本文關鍵詞： 苜蓿 葉部病害 圖像識別 圖像分割 特征優(yōu)選 支持向量機　出處：《中國農(nóng)業(yè)大學學報》2016年10期 　論文類型：期刊論文

【摘要】：基于圖像處理技術,對4種苜蓿葉部病害進行識別研究。利用結合K中值聚類算法和線性判別分析的分割方法對病斑圖像作分割,獲得了較好的分割效果。結果表明:該分割方法在由4種病害圖像數(shù)據(jù)集整合成的匯總圖像數(shù)據(jù)集上綜合得分的平均值和中值分別為0.877 1和0.899 7;召回率的平均值和中值分別為0.829 4和0.851 4;準確率的平均值和中值分別為0.924 9和0.942 4。進一步提取病斑圖像的顏色特征、形狀特征和紋理特征共計129個,利用樸素貝葉斯方法和線性判別分析方法建立病害識別模型,并結合順序前向選擇方法實現(xiàn)特征篩選,分別獲得最優(yōu)特征子集;同時利用這2個最優(yōu)特征子集,結合支持向量機(Support vector machine,SVM)建立病害識別模型。比較各模型的識別效果,發(fā)現(xiàn)利用所建線性判別分析模型下的最優(yōu)特征子集,結合SVM建立的病害識別模型識別效果最好,訓練集識別正確率為96.18%,測試集識別正確率為93.10%。由此可見,本研究所建基于圖像處理技術的病害識別模型可用于識別上述4種苜蓿葉部病害,為苜蓿病害的診斷和鑒別提供了一定依據(jù)。
[Abstract]:Based on image processing technology to study identification of 4 kinds of alfalfa leaf diseases. For the segmentation of the lesion image segmentation using median method combined with K clustering algorithm and linear discriminant analysis, obtain good segmentation results. The results show that the segmentation method in 4 kinds of diseases by image data integration summary image data sets the mean and median scores were 0.8771 and 0.8997; the mean and median recall rate were 0.8294 and 0.8514; the mean and median accuracy rate were 0.9249 and 0.942, 4. further lesion image color feature extraction, texture features and shape features a total of 129, using Naive Bayesian method and linear discriminant analysis method of disease recognition model, and combined with sequential forward selection method for feature selection, optimal feature subset is obtained respectively; at the same time using the 2 best feature subset, node Combined support vector machine (Support vector machine, SVM) to establish disease recognition model. The model recognition results, found that the use of the linear discriminant analysis model of the optimal feature subset, combined with disease identification model to identify the effect of SVM the best training set recognition correct rate is 96.18%, the correct recognition rate for the test set 93.10%. therefore, this study built the disease recognition model of image processing technology can be used to identify the 4 kinds of Alfalfa Leaf Diseases Based on, provides a basis for the diagnosis and differential diagnosis of diseases of alfalfa.

【作者單位】： 中國農(nóng)業(yè)大學植物保護學院;河北北方學院農(nóng)林科技學院;中國科學院微生物研究所;
【基金】：公益性行業(yè)(農(nóng)業(yè))科研專項經(jīng)費項目(201303057)
【分類號】：S435.4;S126
【正文快照】： 苜蓿被稱為“牧草之王”,是我國最重要的栽培牧草,對畜牧業(yè)有重要價值[1,2]。苜蓿褐斑病(病原Pseudopeziza medicaginis)、銹病(病原Uromycesstriatus)、小光殼葉斑病(病原Pleosphaerulinabriosiana)和尾孢菌葉斑病(病原Cercosporamedicaginis)是4種常見的苜蓿葉部病害,影響植

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1 谷慶魁;基于計算機圖像處理的玉米葉部病害識別系統(tǒng)[D];沈陽理工大學;2008年

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