本文關(guān)鍵詞： 圖像采集 灰度直方圖 檢測與分割 特征向量 識別準(zhǔn)確率　出處：《西安科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,隨著我國經(jīng)濟的不斷發(fā)展,農(nóng)業(yè)也隨之迅速發(fā)展起來,人們對農(nóng)作物的產(chǎn)量也越來越重視。目前,如何提高農(nóng)作物的產(chǎn)量是人們主要討論的一個問題。病害圖像的檢測與識別作為病害圖像系統(tǒng)的組成部分,對農(nóng)作物病害的診斷與防治起著至關(guān)重要的作用,同時也可以提高農(nóng)作物的產(chǎn)量,增加人們的收入。因此,對病害圖像的分類識別做更進一步的研究會給人們的生活帶來一定的實際意義。作物病害種類的正確識別是病害預(yù)防的前提。本論文借助圖像采集裝置收集了蘋果樹葉三類常見的病害圖像。首先,對收集的病害圖像進行預(yù)處理,選擇直方圖均衡化、中值濾波分別實現(xiàn)了圖像的增強、去噪,再根據(jù)病害圖像的灰度直方圖通過雙峰法確定閾值對病害圖像先進行粗分割,之后再分析病害圖像灰度直方圖的特點對其進行精分割,并對圖像的分割算法做相應(yīng)的改進,從而完成病害圖像的檢測與分割。其次,通過分析對比幾種彩色空間優(yōu)劣,選擇RGB和HSI彩色空間中提取分割出的病害圖像的顏色特征,再依據(jù)灰度共生矩陣提取分割出的病害圖像的紋理特征,并借助主成分分析法選取最具代表性的特征值對問題進行分析研究。利用這些計算簡便且能反映病害本質(zhì)特點的特征來組成特征向量,并構(gòu)建樣本特征數(shù)據(jù)庫。最后,通過兩種分類方法的對比,選擇更適合本文病害圖像分類的支持向量機模型,再通過設(shè)計分類算法,選擇粒子群優(yōu)化算法對支持向量機模型的參數(shù)進行優(yōu)選,且對比不同參數(shù)下病害圖像的識別準(zhǔn)確率,選取識別準(zhǔn)確率較高的參數(shù)建立病害圖像分類識別模型。借助SPSS軟件和MATLAB編程進行實驗。利用Fisher判別分析法對病害圖像進行分類,分類識別率為92.667%.再運用LIBSVB軟件包和支持向量機模型對病害圖像進行分類。首先,對本文中提取的病害圖像的29個特征值不進行優(yōu)化得到,病害圖像的分類識別率為89.3939%.其次,借助主分量分析的方法選擇了9個具有代表性的成分進行實驗,分類準(zhǔn)確率為92.4246%.最后,對模型的參數(shù)錯分懲罰常數(shù)c和非負的松弛項g進行優(yōu)選,得出當(dāng)c(28)1618.28,g(28)039866.0時,此模型對病害圖像的分類準(zhǔn)確率為96.969%.達到了預(yù)期的結(jié)果。
[Abstract]:In recent years, with the continuous development of our economy, agriculture has also developed rapidly, and people pay more and more attention to the production of crops. How to improve the yield of crops is a major issue discussed by people. As a part of disease image system, the detection and recognition of disease image plays an important role in the diagnosis and control of crop diseases. At the same time, it can also increase the yield of crops and increase people's income. Further research on classification and recognition of disease images will bring some practical significance to people's lives. The correct identification of crop diseases is the premise of disease prevention. In this paper, we collect apple with the help of image acquisition device. Three kinds of common disease images of fruit tree leaves. First of all. The disease images were preprocessed, histogram equalization and median filter were used to realize image enhancement and denoising respectively. Then according to the disease image gray histogram through the double peak method to determine the threshold value of the disease image first rough segmentation, and then analyze the disease image gray histogram characteristics of the fine segmentation. And the image segmentation algorithm is improved to complete the disease image detection and segmentation. Secondly, through the analysis and comparison of several color space advantages and disadvantages. Select the color feature of the disease image extracted from RGB and HSI color space, then extract the texture feature of the disease image according to the gray level co-occurrence matrix. With the help of principal component analysis (PCA), the most representative eigenvalues are selected to analyze and study the problem. The characteristics which are simple and can reflect the essential characteristics of the disease are used to form the eigenvector. Finally, through the comparison of the two classification methods, the support vector machine model which is more suitable for the classification of disease image is selected, and then the classification algorithm is designed. Particle swarm optimization algorithm is selected to optimize the parameters of support vector machine (SVM) model, and the recognition accuracy of disease images under different parameters is compared. The classification and recognition model of disease image is established by selecting the parameters with high recognition accuracy. The experiment is carried out by means of SPSS software and MATLAB programming. The disease image is classified by Fisher discriminant analysis. The classification recognition rate is 92.667. then using LIBSVB software package and support vector machine model to classify the disease image. First. The 29 eigenvalues of the disease image extracted in this paper are not optimized. The classification and recognition rate of the disease image is 89.39. Secondly. With the method of principal component analysis, 9 representative components were selected for experiment, and the classification accuracy was 92.4246. Finally. The model parameters are optimized for the penalty constant c and the non-negative relaxation term g, and the results show that when cn281618.28g / g) 039866.0, the model parameters are deviated from the penalty constant (c) and the non-negative relaxation term (g). The classification accuracy of the model is 96.9699.The expected result is achieved.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：S436.611;TP391.41

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本文編號：1477295