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基于卷積神經(jīng)網(wǎng)絡(luò)的白背飛虱識別方法
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江蘇省科學(xué)技術(shù)廳前瞻性聯(lián)合研究項目(BY2014095)


Automatic Identification Method for Sogatella furcifera Based on Convolutional Neural Network
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    摘要:

    為了實現(xiàn)白背飛虱蟲情信息的自動收集和監(jiān)測,提出一種基于卷積神經(jīng)網(wǎng)絡(luò)的白背飛虱識別方法并進(jìn)行應(yīng)用研究。首先,用改進(jìn)的野外環(huán)境昆蟲圖像自動采集裝置,采集田間自然狀態(tài)下的白背飛虱圖像,對所獲取的圖像進(jìn)行歸一化處理。然后,隨機(jī)選取1/2圖像樣本作為訓(xùn)練集、1/4作為測試集。利用5×5卷積核對訓(xùn)練樣本進(jìn)行卷積操作,將所獲取的特征圖以2×2鄰域進(jìn)行池化操作。再次經(jīng)過卷積操作和3×3鄰域池化操作后,通過自動學(xué)習(xí)獲取網(wǎng)絡(luò)模型參數(shù)和確定網(wǎng)絡(luò)模型參數(shù),得到白背飛虱的最佳網(wǎng)絡(luò)識別模型。試驗結(jié)果顯示,利用訓(xùn)練后的網(wǎng)絡(luò)識別模型,對訓(xùn)練集白背飛虱的識別正確率可達(dá)96.17%,對測試集白背飛虱的識別正確率為94.14%。

    Abstract:

    In order to realize the pest information automatic collection and monitoring for Sogatella furcifera, an automatic recognition method based on convolutional neural network was presented and its application was carried out. The images of Sogatella furcifera were collected in the natural state of the field by using the improved automatic acquisition system for insect images in field environment and the acquired images were normalized. Six hundred of images were randomly selected from the normalized images as training set and three hundred ones were chosen as test set. The convolution operation was performed on the training set with 5×5 convolution kernel and the acquired feature graphs were pooled in a 2×2 neighborhood. After the convolution operation and 3×3 neighborhood pooling operation, the network model parameters were obtained by using automatic learning and the optimal network identification model for Sogatella furcifera was achieved. The experimental results showed that the recognition accuracy for Sogatella furcifera could reach 96.17% for training set, and for test set, the recognition accuracy was 94.14%.

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劉德營,王家亮,林相澤,陳京,於海明.基于卷積神經(jīng)網(wǎng)絡(luò)的白背飛虱識別方法[J].農(nóng)業(yè)機(jī)械學(xué)報,2018,49(5):51-56. LIU Deying, WANG Jialiang, LIN Xiangze, CHEN Jing, YU Haiming. Automatic Identification Method for Sogatella furcifera Based on Convolutional Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2018,49(5):51-56.

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  • 收稿日期:2017-10-13
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  • 在線發(fā)布日期: 2018-05-10
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