基于机器视觉的农田害虫快速检测与识别研究
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摘要
害虫快速检测与识别是农作物病虫害防治的基础。传统的害虫分类和识别主要是专家通过肉眼观察害虫的外部特征并与模式标本对照来完成的,这种识别方法费时费力。随着计算机技术的发展,人们逐渐将图像处理技术与模式识别技术应用到害虫的研究和识别中,并建立害虫的识别系统,丰富了识别手段,提高了识别效率。
本文以农田典型害虫作为研究对象,采用数字图像处理技术和模式识别技术研究了害虫图像的分割、特征提取、分类器分类等方面技术问题,并在此基础上结合3G无线网络技术建立了基于物联网的昆虫远程自动识别系统。本论文的主要研究工作如下：
(1)设计了害虫图像采集系统。本论文研究的害虫尺寸差异较大,同一害虫姿态各异,而且害虫的活动能力较强。为此,本论文研究了满足两种需求的害虫采集系统。一种系统采集诱捕到的害虫的图像。该系统对象的特点是目标静止、目标到镜头的距离固定、视野范围固定,因此,该系统使用CMOS相机和定焦镜头。另一种系统实时采集田间害虫的图像。该系统对象的特点是目标运动、目标到镜头的距离可变、视野范围可变,因此,该系统使用CCD相机和变焦镜头。
(2)提出了基于HSV颜色模型的害虫图像分割技术。本论文针对害虫图像背景和目标颜色的特点,将基于HSV颜色模型的Otsu阈值分割方法应用到背景和目标的分割中。在进行图像分割前,将图像的RGB模型转换成HSV模型,并且将转换得到的H分量旋转180度后利用Otsu算法自适应找到阈值,从而实现了背景和目标的分离。最后,对分割后的图像进行了一些后续处理,得到完整的害虫目标。该分割技术克服了采用害虫RGB原图进行分割时,有较多背景错分为口标的不足。
(3)研究了害虫图像多特征提取技术和特征选择技术。根据害虫的形态特点,提取了目标对象的几何形状特征和矩特征两类形态特征参数；根据害虫之间颜色的差别,提取了害虫的颜色矩作为颜色特征；根据害虫纹理特点,提取了基于灰度共生矩阵的害虫的纹理特征。这些特征共同组成了35个低层视觉特征。研究了基于蚁群算法的特征选择技术,将原始的35维特征降低到29维,识别准确率从87.4%提高到89.5%。本文将近年来图像处理领域的研究热点——SIFT局部特征的提取方法应用于害虫图像的特征提取中。害虫的局部特征具有旋转、平移和尺度不变的特性、对光照变化不敏感且不依赖于背景分割,适合提取在自然光和复杂背景下获得的害虫图像的特征值。将局部特征应用到昆虫的分类识别中·,既拓宽了局部特征提取技术的应用领域,又给昆虫的特征值获取提供了新思路和新方法。
(4)研究了害虫图像识别技术。本文采用SVM模式识别方法建立害虫的识别模型。介绍了常见模式识别方法,详细分析了支持向量机(SVM)的理论研究基础和基本方法。本文通过不同特征组合的识别试验验证采用的特征提取技术和模式分类技术的有效性。采用由形态特征、颜色特征和纹理特征组成的低层视觉特征的正确识别率为85%以上；采用经过蚁群优化的低层视觉特征子集的正确识率为89.5%；采用原图的SIFT特征的正确识别率为79.2%。试验结果表明蚁群优化算法能够消除特征间的相关性、剔除冗余特征、提高识别率。同时,试验结果也表明局部特征提取方法可以尝试应用于不进行背景分割而直接提取害虫特征值的研究中。
(5)研究构建了基于物联网的害虫远程智能识别系统。研究了包括稻纵卷叶螟、斜纹夜峨、玉米螟、大螟、稻螟蛉、二化螟、金龟子、小地老虎、黄杨绢野螟、蝼蛄、桃蛀螟和白背飞虱等12种典型农田害虫的图像分割、特征值提取技术,并利用SVM分类器完成了分类识别；在上述研究基础上设计了基于物联网的害虫远程自动识别系统。系统通过3G无线网络组成一个主控端和多个远端的分布式识别网络,系统既能够在远端自动识别害虫,也能够在远端将害虫图像压缩后,通过3G无线网络将图片传输到主控端,在主控端进行自动识别。系统通过读入本地磁盘保存的图片实现动态扩充样本库的功能。同时、系统设计了专家识别的接口,使专家能够对本系统识别后的害虫图片进行观测分析,并和系统识的结果进行比较。该系统采用在自然光、姿态随机的状态下获得的害虫图像建模,识别模型具有较好的泛化能力,克服了现有大多研究中因采用标准样本图像建立识别模型而导致推广能力较差的不足。
The fast detection and recognition of pests is the basis of crop pest and disease control in agriculture. Traditionally, experts observe the external features of pests and then compare these features with specimens while identifying pests, which is time-consuming and labor exhaustive. With the development of computer technology, image processing technology and pattern recognition technology were used to the research and identification of pests gradually and the identification system of pests was established, which not only enriched the means of identification but also improved the efficiency of identification.
Typical agricultural pests were studied as research objects in this thesis. Image segmentation, feature extraction, classification, etc. were studied based on pests' images with the technologies of digital image processing, pattern recognition. A remote automatic insect recognition system based on internet of things was established based on these results and technologies through3G wireless network. The main research contents of thesis are as follows:
(1) The image acquisition system was designed. The length of pests in this research have great difference, the same pest always has different attitude and pests have strong activity ability. Therefore, the pest image acquisition systems which meet two kinds of demand were designed. The first system was designed to get image of trapped pests which were stationary. The distance of pests from the system and the scope are fixed. Therefore, the camera uses the mode of CMOS line scan and the focal length of camera lens is fixed. The second system was designed to get pest images in the field real-time. The pests were active, the distance of pests from the system can change and the scope is fixed. Therefore, the camera uses the mode of CCD line scan and the focal length of camera lens is adjusted.
(2) The segmentation technology of pest images based on HSV color model was put forward. According to the characteristics of the sample background and objectives, the Otsu threshold segmentation method based on HSV color model was applied. The RGB model was transformed to HSV model before segmentation and the Otsu algorithm was applied to H component of pest images for getting the threshold adaptively. To get the whole pests, the subsequent processing was done after finishing the segmentation. The applied method overcame the disadvantages that many backgrounds were misclassified as target when using the pest RGB image.
(3) Multi-feature extraction and feature selection technologies of pest images were studied. The visual features including the morphological characteristics, color characteristics and texture feature were extracted and the redundant features were eliminated through the Ant colony optimization algorithm(ACO). With the ACO, the35dimensional features were lower to29dimensions and the recognition accuracy was improved from87.4%to89.5%. The SIFT algorithm which is considered easy to use by all in recent years was applied to extract local features of pests in this study. The local features are invariant to image scaling, translation, rotation, partially invariant to illumination changes and affine or3D projection and independent on the background segmentation which are suitable for the extraction of pest image features obtained in natural light and complex background. The application of the local feature in the identification of pests not only expanded the application field of the local feature but also provided new ideas and new methods for pest feature extraction.
(4) The method of pest pattern recognition was studied. The SVM model was used to identify the pests. The theory of foundations and basic methods of the support vector machine(SVM) were elaborated. The different identification experiments using different pest features were done and the test accuracies were compared in this thesis. The accuracy using morphological characteristics, color features and texture features was over85%. The accuracy using the local features obtained by SIFT algorithm in original RGB images was79%. The test results showed that the ant colony optimization algorithm can eliminate the correlation of image feature and improve the accuracy. At the same time, the test results showed that the local feature extraction method can be applied to obtain the features of pests with no background segmentation.
