本文關鍵詞： 馬鈴薯典型病害 Hough變換 主成分分析 加權融合 支持向量機　出處：《農(nóng)業(yè)機械學報》2017年12期 　論文類型：期刊論文

【摘要】：針對自然條件下馬鈴薯典型病害區(qū)域定位和識別難的問題,提出了一種馬鈴薯典型病害圖像的自適應特征融合與快速識別方法。該方法利用K-means、Hough變換與超像素算法定位葉片,結合二維Otsu與形態(tài)學法分割病斑區(qū)域,通過病斑圖像顏色、形狀、紋理的自適應主成分分析(PCA)特征加權融合,進行支持向量機(SVM)病害識別。對3類馬鈴薯典型病害圖像進行識別試驗,結果表明:SVM識別模型下,自適應特征融合方法相比PCA降維、特征排序選擇等傳統(tǒng)自適應方法,平均識別率至少提高了1.8個百分點;13個自適應融合特征下,識別方法平均識別率為95.2%,比人工神經(jīng)網(wǎng)絡、貝葉斯分類器提高了3.8個百分點和8.5個百分點,運行時間為0.600 s,比人工神經(jīng)網(wǎng)絡縮短3 s,可有效保證識別精度,大大加快了識別速度。
[Abstract]:An adaptive feature fusion and fast recognition method based on K-means-Hough transform and super-pixel algorithm is proposed to locate the leaves of potato typical diseases. Combining two-dimensional Otsu and morphological method to segment the disease spot region, the adaptive principal component analysis (PCA) method of image color, shape and texture is used for weighted fusion. Three kinds of typical potato disease images are identified by using support vector machine (SVM). The results show that the adaptive feature fusion method is better than the traditional adaptive methods such as PCA dimension reduction, feature ranking selection and so on. The average recognition rate is at least 1.8 percentage points higher than that of the artificial neural network, and the average recognition rate of 13 adaptive fusion features is 95.2 percentage points, which is 3.8 percentage points and 8.5 percentage points higher than that of the artificial neural network and Bayesian classifier. The operating time is 0.600 s, which is 3 s shorter than that of artificial neural network, which can effectively guarantee the recognition accuracy and greatly accelerate the recognition speed.
【作者單位】： 內(nèi)蒙古工業(yè)大學電力學院;
【基金】：國家自然科學基金項目(61661042) 內(nèi)蒙古自治區(qū)自然科學基金項目(2015MS0617)
【分類號】：S435.32;TP391.41

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本文編號：1552980