本文選題：機器視覺　切入點：黃瓜葉片病害　出處：《長江大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：新時期提出的智慧農(nóng)業(yè)讓農(nóng)業(yè)和現(xiàn)代信息技術(shù)有了更密切的結(jié)合,達(dá)到的目標(biāo)就是實現(xiàn)農(nóng)業(yè)科技信息智能化、農(nóng)業(yè)生產(chǎn)經(jīng)營智能化和農(nóng)業(yè)生活智能化。農(nóng)業(yè)物聯(lián)網(wǎng)是智慧農(nóng)業(yè)的一種具體體現(xiàn),而機器視覺技術(shù)又讓農(nóng)業(yè)物聯(lián)網(wǎng)更具智能化。黃瓜是我國蔬菜種植中的主要經(jīng)濟作物,對病害進(jìn)行準(zhǔn)確的識別能做到對病害的預(yù)測預(yù)報及預(yù)防,對促進(jìn)地方的經(jīng)濟發(fā)展有著重要作用。本文借助農(nóng)業(yè)物聯(lián)網(wǎng)和機器視覺技術(shù)理論基礎(chǔ),以識別黃瓜常見的霜霉病、白粉病、褐斑病和炭疽病四種病害為目的,利用圖像處理技術(shù)為主導(dǎo),根據(jù)病害葉片的顏色特征、選擇合適的模式識別技術(shù),研究了以Android智能手機為代表的移動終端通過遠(yuǎn)程診斷來自動識別黃瓜病害,識別效果很好,為黃瓜種植菜農(nóng)提供了一種智能、快捷又便利地判斷病害的新途徑。主要研究內(nèi)容和成果如下：(1)借助物聯(lián)網(wǎng)的三層架構(gòu)和機器視覺技術(shù)的基本思想,確定了本課題的實現(xiàn)思路。本文以Android智能手機作為圖像采集設(shè)備,擔(dān)當(dāng)信息感知層,作為客戶端。網(wǎng)絡(luò)采用現(xiàn)在技術(shù)成熟的局域無線網(wǎng)絡(luò)或移動4G網(wǎng)絡(luò),確保信息的上下行可靠傳輸,擔(dān)當(dāng)網(wǎng)絡(luò)層；由于智能手機對圖像處理的各函數(shù)庫支持不完善,加上計算能力與PC機相比還有很大差距,圖像的處理、病害特征庫的建立及病害的模式識別就交由PC機來實現(xiàn),PC機就是遠(yuǎn)程連接的服務(wù)器端,承擔(dān)應(yīng)用層的各項任務(wù),并把識別處理的結(jié)果反饋手機客戶端。(2) Android智能手機作為客戶端完成的主要功能是實現(xiàn)病害圖像的采集、存儲、裁剪和聯(lián)網(wǎng)上傳及對結(jié)果接受進(jìn)行顯示。圖像的采集調(diào)用智能手機自身的高清攝像頭,采集圖像存儲于本地磁盤；調(diào)用本地磁盤的病害圖像,定位病害突出部分進(jìn)行裁剪,裁剪可以減少遠(yuǎn)程服務(wù)器的計算量,同時也簡化了圖像的增強和去噪聲等圖像預(yù)處理,讓一張清晰的病害圖像上傳到服務(wù)器端。在聯(lián)網(wǎng)上傳的過程中,使用4G網(wǎng)絡(luò),采用http協(xié)議連接遠(yuǎn)程Tomcat服務(wù)器環(huán)境下的web服務(wù)器端,服務(wù)器端采用Struts2框架技術(shù),很好地處理了手機客戶端到服務(wù)器端的數(shù)據(jù)傳輸問題。在Android+Struts2技術(shù)的結(jié)合下,實現(xiàn)了圖像數(shù)據(jù)快速無損傳輸。圖像經(jīng)過遠(yuǎn)程服務(wù)器端的處理和對比識別,最后把識別結(jié)果返回給手機端顯示。(3)構(gòu)建了一套完整的圖像處理流程,快捷又成功的實現(xiàn)圖像的病斑分割。選擇紅色分量灰度圖像進(jìn)行圖像灰度化,得到病斑和背景對比清晰的灰度圖,完成圖像的預(yù)處理；選擇一維最大熵分割法實現(xiàn)灰度圖像的二值化,完成了背景和病斑的圖像分割處理。并用圖像數(shù)學(xué)形態(tài)學(xué)算法處理去掉了分割圖像中的干擾雜點,完善和提升了分割效果,得到了和原彩圖尺寸大小一致的,病斑和背景分離的二值圖像。(4)充分研究病斑的特征,構(gòu)建了病斑的顏色模型,建立了病害特征參數(shù)庫。利用顏色的不同來達(dá)到識別區(qū)分不同事物是機器視覺中最為常見的一種方式。分割后的二值圖像是原彩圖經(jīng)過灰度化和域值化處理而來,二值圖像中的白色區(qū)域代表病斑區(qū)域,正常綠色背景區(qū)就對應(yīng)其黑色區(qū)域。采用位置對應(yīng)法,利用顏色直方圖統(tǒng)計得到彩圖病斑區(qū)域的R、G、B三分量的均值。經(jīng)過實驗分析發(fā)現(xiàn)R、G、B三分量的值會根據(jù)光照的強度變化呈正線性變化,大腦感覺色光的色度由R、G和B三分量之間的相互比值來決定,本文選擇R均值和B均值分別與G均值的比作為選定的顏色特征參數(shù)。采用嵌入式、免安裝、輕便型的SQLite數(shù)據(jù)庫來存儲各病害的特征參數(shù)。根據(jù)各病害的發(fā)病時期不同,病害的顏色表征也不盡相同,采集了各病害的早中期病害葉片圖像樣本多份取特征參數(shù)平均值,最后建立了每一病害早中期的病害特征庫。(5)選擇了適合本課題研究的模式識別方法來識別各病害。根據(jù)建立的顏色特征參數(shù)庫,比較了各種模式識別方法,從中選擇多類別的模板匹配模式識別方法。對四種病害各取30份,利用軟件識別與普通農(nóng)戶及農(nóng)技人員識別對比來分析,優(yōu)勢是明顯的,識別正確率達(dá)到了91.7%。基于移動終端的黃瓜病害智能識別系統(tǒng)的研究,實現(xiàn)了實時、快速、便捷、準(zhǔn)確、無損地進(jìn)行黃瓜葉片病害診斷,解決了傳統(tǒng)人工目測的誤差和錯誤及農(nóng)技人員的缺乏,節(jié)約了勞動力,減少了人為的主觀因素,對病害做到了早知道早預(yù)防早處理,減少經(jīng)濟損失。方法新穎、應(yīng)用潛力大、對精準(zhǔn)農(nóng)業(yè)和智能化農(nóng)業(yè)的發(fā)展有著重要的意義。
[Abstract]:The new era of wisdom agriculture agricultural and modern information technology are combined more closely, to achieve the goal is to achieve intelligent agricultural information, agricultural production and operation of intelligent agriculture and intelligent life. Agricultural IOT is a concrete manifestation of the wisdom of agriculture, and the technology of machine vision and make agricultural things more intelligent. Cucumber is the main economic crops planting vegetables in our country, the disease can do for accurate identification of the prediction and prevention of the diseases and plays an important role in promoting regional economic development. With the help of agricultural IOT and machine vision technology theory, downy mildew, cucumber powdery mildew to identify common. Brown spot and anthracnose of four kinds of diseases for the purpose of using image processing technology as the leading factor, according to the color feature of diseased leaf, choose appropriate pattern recognition technology, research on Android wisdom Can the mobile phone as the representative of the remote diagnosis to automatic recognition of cucumber disease recognition, the effect is very good, provides an intelligent Cucumber Planting Vegetable, fast and convenient way to judge the new disease. The main research contents and results are as follows: (1) the basic idea with the networking of three layer architecture and machine vision technology the determination of the realization of the idea of this topic. Based on the Android intelligent mobile phone as the image acquisition equipment, as the information perception layer, network technology as a client. With the now mature wireless network or mobile 4G network, to ensure the reliable information transmission on the downlink, as the network layer; the intelligent mobile phone on the image processing function library