(5) The pest remote automatic identification system based on internet of things was designed. The thesis took twelve kinds of typical agricultural pests including Cnaphalocrocis medinalis Guenee, Prodenia litura, Chilo suppressalis, Sesamia inferens, Anomala corpulenta Motschulsky, Ostrinia nubilalis, Naranga aenescm, Sogatella furcifera, Agrotis ypsilon Rottemberg, Gryllotalpa orientalis Burmeister, Diaphania perspectalia and Conogethes punctiferalis as the object of study. The system included one host control platform and more remote platforms which formed a distributed identification network through3G wireless network. The identification process can be finished in remote automatically and can also be done in the host control platform after the pest images were compressed and transmitted to the host control platform through3G network. The system has the function of expanding the sample library dynamically by getting the image from the local disks. The system also included the expert identification interface. The expert can identify the pests which have been classified by the system automatically and can compare the identification results through the interface. Because of adopting the pest images obtaining in natural light and random pest attitudes, the recognition model has strong generalization ability which is superior to research existing.
本文以农田典型害虫作为研究对象,采用数字图像处理技术和模式识别技术研究了害虫图像的分割、特征提取、分类器分类等方面技术问题,并在此基础上结合3G无线网络技术建立了基于物联网的昆虫远程自动识别系统。本论文的主要研究工作如下：
(1)设计了害虫图像采集系统。本论文研究的害虫尺寸差异较大,同一害虫姿态各异,而且害虫的活动能力较强。为此,本论文研究了满足两种需求的害虫采集系统。一种系统采集诱捕到的害虫的图像。该系统对象的特点是目标静止、目标到镜头的距离固定、视野范围固定,因此,该系统使用CMOS相机和定焦镜头。另一种系统实时采集田间害虫的图像。该系统对象的特点是目标运动、目标到镜头的距离可变、视野范围可变,因此,该系统使用CCD相机和变焦镜头。
(2)提出了基于HSV颜色模型的害虫图像分割技术。本论文针对害虫图像背景和目标颜色的特点,将基于HSV颜色模型的Otsu阈值分割方法应用到背景和目标的分割中。在进行图像分割前,将图像的RGB模型转换成HSV模型,并且将转换得到的H分量旋转180度后利用Otsu算法自适应找到阈值,从而实现了背景和目标的分离。最后,对分割后的图像进行了一些后续处理,得到完整的害虫目标。该分割技术克服了采用害虫RGB原图进行分割时,有较多背景错分为口标的不足。
(3)研究了害虫图像多特征提取技术和特征选择技术。根据害虫的形态特点,提取了目标对象的几何形状特征和矩特征两类形态特征参数；根据害虫之间颜色的差别,提取了害虫的颜色矩作为颜色特征；根据害虫纹理特点,提取了基于灰度共生矩阵的害虫的纹理特征。这些特征共同组成了35个低层视觉特征。研究了基于蚁群算法的特征选择技术,将原始的35维特征降低到29维,识别准确率从87.4%提高到89.5%。本文将近年来图像处理领域的研究热点——SIFT局部特征的提取方法应用于害虫图像的特征提取中。害虫的局部特征具有旋转、平移和尺度不变的特性、对光照变化不敏感且不依赖于背景分割,适合提取在自然光和复杂背景下获得的害虫图像的特征值。将局部特征应用到昆虫的分类识别中·,既拓宽了局部特征提取技术的应用领域,又给昆虫的特征值获取提供了新思路和新方法。
(4)研究了害虫图像识别技术。本文采用SVM模式识别方法建立害虫的识别模型。介绍了常见模式识别方法,详细分析了支持向量机(SVM)的理论研究基础和基本方法。本文通过不同特征组合的识别试验验证采用的特征提取技术和模式分类技术的有效性。采用由形态特征、颜色特征和纹理特征组成的低层视觉特征的正确识别率为85%以上；采用经过蚁群优化的低层视觉特征子集的正确识率为89.5%；采用原图的SIFT特征的正确识别率为79.2%。试验结果表明蚁群优化算法能够消除特征间的相关性、剔除冗余特征、提高识别率。同时,试验结果也表明局部特征提取方法可以尝试应用于不进行背景分割而直接提取害虫特征值的研究中。
(5)研究构建了基于物联网的害虫远程智能识别系统。研究了包括稻纵卷叶螟、斜纹夜峨、玉米螟、大螟、稻螟蛉、二化螟、金龟子、小地老虎、黄杨绢野螟、蝼蛄、桃蛀螟和白背飞虱等12种典型农田害虫的图像分割、特征值提取技术,并利用SVM分类器完成了分类识别；在上述研究基础上设计了基于物联网的害虫远程自动识别系统。系统通过3G无线网络组成一个主控端和多个远端的分布式识别网络,系统既能够在远端自动识别害虫,也能够在远端将害虫图像压缩后,通过3G无线网络将图片传输到主控端,在主控端进行自动识别。系统通过读入本地磁盘保存的图片实现动态扩充样本库的功能。同时、系统设计了专家识别的接口,使专家能够对本系统识别后的害虫图片进行观测分析,并和系统识的结果进行比较。该系统采用在自然光、姿态随机的状态下获得的害虫图像建模,识别模型具有较好的泛化能力,克服了现有大多研究中因采用标准样本图像建立识别模型而导致推广能力较差的不足。
The fast detection and recognition of pests is the basis of crop pest and disease control in agriculture. Traditionally, experts observe the external features of pests and then compare these features with specimens while identifying pests, which is time-consuming and labor exhaustive. With the development of computer technology, image processing technology and pattern recognition technology were used to the research and identification of pests gradually and the identification system of pests was established, which not only enriched the means of identification but also improved the efficiency of identification.
Typical agricultural pests were studied as research objects in this thesis. Image segmentation, feature extraction, classification, etc. were studied based on pests' images with the technologies of digital image processing, pattern recognition. A remote automatic insect recognition system based on internet of things was established based on these results and technologies through3G wireless network. The main research contents of thesis are as follows:
(1) The image acquisition system was designed. The length of pests in this research have great difference, the same pest always has different attitude and pests have strong activity ability. Therefore, the pest image acquisition systems which meet two kinds of demand were designed. The first system was designed to get image of trapped pests which were stationary. The distance of pests from the system and the scope are fixed. Therefore, the camera uses the mode of CMOS line scan and the focal length of camera lens is fixed. The second system was designed to get pest images in the field real-time. The pests were active, the distance of pests from the system can change and the scope is fixed. Therefore, the camera uses the mode of CCD line scan and the focal length of camera lens is adjusted.
(2) The segmentation technology of pest images based on HSV color model was put forward. According to the characteristics of the sample background and objectives, the Otsu threshold segmentation method based on HSV color model was applied. The RGB model was transformed to HSV model before segmentation and the Otsu algorithm was applied to H component of pest images for getting the threshold adaptively. To get the whole pests, the subsequent processing was done after finishing the segmentation. The applied method overcame the disadvantages that many backgrounds were misclassified as target when using the pest RGB image.
(3) Multi-feature extraction and feature selection technologies of pest images were studied. The visual features including the morphological characteristics, color characteristics and texture feature were extracted and the redundant features were eliminated through the Ant colony optimization algorithm(ACO). With the ACO, the35dimensional features were lower to29dimensions and the recognition accuracy was improved from87.4%to89.5%. The SIFT algorithm which is considered easy to use by all in recent years was applied to extract local features of pests in this study. The local features are invariant to image scaling, translation, rotation, partially invariant to illumination changes and affine or3D projection and independent on the background segmentation which are suitable for the extraction of pest image features obtained in natural light and complex background. The application of the local feature in the identification of pests not only expanded the application field of the local feature but also provided new ideas and new methods for pest feature extraction.
