本文選題：蘋(píng)果 + 病害識(shí)別��； 參考：《西北農(nóng)林科技大學(xué)》2017年碩士論文

【摘要】：蘋(píng)果產(chǎn)業(yè)對(duì)陜西和甘肅等地區(qū)的經(jīng)濟(jì)有著重要的影響,是黃土高原地區(qū)的特色產(chǎn)業(yè)之一。由于得天獨(dú)厚的自然條件,黃土高原地區(qū)的蘋(píng)果品質(zhì)十分優(yōu)良。近年來(lái)蘋(píng)果產(chǎn)業(yè)蓬勃發(fā)展,種植面積不斷擴(kuò)大,但是由于自然災(zāi)害和治理不當(dāng)?shù)仍?蘋(píng)果病蟲(chóng)害頻發(fā),嚴(yán)重影響了蘋(píng)果的產(chǎn)量。針對(duì)黃土高原蘋(píng)果病害頻發(fā)又得不到及時(shí)有效治理的問(wèn)題,本文對(duì)該地區(qū)蘋(píng)果葉面比較常見(jiàn)的病害:斑點(diǎn)落葉病、花葉病和銹病進(jìn)行了相關(guān)的資料調(diào)查和研究,并采集了相應(yīng)的圖像作為樣本,應(yīng)用圖像處理技術(shù)對(duì)圖像進(jìn)行處理和特征的分析,開(kāi)發(fā)出蘋(píng)果葉面病害識(shí)別系統(tǒng),最終實(shí)現(xiàn)3種病害的識(shí)別。本文的主要研究?jī)?nèi)容如下:(1)針對(duì)復(fù)雜背景下的蘋(píng)果葉面病害圖像特點(diǎn),深入研究了圖像預(yù)處理中的去噪和病斑分割問(wèn)題,建立了完整的預(yù)處理流程:采用三段線(xiàn)性法對(duì)圖像進(jìn)行灰度變換,擴(kuò)展灰度動(dòng)態(tài)范圍;利用改進(jìn)的中值濾波方法對(duì)圖像進(jìn)行濾波處理,該方法可以有效地去除噪點(diǎn),增強(qiáng)圖像信息;將圖像由RGB顏色空間轉(zhuǎn)化到L*a*b*顏色空間,并使用K均值聚類(lèi)方法將葉面與背景分割,然后采用改進(jìn)的最大類(lèi)間方差法對(duì)分離出的葉面圖像進(jìn)行分割,得到病斑圖像。實(shí)驗(yàn)表明,這種病斑分割方法可以達(dá)到較為理想的分割效果。(2)研究了蘋(píng)果病害圖像的有效特征的提取,分別從顏色特征、形狀特征和紋理特征三方面對(duì)測(cè)試對(duì)象進(jìn)行實(shí)驗(yàn),提取病斑的H方差并繪制H-S直方圖作為病斑的顏色特征,根據(jù)病斑的幾何特征和Hu不變矩提取病斑的形狀特征,采用灰度共生矩陣對(duì)病斑紋理進(jìn)行分析,從實(shí)驗(yàn)中的22個(gè)特征優(yōu)選出13個(gè)作為分類(lèi)特征參數(shù)。(3)研究模式識(shí)別相關(guān)方法和支持向量機(jī)模型,并通過(guò)與貝葉斯(Bayes)決策方法、人工神經(jīng)網(wǎng)絡(luò)方法優(yōu)缺點(diǎn)的比較,選用基于支持向量機(jī)的病害分類(lèi)模型,根據(jù)一對(duì)一投票策略設(shè)計(jì)出多分類(lèi)情況下支持向量機(jī)的分類(lèi)器模型,測(cè)試并確定其模型參數(shù),對(duì)優(yōu)選出的13個(gè)特征進(jìn)行分類(lèi)訓(xùn)練。(4)本文采用C#代碼做編程實(shí)驗(yàn),并在C#平臺(tái)中加入Matlab程序接口,開(kāi)發(fā)出識(shí)別系統(tǒng)。實(shí)驗(yàn)結(jié)果表明,該分類(lèi)方法能夠?qū)?種蘋(píng)果葉面病害圖像進(jìn)行有效識(shí)別,可以滿(mǎn)足蘋(píng)果病害智能診斷的需要。
[Abstract]:Apple industry has an important impact on the economy of Shaanxi and Gansu provinces and is one of the characteristic industries in the Loess Plateau. Due to the unique natural conditions, the apple quality in the Loess Plateau is very good. In recent years, the apple industry is booming and the planting area is expanding. However, due to natural disasters and improper management, apple diseases and insect pests occur frequently, which seriously affects the production of apple. In order to solve the problem of apple diseases occurring frequently and without timely and effective treatment in the Loess Plateau, this paper investigated and studied the common diseases of apple leaves in this area: speckle disease, mosaic disease and rust disease. The corresponding images were collected as samples, and the image processing technology was used to process and analyze the characteristics of the image. The apple leaf surface disease recognition system was developed. Finally, the recognition of three kinds of diseases was realized. The main contents of this paper are as follows: (1) in view of the characteristics of apple leaf surface disease image under complex background, the problem of de-noising and disease spot segmentation in image preprocessing is deeply studied. A complete preprocessing flow is established: the image is transformed into gray scale by three-segment linear method to extend the dynamic range of gray scale, and the improved median filtering method is used to filter the image, which can effectively remove the noise. Image information is enhanced, the image is transformed from RGB color space to Lena color space, and the leaf surface and background are segmented by K-means clustering method, and then the separated leaf surface image is segmented by the improved maximum inter-class variance method. Get an image of the disease. The experimental results show that this method can achieve an ideal segmentation effect. The effective feature extraction of apple disease image is studied. The experiment is carried out from three aspects: color feature, shape feature and texture feature. The H variance of the disease spot is extracted and the H-S histogram is drawn as the color feature of the disease spot. According to the geometric feature of the disease spot and the Hu invariant moment, the shape feature of the disease spot is extracted, and the gray level co-occurrence matrix is used to analyze the texture of the disease spot. From the 22 features in the experiment, 13 are selected as classification feature parameters.) the correlation method of pattern recognition and support vector machine model are studied, and the advantages and disadvantages of artificial neural network are compared with Bayes Bayes decision method. Based on the support vector machine (SVM) based disease classification model, the classifier model of support vector machine (SVM) is designed according to one-to-one voting strategy, and its model parameters are tested and determined. In this paper, we use C # code to do programming experiment, and add Matlab program interface to C # platform to develop the recognition system. The experimental results show that the classification method can effectively identify the three apple leaf disease images and can meet the need of intelligent diagnosis of apple diseases.
【學(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：S436.611;TP391.41

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