【摘要】：根據(jù)作物葉片癥狀準(zhǔn)確、快速檢測(cè)作物病害是防治和控制作物病害的基礎(chǔ)。為準(zhǔn)確檢測(cè)作物葉部病害,在窗閾值中心對(duì)稱局部二值模式(WTCSLBP)的基礎(chǔ)上,提出了一種作物病斑檢測(cè)方法。首先利用自適應(yīng)局部二值模式獲取正常葉片圖像特征并確定病斑判斷閾值,然后將待檢測(cè)葉片圖像分割為大小相同的檢測(cè)窗,并提取同樣特征與閾值進(jìn)行比較,以判斷該檢測(cè)窗是否有病斑。在三種蘋(píng)果病害葉片圖像數(shù)據(jù)庫(kù)上的實(shí)驗(yàn)結(jié)果表明,該方法能夠有效檢測(cè)作物病斑分布特性。與中心對(duì)稱LBP(CS-LBP)和WTCSLBP相比,該方法具有更少的特征維數(shù)和更高的正確識(shí)別率。
[Abstract]:According to the accurate symptom of crop leaves, rapid detection of crop diseases is the basis of preventing and controlling crop diseases. In order to accurately detect crop leaf diseases, a method for detecting crop disease spot was proposed based on window threshold centrosymmetric local binary mode (WTCSLBP). Firstly, the adaptive local binary mode is used to obtain the normal leaf image features and determine the threshold of the disease spot, then the image is divided into the same size detection window, and the same feature is extracted and compared with the threshold value. To determine whether the detection window has disease spots. The experimental results on three apple disease leaf image databases show that this method can effectively detect the distribution characteristics of crop disease spots. Compared with centrosymmetric LBP (CS-LBP) and WTCSLBP, this method has less characteristic dimension and higher correct recognition rate.
【作者單位】： 西北大學(xué)信息科學(xué)與技術(shù)學(xué)院;西北工業(yè)大學(xué)電子信息學(xué)院;西京學(xué)院工程技術(shù)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(No.61473237) 陜西省自然科學(xué)基礎(chǔ)研究計(jì)劃(No.2014JM2-6096)
【分類(lèi)號(hào)】：TP391.41

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1 陳剛;農(nóng)作物病害圖像處理系統(tǒng)[D];沈陽(yáng)理工大學(xué);2008年

2 聶林紅;基于魯棒局部二值模式的紋理圖像分類(lèi)算法研究[D];天津大學(xué);2016年

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本文編號(hào)：2249079