(4) The method of pest pattern recognition was studied. The SVM model was used to identify the pests. The theory of foundations and basic methods of the support vector machine(SVM) were elaborated. The different identification experiments using different pest features were done and the test accuracies were compared in this thesis. The accuracy using morphological characteristics, color features and texture features was over85%. The accuracy using the local features obtained by SIFT algorithm in original RGB images was79%. The test results showed that the ant colony optimization algorithm can eliminate the correlation of image feature and improve the accuracy. At the same time, the test results showed that the local feature extraction method can be applied to obtain the features of pests with no background segmentation.
(5) The pest remote automatic identification system based on internet of things was designed. The thesis took twelve kinds of typical agricultural pests including Cnaphalocrocis medinalis Guenee, Prodenia litura, Chilo suppressalis, Sesamia inferens, Anomala corpulenta Motschulsky, Ostrinia nubilalis, Naranga aenescm, Sogatella furcifera, Agrotis ypsilon Rottemberg, Gryllotalpa orientalis Burmeister, Diaphania perspectalia and Conogethes punctiferalis as the object of study. The system included one host control platform and more remote platforms which formed a distributed identification network through3G wireless network. The identification process can be finished in remote automatically and can also be done in the host control platform after the pest images were compressed and transmitted to the host control platform through3G network. The system has the function of expanding the sample library dynamically by getting the image from the local disks. The system also included the expert identification interface. The expert can identify the pests which have been classified by the system automatically and can compare the identification results through the interface. Because of adopting the pest images obtaining in natural light and random pest attitudes, the recognition model has strong generalization ability which is superior to research existing.
引文
A. Colorni, M. Dorigo, V. Maniezzo. Distributed Optimization by Ant Colonies. Proceedings of the First European Conference on Artificial Life, Paris, France, F.Varela and P.Bourgine (Eds.), Elsevier Publishing,1991:134-142.
A. Rosenfeld, P. D. Torre. Histogram concavity analysis as an aid in threshold selection. IEEE Trans. Syst. Man Cybern,1983, SMC-13:231-235.
A.G.Berlage, T.M.Cooper, J.F. Aristazabal. Machine vision Identification of Diploid and Tetrapleid Ryegrass Seed. Transaction of ASAE,1988,31(1):24-27.
B.S.Manjunath, W.Y.Ma. Texture feature for browsing and retrieval of image data. IEEETransaction on PAMI,1996,18(8):837-842.
Baumberg A. Reliable Feature Matching Across Widely Separated Views. International Conference on Computer Vision,2000:774-781.
Beaudet, P.R. Rotationally invariant image operators. International Joint Conference On Pattern Recognition,1978:579-583.
Brown,M., Loer,D.G. Invariant features from interest point groups. British Machine Vision Conference, BMVC 2002,Cardiff,Wales:British Machine Vision Association,2002.656-665.
C Harris, M Stephens. A combined corner and edge detector.in Alvey Vision Conference, 1988:147-151.
Chenglu, Daniel E.Guyer, Wei Li. Local feature-based identification and classification for orchard insects. Biosyst. Eng., io4 2009,299-307.
Christian Blum. Ant colony optimization:Introduction and recent trends. Physics of Life Reviews 2,2005,353-373.
Cortes C., Vapnik V. Support Vector Networks. Machine Learning,1995,20:273-297.
D Lowe. Distinetive Image Features from Seale-Invariant Keypoints. International Journal of Computer Vision,2004,60(2):91-110.
D Lowe. Local Feature View Clustering for 3D object Reeognition. In IEEE Conference on Computer Vision and Pattern Recognition(CVPR'01),2001,1-7.
D Lowe. Object Recognition from Local Scale Invariant Features. In Seventh International Conference on Computer Vision(ICCV'99),1999,1-8.
D Whiter Jain. Alogorithm and strategies for similarity retrieval. In TRVCL-96-101 University of California, San Diego,1996.
D.E. Guyer, G. E. Miles, M. M. Schreiber et al. Machine Vision and Image Processing for Plant Identification. Transaction of ASAE,1986,29(60):1500-1507.
D.Larlus, J. Verbeek, F. Jurie. Category level object segmentation by combining bag-of-words models with Dirichlet processes and random fields, International Journal of Computer Vision 2010,88(2):238-253.
Whittaker A D, Miles G E, Mitchell O R, et al. Fruit location in a Partially Occluded Image. Transaction of ASAE,1987,30(3):591-596.
Daly HV, Hoelmer K, Nornan P, etc. Computer-Assisted Measurement and Identification of Honey Bees(Hymenoptera:Apidae). Annals of the Entomological Society of America, 1982,75(6):591-594.
Do M.T., Harp J.M., Norris K.C. A test of a pattern recognition system for identification of spiders. Bull. Entomol. Res.,1999,89:217-224.
Eric Nowak, Frederic Jurie, Bill Triggs, et al. Sampling Strategies for Bag-of-Features Image Classification. European Conference on Computer Vision,2006,3954:490-503.
Fedor P., Malenovsky I., Vanhara J., et al. Thrips (Thysanoptera) identification using artificial neural networks. Bull. Entomol. Res.,2008,98:437-447.
Fedor P., Vanhara J., Havel J., et al. Artificial intelligence in pest insect monitoring. Syst. Entomol., 2009,34:398-400.
Gambardella L M, Dorigo M. Solving symmetric and asymmetric TSPs by ant colonies. Proceedings of IEEE International Conference on Evolutionary Computation, IEEE-EC 96, May 20-22,1996, Nagoya, Japan:622-627.
Gassoumi H, Prasad NR, Ellington JJ,et al. A neuro-furry approach for insect classification. World Automation Congress, Third International Symposium on Soft Computing for Industry, Maui, Hawaii,2000.
H Gassoumi, NR Prasad. Neural Network-Based Approach For Insect Classification In Cotton Ecosystems. International Conference on Intel ligent Technologies(InTch2000), Bangkok, Thailand, Deeember13-15,2000.
Hamidreza Rashidy Kanan, Karim Faez. An improved feature selection method based on ant colony optimization (ACO) evaluated on face recognition system. Applied Mathematics and Computation,2008,205:716-725.
Haralick R M, Shapiro L G. Image segmentation techniques. Computer Graphics Image Proeessing,1985,29:100-132.
Huan Hao, Cheng Cai, Yu Meng, et al. Butterfly image retrieval based on SIFT feature analysis. PIAGENG 2009:Image Processing and Photonics for Agricultural Engineering,2009,7489: 748900-1-74890O-8
J Matas, O Chum, U Martin, et.al. Robust wide baseline stereo from maximally stable extremal regions, in British Machine Vision Conference(BMVC'02),2002:384-393.
J. F. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-698.
J. N. Kapur, P. K. Sahoo, A. K. C. Wong. A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision Graphics Image Processing,1985, 29:273-285.
J.C.Niebles, H.Wang, L.Fei-Fei. Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision,2008,79 (3):299-318.
J.M.Chassery, C.Garbay, G. Brugal. An iterative segmentation method based on a contextual color and shape criterion. IEEE PAMI,1984,6(6):794-800.
