)研究。首先對圖像縮放、顏色空間轉(zhuǎn)換、圖像灰度化等進(jìn)行了研究。然后利用四種不同的分割方法:K-means聚類法、Otsu閾值分割法、Canny邊緣檢測法、分水嶺分割法進(jìn)行了分割對比試驗(yàn)。對比了RGB、L*a*b*、HSV等三種顏色空間下利用不同分量進(jìn)行K-means聚類的效果。通過對試驗(yàn)結(jié)果的分析比較選擇出了最優(yōu)的分割方法,本研究中最優(yōu)分割方法為基于Canny邊緣檢測的分割方法。對分割后的圖像進(jìn)行了形態(tài)學(xué)處理。該研究利用基于Canny邊緣檢測的分割方法實(shí)現(xiàn)了孢子目標(biāo)區(qū)域的分割,通過形態(tài)學(xué)處理得到了邊緣平滑的孢子區(qū)域二值圖像。(2)對小麥條銹菌孢子的識別與計數(shù)方法進(jìn)行了研究。利用單個孢子形狀簡單、形狀因子較大,粘連孢子形狀較復(fù)雜、形狀因子較小的特性,通過形狀因子對單個與粘連孢子進(jìn)行了識別。對識別后的孢子利用兩種計數(shù)方法:平均面積法和角點(diǎn)檢測法進(jìn)行了計數(shù)。對Harris和SUSAN兩種角點(diǎn)檢測算法進(jìn)行了對比試驗(yàn),對經(jīng)典Harris角點(diǎn)檢測算法進(jìn)行了改進(jìn),使其更適合于檢測粘連區(qū)域的邊緣交點(diǎn),通過對比發(fā)現(xiàn)Harris角點(diǎn)檢測法檢測結(jié)果更好。利用127幅400倍放大的顯微圖像和342幅200倍放大的顯微圖像進(jìn)行了孢子計數(shù)實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明平均面積法和角點(diǎn)檢測法的平均計數(shù)準(zhǔn)確率均不小于84%,能夠?qū)︽咦舆M(jìn)行較準(zhǔn)確的計數(shù)。(3)提出了一種小麥條銹菌孢子顯微圖像在線獲取方法。該方法利用真空泵、51單片機(jī)、數(shù)碼顯微鏡、4G無線路由器、Intel微型機(jī)、太陽能供電系統(tǒng)等進(jìn)行小麥條銹菌孢子的捕獲、顯微圖像獲取和圖像無線傳輸,從而實(shí)現(xiàn)了小麥條銹菌孢子顯微圖像的在線獲取。解決了田間小麥條銹菌孢子監(jiān)測的實(shí)時性問題,為進(jìn)一步實(shí)現(xiàn)小麥條銹病的預(yù)測預(yù)報提供了強(qiáng)有力的基礎(chǔ)支撐。(4)設(shè)計了一種小麥條銹菌孢子計數(shù)軟件。利用Matlab的GUI編譯工具箱完成了小麥條銹菌孢子計數(shù)軟件的設(shè)計。對其功能實(shí)現(xiàn)與使用進(jìn)行了詳細(xì)說明。該軟件可利用4種不同的分割方法、2種不同的計數(shù)方法進(jìn)行孢子的計數(shù),使用直觀明了,操作簡單,可在Matlab環(huán)境下運(yùn)行。
[Abstract]:Wheat stripe rust is an important disease that threatens the quality and high yield of wheat in China. Due to the lack of early and accurate prediction technology, stripe rust of wheat is common and serious. The number of summer spores is a direct factor to affect the incidence and transmission of wheat stripe rust. In order to measure the number of summer spores of wheat stripe rust in field air, the real-time on-line image acquisition and microscopic image processing were studied in this study. The main contents and conclusions are as follows: (1) Pretreatment, segmentation and morphological processing of the microscopic image of wheat stripe rust spore were studied. The micrographs of wheat stripe rust spores obtained under laboratory conditions were studied experimentally. Firstly, image scaling, color space conversion and image grayscale are studied. Then, four different segmentation methods, namely: K-means clustering method, Otsu threshold segmentation method, Canny edge detection method and watershed segmentation method, are used to carry out a comparative segmentation experiment. The results of K-means clustering with different components in three color spaces were compared. Through the analysis and comparison of the experimental results, the optimal segmentation method is selected. In this study, the optimal segmentation method is based on Canny edge detection. The segmented image was processed by morphology. In this study, the segmentation method based on Canny edge detection was used to segment the target area of spores. The binary image of spore region with smooth edge was obtained by morphological processing. The method of identifying and counting spores of wheat stripe rust was studied. Based on the simple shape, large shape factor, complex shape and small shape factor of a single spore, a single conidial spore was identified by shape factor. The spores identified were counted by two counting methods: average area method and corner detection method. In this paper, two corner detection algorithms, Harris and SUSAN, are compared, and the classical Harris corner detection algorithm is improved to make it more suitable for detecting the edge intersection of adhesion region. It is found that the Harris corner detection method is better than the traditional Harris corner detection method. Spore counting experiments were carried out using 127 magnified microscopic images and 342 200 magnified microscopic images. The experimental results show that the average counting accuracy of the average area method and the corner detection method are not less than 84, and the spores can be counted accurately. (3) an online method for obtaining the microscopic image of wheat stripe rust spores is proposed. This method uses vacuum pump 51 single chip computer, digital microscope 4G wireless router and Intel microcomputer, solar power supply system to capture wheat stripe rust spores, obtain microscopic images and transmit images wirelessly. Thus, the micrograph of wheat stripe rust spores can be obtained on line. The real-time monitoring problem of wheat stripe rust spores in the field was solved, and a software was designed to calculate the spores of wheat stripe rust. The software of spores counting of wheat stripe rust was designed by using GUI compiler toolbox of Matlab. The implementation and use of its functions are described in detail. The software can use four different segmentation methods and two different counting methods to count spores. The software is intuitive and easy to operate. It can be run in Matlab environment.
【學(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S435.121.42;TP391.41

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