刘德营,王家亮,林相泽,陈京,於海明.基于卷积神经网络的白背飞虱识别方法[J].农业机械学报,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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基于卷积神经网络的白背飞虱识别方法   [下载全文]
Automatic Identification Method for Sogatella furcifera Based on Convolutional Neural Network   [Download Pdf][in English]
投稿时间:2017-10-13  
DOI:10.6041/j.issn.1000-1298.2018.05.006
中文关键词:  白背飞虱  识别  卷积神经网络
基金项目:江苏省科学技术厅前瞻性联合研究项目(BY2014095)
作者单位
刘德营 南京农业大学 
王家亮 南京农业大学 
林相泽 南京农业大学 
陈京 南京农业大学 
於海明 南京农业大学 
中文摘要:为了实现白背飞虱虫情信息的自动收集和监测,提出一种基于卷积神经网络的白背飞虱识别方法并进行应用研究。首先,用改进的野外环境昆虫图像自动采集装置,采集田间自然状态下的白背飞虱图像,对所获取的图像进行归一化处理。然后,随机选取1/2图像样本作为训练集、1/4作为测试集。利用5×5卷积核对训练样本进行卷积操作,将所获取的特征图以2×2邻域进行池化操作。再次经过卷积操作和3×3邻域池化操作后,通过自动学习获取网络模型参数和确定网络模型参数,得到白背飞虱的最佳网络识别模型。试验结果显示,利用训练后的网络识别模型,对训练集白背飞虱的识别正确率可达96.17%,对测试集白背飞虱的识别正确率为94.14%。
LIU Deying  WANG Jialiang  LIN Xiangze  CHEN Jing  YU Haiming
Nanjing Agricultural University,Nanjing Agricultural University,Nanjing Agricultural University,Nanjing Agricultural University and Nanjing Agricultural University
Key Words:Sogatella furcifera  identification  convolutional neural network
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%.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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