Jiang J.-A., Tseng C.-L., Lu F.-M., et al. A GSM-based remote wireless automatic monitoring system for field information:A case study for ecological monitoring of the oriental fruit fly, Bactrocera dorsalis (Hendel). Comput. Electron. Agric.,2008,62:243-259.
Jing Li, Nigel M. Allinson. A comprehensive review of current local features for computer vision. Neurocomputing,2008,71:1771-1787.
Jordan, Jay B. Vision guided insect handling system, Modeling and Simulation. Proceedings of the Annual Pittsburgh Conference.1990,21(5):1995-2000.
Kenneth R. Castleman.数字图像处理,北京：电子工业出版社,2002.
Kevin J. Gaston, Mark A. O'Neill. Automated species identification:why not?. Phil. Trans. R. Soc. Lond.,2004, B(359):655-667.
Koenderink J.J. The structure of images. Biological Cybernetics,1984,50:363-396.
L.Wu, S. Hoi, N. Yu. Semantics-preserving bag-of-words models and applications, IEEE Transactions on Image Processing,2010,19(7):1908-1920.
Li F F, Perona P. A Bayesian Hierarchical model for Learning Natural Scene Categories. International Conference on Computer Vision and Pattern Recognition,2005,2(2):524-531.
Lindeberg.T. Scale-space theory:A basic tool for analyzing structures at different scales. Journal of Applied Statistics,1994,21 (2):224-270.
M.H.Gross, R Koch, Li. Lippert, et al. Multiscale image texture analysis in wavelet spaces. In Proc.IEEE. Int. Conf. on Image Proc,1994,412-416.
M.J. Delwiche, S Tang, J.F. Thompson, et al. Prune Defect Detection By Line-Scan Imaging. Transaction of ASAE,1990,33(3):950-954.
M.P. Rigney, G.H. Brusewitz, GA. Kranzler, et al. Asparagus Defect Inspection With Machine Vision. Transaction of ASAE,1992,5(6):1873-1878.
M.T.Hagan, H.B.Demuth, M.Beale.神经网络设计.北京:机械工业出版社,2002.
Mayo M.,Watson A.T. Automatic species identification of live moths. Knowl. Based Syst.,2007, 20,195-202.
Mehdi Hosseinzadeh Aghdam, Nasser Ghasem-Aghaee, Mohammad Ehsan Basiri. Text feature selection using ant colony optimization. Expert Systems with Applications,2009,36: 6843-6853.
Mikolajczyk K, Schmid C. A Performance Evaluation of Local Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630.
Mikolajczyk K, Schmid C. An Affine Invariant Interest Point Detector. European Conference on Computer Vision,2002:128-142.
Mikolajczyk K, Schmid C. Indexing Based on Scale-invariant Interest Points. International Conference on Computer Vision,2001,1:525-531.
N. Larios, H. Deng, W. Zhang, et al. Automated Insect Identification through Concatenated Histograms of Local Appearance Features. IEEE Work. App. Comp.,2007.
N. Otsu. A threshold selection method from gray-level histogram. IEEE Trans. System Man Cybernetics,1979,SMC-9(1):62-66.
N.otsu. A threshold selection method from gray-level histogram. IEEE Transactions on System Man Cybernetics,1979,SMC-9(1):62-66.
Natalia Larios, Hongli Deng, Wei Zhang, et al. Automated insect identification through concatenated histograms of local appearance features:feature vector generation and region detection for deformable objects. Mach. Vision Appl.,2008,19,105-123.
P K Sahoo, S Soltani. Survey of thresholding techniques. Computer Vision, GraPhies,and Image proeessing,1998,41 (2):233-260.
Peter W.Sites,et al.. Computer Vision to Location Fruit on a Tree. Transaction of ASAE, 1988,31(1):257-263.
R C GonZalez, R E Woods.数字图像处理(第二版).北京：电子工业出版社,2006.
R.M.Haralick, K.Shangmugam, I.Dinstein. Texture feature for image classification. IEEE Transaction on SMC.1973,3:610-621.
Rafael C. Gonzalez, Richard E.Woods著.阮秋琦,阮宇智等译.数字图像处理(第二版).北京：电子工业出版社.2007,229-232.
Reed T.R., Buf J.M.H. A review of recent texture segmentation and feature extraction techniques.CVGIP-IU57(3),1993,359-372.
Richard J. Prokop, Anthony P.Reeves. A Survey of Moment-Based techniques for Unoccluded Object Representation and Recognition.CVGIP,1992,54(5):438-460.
Roth V, Pogoda A, Steinhage V, et al. Pattern Recognition Combining Feature and Pixel-based Classification within a Real World Application. DAGM-Symposium,1999,21:120-129.
Russell K.N., Do M.T., Platnick N.I. Introducing SPIDA-Web:An Automated Identification System for Biological Species. In Proceedings of Taxonomic Database Working Group Annual Meeting, St Petersburg, Russia,11-18 September 2005.
Shearer, S. A., Payne, F. A. Color and Defect Sorting of Bell Peppers Using Machine Vision. Transaction of ASAE,1990,33(6):2045-2050.
S. Gunasekaran, T. M. Cooper, A. G. Berlage.Evaluating Quality Factors of Com and Soybeans Using a Computer Vision system. Transaction of ASAE,1988,31(4):1264-1271.
Gunasekaran S, Cooper T M, Berlage A G, et al. Image Processing for Stress Cracks in Cron Kernels. Transaction of ASAE,1988,31:257-263.
S.L.Horowitz, T.Pavlidis. Picture segmentation by a tree traversal algorithm. Journal of the ACM, 1976,23:368-388.
Sena.rj. et al. Fall armyworrn damaged maize Plant identification usnig digiatal images. Biosystems Engineering,2003,85(4):449-540.
Shahla Nemati, Mohammad Ehsan Basiri. Text-independent speaker verification using ant colony optimization-based selected features. Expert Systems with Applications,2011,38:620-630.
Sivic J, Russell B C, Efros A A, et al. Discovering Objects and Their Location in Images. International Conference on Computer Vision,2005, 1(1):872-877.
Sporring, J., Florack, L., Nielsen, M., et al. Gaussian scale-space theory, USA:Kluwer Aeademic Publishers Norwell,1997.
Strieker M., Orengo M. Similarity of color images. In:Proc of SPIE Storage and Retrieval for Image and Video Database,1995,24(20):381-392.
Swain M J, Ballard D H. Color indexing. International Joumal of Computer Vision. 1991,7(1):11-32.
Torralba A, Fergus R, Weiss Y. Small Codes and Large Image Databases for Recognition. International Conference on Computer Vision,2008.
Vanhara J., Murarikova N., Malenovsky I., et al. Artificial Neural Networks for fly identification: A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae). Biol. Bratisl., 2007,62,462-469.
Vapnik V. Statistical Learning Theory, New York:Wiley,1998.
Vapnik V.统计学习理论的本质.北京：清华大学出版社,2000.
Vladimir N. Vapnik. An Overview of Statistical Learning Theory. IEEE Transactions on Neural Networks,1999,10(5).
W. Doyle. Operation useful for similarity-invariant pattern recognition. J. Assoc. Comput, 1962,9:259-267.
W. H. Tsai. Moment-preserving thresholding:a new approach. Computer Vision, Graphics, and Image Processing,1985,29:377-393.
Wang X F, Huang D S, Du J X, et al. Classification of plant leaf images with complicated background. Appl. Mathematics and Computer,2008,205 (2):916-926.
Watson AT, O'Neill MA, Kitching IJ.Automated identification of live moths(macrolepidoptera) using Digital Automated Identification SYstem(DAISY). Syst. Bio,2004,l(3):287-300.
