本文關(guān)鍵詞：基于病斑形狀和神經(jīng)網(wǎng)絡(luò)的黃瓜病害識別，由筆耕文化傳播整理發(fā)布。

賈建楠,吉海彥.基于病斑形狀和神經(jīng)網(wǎng)絡(luò)的黃瓜病害識別[J].農(nóng)業(yè)工程學(xué)報,2013,29(25):115-121.DOI:

基于病斑形狀和神經(jīng)網(wǎng)絡(luò)的黃瓜病害識別

投稿時間：2012-09-23  最后修改時間：2013-04-24

中文關(guān)鍵詞:  病害，識別，神經(jīng)網(wǎng)絡(luò)，病斑形狀，黃瓜

基金項(xiàng)目:

作者單位

賈建楠 中國農(nóng)業(yè)大學(xué)“現(xiàn)代精細(xì)農(nóng)業(yè)系統(tǒng)集成研究”教育部重點(diǎn)實(shí)驗(yàn)室，北京 100083 

吉海彥 中國農(nóng)業(yè)大學(xué)“現(xiàn)代精細(xì)農(nóng)業(yè)系統(tǒng)集成研究”教育部重點(diǎn)實(shí)驗(yàn)室，北京 100083 

摘要點(diǎn)擊次數(shù): 865

全文下載次數(shù): 457

中文摘要:為了研究基于圖像處理的黃瓜病害識別方法，，試驗(yàn)中采集了黃瓜細(xì)菌性角斑病和黃瓜霜霉病葉片進(jìn)行圖像研究。在黃瓜病斑的圖像分割方面，嘗試了邊緣檢測法和最大類間方差法進(jìn)行圖像處理。邊緣檢測法提取出來的病態(tài)部位輪廓不是很完整，而利用最大類間方差法的圖像分割效果較好。試驗(yàn)中提取了10個形狀特征，選取黃瓜細(xì)菌性角斑病和黃瓜霜霉病葉片的各50個樣本，其中每個病害的前30個樣本，共計60個樣本作為訓(xùn)練樣本輸入神經(jīng)網(wǎng)絡(luò)，對2種黃瓜病害葉片的后20個樣本，共計40個樣本進(jìn)行測試，正確識別率達(dá)到了100%，說明通過病斑形狀和神經(jīng)網(wǎng)絡(luò)進(jìn)行黃瓜細(xì)菌性角斑病和黃瓜霜霉病的識別是可行的。

Jia Jiannan,Ji Haiyan.Recognition for cucumber disease based on leaf spot shape and neural network[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2013,29(25):115-121.DOI:

Recognition for cucumber disease based on leaf spot shape and neural network

Author NameAffiliation

Jia Jiannan  

Ji Haiyan  

Key words:diseases, image recognition, neural networks, spot shape, cucumber

Abstract:Disease will seriously affect the yield and quality of cucumber and cause economic losses to farmers. Therefore, the research of recognition for cucumber disease is necessary. In this paper, cucumber disease characteristic parameters were extracted after image processing. Then cucumber diseases were identified using neural network. Cucumber leaves of bacterial angular leaf spot and downy mildew were collected for image recognition. The images of cucumber disease leaves would be processed by using a series of image pre-processing methods, such as image transforming, image smoothing and image segmentation. White was chosen as the background of diseased leaf, median filter was utilized to effectively wipe out the disturbance of noise, and two-apex method was applied to separate the disease images from the background. In the experiment of cucumber lesion site segmentation, this paper attempted to process images by using edge detection method and maximum inter-class variance method. The contour of lesion site extracted by edge detection method was not very complete, while the Image segmentation result by using maximum inter-class variance method was better. First, the lesion site was extracted from R branch image by the method of maximum inter-class variance. The background image was obtained from B branch image by the method of histogram threshold segmentation. The lesion image could be obtained by subtraction of the two images. The shape characteristics of the lesion could be extracted after regional marker. In the experiment of identification for cucumber bacterial angular leaf spot and downy mildew, 10 shape features were extracted. Each class of 30 samples, a total of 60 samples was selected as training samples and input to neural network. After the neural network had been trained, the remaining 20 samples of each class, a total of 40 samples were inputted to the neural network as test samples. The correct recognition rate is 100%. The result of the experiment shows that the identification method for cucumber bacterial angular leaf spot and downy mildew based on lesion site shape and neural network is feasible.

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  本文關(guān)鍵詞：基于病斑形狀和神經(jīng)網(wǎng)絡(luò)的黃瓜病害識別，由筆耕文化傳播整理發(fā)布。