基于動態(tài)集成的黃瓜葉部病害識別方法
發(fā)布時間：2018-08-15 18:14
【摘要】：對作物病害類型的準確識別是病害防治的前提。為提高病害識別的準確度,以黃瓜葉部病害識別為例,提出一種基于動態(tài)集成的作物葉部病害種類的識別方法。首先利用圖像分塊策略提取病害圖像的75維顏色統(tǒng)計特征,然后采用不一致度量方法對構(gòu)建的10個BP神經(jīng)網(wǎng)絡(luò)單分類器進行差異性度量,并按照差異性大小進行排序,最后根據(jù)分類器的可信度,動態(tài)選擇差異性大的分類器子集對病害圖像進行集成識別。在由512幅白粉病、霜霉病、灰霉病和正常葉片4類黃瓜葉片組織圖像構(gòu)成的測試集上,所提方法的識別錯誤率為3.32%,分別比BP神經(jīng)網(wǎng)絡(luò)、SVM、Bagging、Ada Boost算法降低了1.37個百分點、1.56個百分點、1.76個百分點、0.78個百分點。試驗結(jié)果表明:所提方法能夠?qū)崿F(xiàn)黃瓜葉部病害種類的準確識別,可為其它作物病害的識別提供借鑒。
[Abstract]:Accurate identification of crop disease types is the premise of disease prevention and control. In order to improve the accuracy of disease identification, a method based on dynamic integration was proposed to identify cucumber leaf diseases. Firstly, the 75 dimensional color statistical feature of disease image is extracted by image partitioning strategy, and then the 10 BP neural network single classifiers are measured by using inconsistent measure method, and the difference is sorted according to the size of the difference. Finally, according to the reliability of the classifier, the subsets of the classifier with large difference are dynamically selected for the integrated recognition of the disease image. On a test set of 512 leaf tissue images of powdery mildew, downy mildew, gray mildew and normal leaves of four types of cucumber, The recognition error rate of the proposed method is 3.32, which is 1.37% or 1.56% or 1.76% or 0.78% lower than the BP neural network SVMBaggingAda Boost algorithm respectively. The experimental results showed that the proposed method could be used for accurate identification of cucumber leaf diseases and could be used as a reference for the identification of other crop diseases.
【作者單位】： 北京農(nóng)業(yè)信息技術(shù)研究中心;農(nóng)業(yè)部農(nóng)業(yè)信息技術(shù)重點實驗室;北京工業(yè)大學信息學部;
【基金】：國家自然科學基金項目(61403035、71301011) 北京市自然科學基金項目(9152009)
【分類號】：S436.421;TP391.41
[Abstract]:Accurate identification of crop disease types is the premise of disease prevention and control. In order to improve the accuracy of disease identification, a method based on dynamic integration was proposed to identify cucumber leaf diseases. Firstly, the 75 dimensional color statistical feature of disease image is extracted by image partitioning strategy, and then the 10 BP neural network single classifiers are measured by using inconsistent measure method, and the difference is sorted according to the size of the difference. Finally, according to the reliability of the classifier, the subsets of the classifier with large difference are dynamically selected for the integrated recognition of the disease image. On a test set of 512 leaf tissue images of powdery mildew, downy mildew, gray mildew and normal leaves of four types of cucumber, The recognition error rate of the proposed method is 3.32, which is 1.37% or 1.56% or 1.76% or 0.78% lower than the BP neural network SVMBaggingAda Boost algorithm respectively. The experimental results showed that the proposed method could be used for accurate identification of cucumber leaf diseases and could be used as a reference for the identification of other crop diseases.
【作者單位】： 北京農(nóng)業(yè)信息技術(shù)研究中心;農(nóng)業(yè)部農(nóng)業(yè)信息技術(shù)重點實驗室;北京工業(yè)大學信息學部;
【基金】：國家自然科學基金項目(61403035、71301011) 北京市自然科學基金項目(9152009)
【分類號】：S436.421;TP391.41
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