Weeks PJD, O'Neill MA, Gaston KJ, et al. Automating insect identification:exploring the limitations of a prototype system. Appl. Ent,1999,123(1):1-8.
Weeks PJD, O'Neill MA, Gaston KJ, et al. Species-identification of wasps Using principal component assoeiative memories. Image Vis.Comput,1999,17:861-866.
Weeks PJD, O'Neill MA, Gaston KJ, et al. Automating the identification of insects:a new solution to an old problem. Bull.Entomol.Res,1997,87:203-211.
Y. Hu, X.Cheng, L. Chia, et al. Coherent phrase model for efficient image near-duplicate retrieval. IEEE Transactions on Multimedia,2009,11(8):1434-1445.
Y. Koumpouros, B.D. Mahaman, M. Maliappis, et al. Image processing for distance diagnosis in pest management. Computers and Electronics in Agriculture,2004(44):121-131.
Y. Wang, G. Mori. Human action recognition by semilatent topic models. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009:1762-1774.
Yu DS, Kokko EG, BarronJR, et al. Identification of Ichneumonid wasps using image analysis of wings. Syst. Ent,1992,17(4):389-395.
Zayas I., Converse H., Steele J. Discrimination of Whole Worm Broken Com Kernels With Image of Analysis. Transaction of ASAE,1990,33(5):1642-1646.
Zhou YH, Ling LB, Rohlf FJ. Automatic Description of the Venation of Mosquito Wings from Digitized Images. Syst. Zool,1985,34(3):346-358.
边肇棋,张学工.模式识别(第二版).北京：清华大学出版社,2000.
常甜甜.支持向量机学习算法若干问题的研究.[博十学位论文]西安电了科技大学,2010.
陈纯.计算机图像处理技术与算法.北京：清华大学出版社.2003.
陈建能,张国凤.计算机视觉技术在农业中的应用及展望.甘肃农业大学学报,2003,38(2)：248-253.
陈婷婷.采用模糊形态学的大田害虫图像分割.内蒙古农业科技,2007(6)：45-47.
程小梅.基于图像的昆虫识别研究与设计.[硕士学位论文]西北大学,2008.
杜瑞卿,褚学英,王庆林,赵秋红,庞发虎.粗糙集神经网络在昆虫总科阶元分类学上的应用.中国农业大学学报,2007,12(1)：33-38.
范昕炜.支持向量机算法的研究及其应用.[博士学位论文]浙江大学,2003.
范艳峰,甄彤.基于图像分析的谷物害虫检测与分类识别技术研究.小型微型计算机系统,2005,26(10)：1828-1832.
冯建辉,杨玉静.基于灰度共生矩阵提取纹理特征图像的研究.北京测绘,2007(3)：19-22.
高健,黄心汉,王敏等.基于彩色的SIFT特征点提取与匹配.计算机工程与应用,2007,43(34)：10-15.
高隽.人工神经网络原理及仿真实例,北京：机械工业出版社,2003.
高灵旺,沈佐锐,刘志琦,马晓光.基于二义分类推理的昆虫分类辅助鉴定多媒体专家系统通用平台TaxoKeys的设计与开发.昆虫分类学报,2003,46(5)：644-648.
管继刚.物联网技术在智能农业中应用.通信管理与技术,2010,3：24-27.
郭勇豪.苹果图像特征提取与分类算法的研究与应用.[硕士学位论文]重庆大学,2010.
韩思奇,王蕾.图像分割的阈值法综述.系统工程与电子技术,2002,24(6)：91-94.
何春华,胡迎春.基于改进遗传算法的自动阈值图像分割方法.计算机仿真,2011,28(2)：312-315.
何东健等.数字图像处理.西安电子科技大学出版社,西安,2003.
何勇,刘飞,聂鹏程.数字农业与农业物联网.现代农机,2012,1：8-10.
胡广寰.基于内容图像检索中图像语义分类技术研究.[博士学位论文]浙江大学,2005.
胡洁.高维数据特征降维研究综述.计算机应用研究,2008,25(9)：2601-2606.
胡奇,马吉祥.用计算机进行昆虫分类检索研究初探.昆虫知识,1990,27(1)：40-44.
黄小燕,郭勇,赵太飞.数学形态学的储粮害虫彩色数字图像分割.计算机测量与控制,2003,11(6)：467-469.
李朝晖,陈明.基于小波和自学习神经网络的图像分割.计算机应用研究,2006,1：246-249.
李东明.基于机器视觉的田间杂草识别方法研究.[硕士学位论文]河北农业大学,2008.
李力,靳蕃.基于图的频繁闭项集挖掘算法.西南交通大学报,2004,39(3)：385-389.
李珊珊.基于图像识别的大田害虫多目标识别研究.[硕士学位论文]华北水利水电学院,2007.
廉飞宇,张元.基于小波变换压缩和支持向量机组的储粮害虫图像识别.河南工业大学学报(自然科学版),2006,27(1)：21-24.
刘东菊.基于阈值的图像分割算法的研究.[硕士学位论文]北京交通大学,2009.
刘小军,杨杰等.基于SIFT的图像匹配方法.红外与激光工程,2008,37(7)：156-160.
刘新宇,张红涛.模糊识别技术在大田害虫检测系统中的应用.农机化研究,2006,(12)：192-194.
刘忠伟,章毓晋.综合利用颜色和纹理特征的图像检索.通信学报,1999,20(5)：36-40.
罗希平,田捷等.图像分割方法综述.模式识别与人工智能,1999,12(3)：300-312.
毛韶阳,林肯立.优化k-means初始聚类中心研究.计算机工程与应用,2007,43(22)：179-181.
孟章荣.各种颜色模型选用需求分析.中国图像图形学报,1996,1(3)：238-242.
闵克学.蚁群-粒子群优化算法混合求解TSP问题.[硕士学位论文]吉林大学,2005.
牛少平.计算机视频监控技术研究.[硕士学位论文]西北工业大学电子信息学院,2006.
欧萍,贺电.遗传算法粒在二维最大熵值图像分割中的应用.计算机仿真,2011,28(1)：29-298.
邱道尹,王要沛,张孝远,王志迁,顾波.农田害虫的图像预处理研究.华北水利水电学院学报,2007,28(6)：3941.
邱道尹,张红涛,陈铁军等.模糊识别技术在储粮害虫检测中的应用.农业系统科学与综合研究,2002,18(2)：122-125.
邱道尹,张红涛,刘新宇等.基于机器视觉的大田害虫检测系统.农业机械学报,2007,38(1)：120-122.
沈佐锐,于新文.昆虫数学形态学研究及其应用展望.昆虫学报,1998,41(增刊)：140-148.
沈佐锐.全国第五届昆虫区系分类与多样性学术研讨会,大会特邀报告.北京,2005,8月15-20日.
石军.“感知中国”促进中国物联网加速发展.通信管理与技术,2009,10(5)：1-3.
史忠植.知识发现.北京：清华大学出版社,2002.
苏杰,王丙勤,郭立.数字图像的纹理特征提取与分类研究.电子测量技术,2008,31(5)：52-55.
孙冠英,陈学新,程家安.LUCD多途径的分类检索和诊断专家系统.动物分类学报,2002,27(4)：871-874.
谭菊,张友钟.基于灰度共生矩阵的纹理特征景物识别.重庆文理学院学报(自然科学版) 2010,29(1)：66-68.
王爱民,沈兰荪.图像分割研究综述.测控技术,2000,(5)：1-6.
王东.枣虫害图像自动识别关键技术研究.[硕士学位论文]河北农业大学,2011.