support is not perfect, and the computing power and PC still have a large gap, image processing, pattern recognition and the establishment of disease disease feature library will be handed over to the PC machine, PC machine Is a remote connection to the server for each task in the application layer, and the recognition result of mobile phone client. (2) the main function of Android intelligent mobile phone as the client is the completion of the implementation of disease image acquisition, storage, networking and upload clipping and to display the results. The image acquisition of intelligent mobile phone calls itself HD camera, capture images stored in the local disk; call the local disk disease image, positioning disease projection clipping, clipping can reduce the amount of calculation of the remote server, but also simplifies the image enhancement and the noise of image preprocessing, make a clear disease image uploaded to a server in the process. The network upload, use the 4G network, using HTTP protocol to connect to the remote Tomcat server under the web server, the server adopts the Struts2 frame technology, very good To deal with the problem of data transmission in mobile phone client to server. In combination with Android+Struts2 technology, realizes fast lossless transmission of image data. After image processing and recognition compared to the remote server, and finally the identification results are returned to the mobile phone terminal display. (3) to construct a complete set of image processing, image the lesion is quick and successful segmentation. Choose the red component of image grayscale, contrast clear gray spots and background, complete image preprocessing; binarization choice of one-dimensional maximum entropy segmentation method to realize gray image, complete the image background and lesion segmentation with mathematical morphology and image. To remove the interference of image segmentation algorithm in noise, and improve the segmentation results, and get the original pictures of the same size, lesion and background from the value of two Image. (4) features fully study the lesion, constructed the lesion color model, established the disease characteristic parameter library. Use different colors to achieve the recognition of different things is a common way for the machine vision. After the partition of the two value image is the original image by gray scale and domain value to handle, two values of white areas represent the lesion area in the image, the normal green background area corresponding to the black area. The corresponding position method, using color histogram statistical color lesion regions R, G, B means the three component. After the experimental analysis showed that R, G, B three component values according to the there was a positive linear change of light intensity changes, your brain feels light color by R, their ratio between G and B three components to determine the choice of R mean and B mean respectively with mean G ratio as the color feature parameters selected. Using the embedded, free. The characteristic parameters of each disease, storage of portable SQLite database. According to the different period of onset of disease, disease of color characterization are not the same, the acquisition of early and mid disease disease leaf image samples from multiple copies of characteristic parameters of mean value, finally the disease feature library as early as mid every disease. (5) the pattern recognition method for the research to identify the disease. According to the color feature parameters database, comparison of various methods of pattern recognition, multi category template from matching pattern recognition method. Four kinds of diseases from each of 30 copies, analyzed by the software identification and ordinary farmers and agrotechnicians recognition comparison superiority is obvious, the correct recognition rate of 91.7%. on cucumber disease recognition system based on mobile terminal, real-time, rapid, convenient, accurate, nondestructive of Cucumber Leaves Disease diagnosis, to solve the lack of traditional artificial visual error and error and agrotechnicians, save labor, reduce the subjective factor, the disease did know early prevention and early treatment, reduce the economic loss. The novel method and application potential, is of great significance to the development of precision agriculture and intelligent agriculture.

【學(xué)位授予單位】：長江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41
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本文編號：1590299