王培珍.基于二维阈值化与FCM相混合的图像迅速分割方法.中国图像图形学报,1998：3(9)：735-738.
工水璋,冀小平,闫文娟.基于小波变换的纹理特征提取.科技情报开发与经济,2008,18(11)：149-150.
王志迁,王要沛.基于径向基函数神经网络农田害虫分类器设计研究.科技信息,2008(9)：8-9.
吴今培.基于核函数的主成份分析及应用.系统工程,2005,23(2)：17-20.
夏定元.基于内容的图像检索通用技术研究及应用.[博士学位论文]华中科技大学,2004.
萧嵘,王继成,张福炎.支持向量机理论综述.计算机科学,2000,26(3)：1-3.
邢志卿,付兴,房骏等.物联网技术在现代农业生产中的应用研究.农业技术与装备,2010,4(188)：16-17.
徐晓国,莫建初,程家安.基于web的等翅目昆虫分类系统的设计和开发.昆虫分类学报,2004,26(2)：86-90.
杨红珍,张建伟,李湘涛,沈佐锐.基于图像的昆虫远程自动识别系统的研究.农业工程学报,2008,24(1)：188-192.
杨晖,曲秀杰.图像分割方法综述.电脑开发与应用,2005,18(3)：21-23.
杨玲玲.水稻飞虱自动识别技术的研究.[硕士学位论文]南京农业大学,2008.
杨金龙.图像分割算法研究与实现.[硕士学位论文]西北师范大学,2009.
杨群.基于直方图和小波变换的图像分割方法的研究.[硕士学位论文]南昌大学计算机学院,2006.
杨圣云,袁德辉,赖国明.一种新的聚类初始化方法.计算机应用与软件,2007,8(24)：51-52.
叶志伟,郑肇葆,万幼川,虞欣.基于蚁群优化的特征选择新方法.武汉大学学报(信息科学版),2007,32(12)：1127-1130.
应义斌,傅宾忠,蒋亦元等.机器视觉技术在农业生产自动化中的应用.农业工程学报,1999,15(3)：199-203.
应义斌,饶秀勤.机器视觉技术在农产品品质自动识别中的应用.农业工程学报,2000,16(1)：4-17.
应义斌,章文英,蒋亦元等.机器视觉技术在农产品收获和加工自动化中的应用.农业机械学报,2000,31(3)：112-115.
于新文,沈佐锐.昆虫数字图像的分割技术研究.农业工程学报,2001,17(3)：137-141.
于新文,沈佐锐等.昆虫图像几何形状特征的提取技术研究.中国农业大学学报,2003,8(3)：47-50.
张大奇,曲仕茹,石爽.基于序列图像运动分割的车辆边界轮廓提取算法.交通运输学报, 2009,9(3)：117-121.
张法全,常永英,崔光照.基于小波变换的农田害虫图像预处理研究.郑州轻工业学院学报(自然科学版),2005,20(1)：13-16.
张红梅.基于支持向量机的仓储物害虫分类识别研究.计算机工程与应用,2005,9：216-218.
张红涛,毛罕平,邱道尹.储粮害虫图像识别中的特征提取.农业工程学报,2009,25(2)：126-130.
张红涛,毛罕平,邱道尹.储粮害虫图像识别中的特征提取.农业工程学报,2009,25(2)：126-130.
张杰慧,何中市,王健,黄学全.基于自适应蚁群算法的组合式特征选择算法.系统仿真学报,2009,21(6)：1605-1609.
张娟,黄心渊.基于图像分析的梅花品种识别研究.北京林业大学学报,2012,34(1)：96：104.
张学工译.统计学习理论的本质.北京：清华大学出版社,2000.
张学工.关于统计学习理论与支持向量机.自动化学报,2000,26(1)：32-42.
张艳芳,李晋宏,曹丹阳,魏金强.基于CF-树的k-means聚类算法的改进.软件导刊,2005,15(5)：42-45.
章毓敏.图像分割(第一版).北京：科学出版社,2001.
赵汗青,沈佐锐,于新文.数学形态学应用于昆虫自动鉴别的研究.中国农业大学学报,2002,7(3)：28-42.
赵汗青,沈佐锐,于新文.数学形态学在昆虫分类学上的应用研究Ⅱ.在总科阶元上的应用研究.昆虫学报,2003a,46(2)：201-208.
赵汗青,沈佐锐,于新文.数学形态学在昆虫分类学上的应用研究Ⅰ.在目级阶元上的应用研究.昆虫学报,2003b,46(1)：45-50.
赵汗青,沈佐锐,于新文.数学形态学在昆虫分类学上的应用研究Ⅲ.在科阶元上的应用研究.昆虫学报,2003c,46(1)：339-344.
A. Rosenfeld, P. D. Torre. Histogram concavity analysis as an aid in threshold selection. IEEE Trans. Syst. Man Cybern,1983, SMC-13:231-235.
A.G.Berlage, T.M.Cooper, J.F. Aristazabal. Machine vision Identification of Diploid and Tetrapleid Ryegrass Seed. Transaction of ASAE,1988,31(1):24-27.
B.S.Manjunath, W.Y.Ma. Texture feature for browsing and retrieval of image data. IEEETransaction on PAMI,1996,18(8):837-842.
Baumberg A. Reliable Feature Matching Across Widely Separated Views. International Conference on Computer Vision,2000:774-781.
Beaudet, P.R. Rotationally invariant image operators. International Joint Conference On Pattern Recognition,1978:579-583.
Brown,M., Loer,D.G. Invariant features from interest point groups. British Machine Vision Conference, BMVC 2002,Cardiff,Wales:British Machine Vision Association,2002.656-665.
C Harris, M Stephens. A combined corner and edge detector.in Alvey Vision Conference, 1988:147-151.
Chenglu, Daniel E.Guyer, Wei Li. Local feature-based identification and classification for orchard insects. Biosyst. Eng., io4 2009,299-307.
Christian Blum. Ant colony optimization:Introduction and recent trends. Physics of Life Reviews 2,2005,353-373.
Cortes C., Vapnik V. Support Vector Networks. Machine Learning,1995,20:273-297.
D Lowe. Distinetive Image Features from Seale-Invariant Keypoints. International Journal of Computer Vision,2004,60(2):91-110.
D Lowe. Local Feature View Clustering for 3D object Reeognition. In IEEE Conference on Computer Vision and Pattern Recognition(CVPR'01),2001,1-7.
D Lowe. Object Recognition from Local Scale Invariant Features. In Seventh International Conference on Computer Vision(ICCV'99),1999,1-8.
D Whiter Jain. Alogorithm and strategies for similarity retrieval. In TRVCL-96-101 University of California, San Diego,1996.
D.E. Guyer, G. E. Miles, M. M. Schreiber et al. Machine Vision and Image Processing for Plant Identification. Transaction of ASAE,1986,29(60):1500-1507.
D.Larlus, J. Verbeek, F. Jurie. Category level object segmentation by combining bag-of-words models with Dirichlet processes and random fields, International Journal of Computer Vision 2010,88(2):238-253.
Whittaker A D, Miles G E, Mitchell O R, et al. Fruit location in a Partially Occluded Image. Transaction of ASAE,1987,30(3):591-596.
Daly HV, Hoelmer K, Nornan P, etc. Computer-Assisted Measurement and Identification of Honey Bees(Hymenoptera:Apidae). Annals of the Entomological Society of America, 1982,75(6):591-594.
Do M.T., Harp J.M., Norris K.C. A test of a pattern recognition system for identification of spiders. Bull. Entomol. Res.,1999,89:217-224.
Eric Nowak, Frederic Jurie, Bill Triggs, et al. Sampling Strategies for Bag-of-Features Image Classification. European Conference on Computer Vision,2006,3954:490-503.
Fedor P., Malenovsky I., Vanhara J., et al. Thrips (Thysanoptera) identification using artificial neural networks. Bull. Entomol. Res.,2008,98:437-447.
Fedor P., Vanhara J., Havel J., et al. Artificial intelligence in pest insect monitoring. Syst. Entomol., 2009,34:398-400.
Gambardella L M, Dorigo M. Solving symmetric and asymmetric TSPs by ant colonies. Proceedings of IEEE International Conference on Evolutionary Computation, IEEE-EC 96, May 20-22,1996, Nagoya, Japan:622-627.
Gassoumi H, Prasad NR, Ellington JJ,et al. A neuro-furry approach for insect classification. World Automation Congress, Third International Symposium on Soft Computing for Industry, Maui, Hawaii,2000.
H Gassoumi, NR Prasad. Neural Network-Based Approach For Insect Classification In Cotton Ecosystems. International Conference on Intel ligent Technologies(InTch2000), Bangkok, Thailand, Deeember13-15,2000.
Hamidreza Rashidy Kanan, Karim Faez. An improved feature selection method based on ant colony optimization (ACO) evaluated on face recognition system. Applied Mathematics and Computation,2008,205:716-725.
Haralick R M, Shapiro L G. Image segmentation techniques. Computer Graphics Image Proeessing,1985,29:100-132.
Huan Hao, Cheng Cai, Yu Meng, et al. Butterfly image retrieval based on SIFT feature analysis. PIAGENG 2009:Image Processing and Photonics for Agricultural Engineering,2009,7489: 748900-1-74890O-8
J Matas, O Chum, U Martin, et.al. Robust wide baseline stereo from maximally stable extremal regions, in British Machine Vision Conference(BMVC'02),2002:384-393.
J. F. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-698.
J. N. Kapur, P. K. Sahoo, A. K. C. Wong. A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision Graphics Image Processing,1985, 29:273-285.
J.C.Niebles, H.Wang, L.Fei-Fei. Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision,2008,79 (3):299-318.
J.M.Chassery, C.Garbay, G. Brugal. An iterative segmentation method based on a contextual color and shape criterion. IEEE PAMI,1984,6(6):794-800.
Jiang J.-A., Tseng C.-L., Lu F.-M., et al. A GSM-based remote wireless automatic monitoring system for field information:A case study for ecological monitoring of the oriental fruit fly, Bactrocera dorsalis (Hendel). Comput. Electron. Agric.,2008,62:243-259.
Jing Li, Nigel M. Allinson. A comprehensive review of current local features for computer vision. Neurocomputing,2008,71:1771-1787.
Jordan, Jay B. Vision guided insect handling system, Modeling and Simulation. Proceedings of the Annual Pittsburgh Conference.1990,21(5):1995-2000.
Kenneth R. Castleman.数字图像处理,北京：电子工业出版社,2002.
Kevin J. Gaston, Mark A. O'Neill. Automated species identification:why not?. Phil. Trans. R. Soc. Lond.,2004, B(359):655-667.
Koenderink J.J. The structure of images. Biological Cybernetics,1984,50:363-396.
L.Wu, S. Hoi, N. Yu. Semantics-preserving bag-of-words models and applications, IEEE Transactions on Image Processing,2010,19(7):1908-1920.
Li F F, Perona P. A Bayesian Hierarchical model for Learning Natural Scene Categories. International Conference on Computer Vision and Pattern Recognition,2005,2(2):524-531.
Lindeberg.T. Scale-space theory:A basic tool for analyzing structures at different scales. Journal of Applied Statistics,1994,21 (2):224-270.
M.H.Gross, R Koch, Li. Lippert, et al. Multiscale image texture analysis in wavelet spaces. In Proc.IEEE. Int. Conf. on Image Proc,1994,412-416.
M.J. Delwiche, S Tang, J.F. Thompson, et al. Prune Defect Detection By Line-Scan Imaging. Transaction of ASAE,1990,33(3):950-954.
M.P. Rigney, G.H. Brusewitz, GA. Kranzler, et al. Asparagus Defect Inspection With Machine Vision. Transaction of ASAE,1992,5(6):1873-1878.
M.T.Hagan, H.B.Demuth, M.Beale.神经网络设计.北京:机械工业出版社,2002.
Mayo M.,Watson A.T. Automatic species identification of live moths. Knowl. Based Syst.,2007, 20,195-202.
Mehdi Hosseinzadeh Aghdam, Nasser Ghasem-Aghaee, Mohammad Ehsan Basiri. Text feature selection using ant colony optimization. Expert Systems with Applications,2009,36: 6843-6853.
Mikolajczyk K, Schmid C. A Performance Evaluation of Local Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630.
Mikolajczyk K, Schmid C. An Affine Invariant Interest Point Detector. European Conference on Computer Vision,2002:128-142.
Mikolajczyk K, Schmid C. Indexing Based on Scale-invariant Interest Points. International Conference on Computer Vision,2001,1:525-531.
N. Larios, H. Deng, W. Zhang, et al. Automated Insect Identification through Concatenated Histograms of Local Appearance Features. IEEE Work. App. Comp.,2007.
N. Otsu. A threshold selection method from gray-level histogram. IEEE Trans. System Man Cybernetics,1979,SMC-9(1):62-66.
N.otsu. A threshold selection method from gray-level histogram. IEEE Transactions on System Man Cybernetics,1979,SMC-9(1):62-66.
Natalia Larios, Hongli Deng, Wei Zhang, et al. Automated insect identification through concatenated histograms of local appearance features:feature vector generation and region detection for deformable objects. Mach. Vision Appl.,2008,19,105-123.
P K Sahoo, S Soltani. Survey of thresholding techniques. Computer Vision, GraPhies,and Image proeessing,1998,41 (2):233-260.
Peter W.Sites,et al.. Computer Vision to Location Fruit on a Tree. Transaction of ASAE, 1988,31(1):257-263.
R C GonZalez, R E Woods.数字图像处理(第二版).北京：电子工业出版社,2006.
R.M.Haralick, K.Shangmugam, I.Dinstein. Texture feature for image classification. IEEE Transaction on SMC.1973,3:610-621.
Rafael C. Gonzalez, Richard E.Woods著.阮秋琦,阮宇智等译.数字图像处理(第二版).北京：电子工业出版社.2007,229-232.
Reed T.R., Buf J.M.H. A review of recent texture segmentation and feature extraction techniques.CVGIP-IU57(3),1993,359-372.
Richard J. Prokop, Anthony P.Reeves. A Survey of Moment-Based techniques for Unoccluded Object Representation and Recognition.CVGIP,1992,54(5):438-460.
Roth V, Pogoda A, Steinhage V, et al. Pattern Recognition Combining Feature and Pixel-based Classification within a Real World Application. DAGM-Symposium,1999,21:120-129.
Russell K.N., Do M.T., Platnick N.I. Introducing SPIDA-Web:An Automated Identification System for Biological Species. In Proceedings of Taxonomic Database Working Group Annual Meeting, St Petersburg, Russia,11-18 September 2005.
Shearer, S. A., Payne, F. A. Color and Defect Sorting of Bell Peppers Using Machine Vision. Transaction of ASAE,1990,33(6):2045-2050.
S. Gunasekaran, T. M. Cooper, A. G. Berlage.Evaluating Quality Factors of Com and Soybeans Using a Computer Vision system. Transaction of ASAE,1988,31(4):1264-1271.
Gunasekaran S, Cooper T M, Berlage A G, et al. Image Processing for Stress Cracks in Cron Kernels. Transaction of ASAE,1988,31:257-263.
S.L.Horowitz, T.Pavlidis. Picture segmentation by a tree traversal algorithm. Journal of the ACM, 1976,23:368-388.
Sena.rj. et al. Fall armyworrn damaged maize Plant identification usnig digiatal images. Biosystems Engineering,2003,85(4):449-540.
Shahla Nemati, Mohammad Ehsan Basiri. Text-independent speaker verification using ant colony optimization-based selected features. Expert Systems with Applications,2011,38:620-630.
Sivic J, Russell B C, Efros A A, et al. Discovering Objects and Their Location in Images. International Conference on Computer Vision,2005, 1(1):872-877.
Sporring, J., Florack, L., Nielsen, M., et al. Gaussian scale-space theory, USA:Kluwer Aeademic Publishers Norwell,1997.
Strieker M., Orengo M. Similarity of color images. In:Proc of SPIE Storage and Retrieval for Image and Video Database,1995,24(20):381-392.
Swain M J, Ballard D H. Color indexing. International Joumal of Computer Vision. 1991,7(1):11-32.
Torralba A, Fergus R, Weiss Y. Small Codes and Large Image Databases for Recognition. International Conference on Computer Vision,2008.
Vanhara J., Murarikova N., Malenovsky I., et al. Artificial Neural Networks for fly identification: A case study from the genera Tachina and Ectophasia (Diptera, Tachinidae). Biol. Bratisl., 2007,62,462-469.
Vapnik V. Statistical Learning Theory, New York:Wiley,1998.
Vapnik V.统计学习理论的本质.北京：清华大学出版社,2000.
Vladimir N. Vapnik. An Overview of Statistical Learning Theory. IEEE Transactions on Neural Networks,1999,10(5).
W. Doyle. Operation useful for similarity-invariant pattern recognition. J. Assoc. Comput, 1962,9:259-267.
W. H. Tsai. Moment-preserving thresholding:a new approach. Computer Vision, Graphics, and Image Processing,1985,29:377-393.
Wang X F, Huang D S, Du J X, et al. Classification of plant leaf images with complicated background. Appl. Mathematics and Computer,2008,205 (2):916-926.
Watson AT, O'Neill MA, Kitching IJ.Automated identification of live moths(macrolepidoptera) using Digital Automated Identification SYstem(DAISY). Syst. Bio,2004,l(3):287-300.
Weeks PJD, O'Neill MA, Gaston KJ, et al. Automating insect identification:exploring the limitations of a prototype system. Appl. Ent,1999,123(1):1-8.
Weeks PJD, O'Neill MA, Gaston KJ, et al. Species-identification of wasps Using principal component assoeiative memories. Image Vis.Comput,1999,17:861-866.
Weeks PJD, O'Neill MA, Gaston KJ, et al. Automating the identification of insects:a new solution to an old problem. Bull.Entomol.Res,1997,87:203-211.
Y. Hu, X.Cheng, L. Chia, et al. Coherent phrase model for efficient image near-duplicate retrieval. IEEE Transactions on Multimedia,2009,11(8):1434-1445.
Y. Koumpouros, B.D. Mahaman, M. Maliappis, et al. Image processing for distance diagnosis in pest management. Computers and Electronics in Agriculture,2004(44):121-131.
Y. Wang, G. Mori. Human action recognition by semilatent topic models. IEEE Transactions on Pattern Analysis and Machine Intelligence,2009:1762-1774.
Yu DS, Kokko EG, BarronJR, et al. Identification of Ichneumonid wasps using image analysis of wings. Syst. Ent,1992,17(4):389-395.
Zayas I., Converse H., Steele J. Discrimination of Whole Worm Broken Com Kernels With Image of Analysis. Transaction of ASAE,1990,33(5):1642-1646.
Zhou YH, Ling LB, Rohlf FJ. Automatic Description of the Venation of Mosquito Wings from Digitized Images. Syst. Zool,1985,34(3):346-358.
边肇棋,张学工.模式识别(第二版).北京：清华大学出版社,2000.
常甜甜.支持向量机学习算法若干问题的研究.[博十学位论文]西安电了科技大学,2010.
陈纯.计算机图像处理技术与算法.北京：清华大学出版社.2003.
陈建能,张国凤.计算机视觉技术在农业中的应用及展望.甘肃农业大学学报,2003,38(2)：248-253.
陈婷婷.采用模糊形态学的大田害虫图像分割.内蒙古农业科技,2007(6)：45-47.
程小梅.基于图像的昆虫识别研究与设计.[硕士学位论文]西北大学,2008.
杜瑞卿,褚学英,王庆林,赵秋红,庞发虎.粗糙集神经网络在昆虫总科阶元分类学上的应用.中国农业大学学报,2007,12(1)：33-38.
范昕炜.支持向量机算法的研究及其应用.[博士学位论文]浙江大学,2003.
范艳峰,甄彤.基于图像分析的谷物害虫检测与分类识别技术研究.小型微型计算机系统,2005,26(10)：1828-1832.
冯建辉,杨玉静.基于灰度共生矩阵提取纹理特征图像的研究.北京测绘,2007(3)：19-22.
高健,黄心汉,王敏等.基于彩色的SIFT特征点提取与匹配.计算机工程与应用,2007,43(34)：10-15.
高隽.人工神经网络原理及仿真实例,北京：机械工业出版社,2003.
高灵旺,沈佐锐,刘志琦,马晓光.基于二义分类推理的昆虫分类辅助鉴定多媒体专家系统通用平台TaxoKeys的设计与开发.昆虫分类学报,2003,46(5)：644-648.
管继刚.物联网技术在智能农业中应用.通信管理与技术,2010,3：24-27.
郭勇豪.苹果图像特征提取与分类算法的研究与应用.[硕士学位论文]重庆大学,2010.
韩思奇,王蕾.图像分割的阈值法综述.系统工程与电子技术,2002,24(6)：91-94.
何春华,胡迎春.基于改进遗传算法的自动阈值图像分割方法.计算机仿真,2011,28(2)：312-315.
何东健等.数字图像处理.西安电子科技大学出版社,西安,2003.
何勇,刘飞,聂鹏程.数字农业与农业物联网.现代农机,2012,1：8-10.
胡广寰.基于内容图像检索中图像语义分类技术研究.[博士学位论文]浙江大学,2005.
胡洁.高维数据特征降维研究综述.计算机应用研究,2008,25(9)：2601-2606.
胡奇,马吉祥.用计算机进行昆虫分类检索研究初探.昆虫知识,1990,27(1)：40-44.
黄小燕,郭勇,赵太飞.数学形态学的储粮害虫彩色数字图像分割.计算机测量与控制,2003,11(6)：467-469.
李朝晖,陈明.基于小波和自学习神经网络的图像分割.计算机应用研究,2006,1：246-249.
李东明.基于机器视觉的田间杂草识别方法研究.[硕士学位论文]河北农业大学,2008.
李力,靳蕃.基于图的频繁闭项集挖掘算法.西南交通大学报,2004,39(3)：385-389.
李珊珊.基于图像识别的大田害虫多目标识别研究.[硕士学位论文]华北水利水电学院,2007.
廉飞宇,张元.基于小波变换压缩和支持向量机组的储粮害虫图像识别.河南工业大学学报(自然科学版),2006,27(1)：21-24.
刘东菊.基于阈值的图像分割算法的研究.[硕士学位论文]北京交通大学,2009.
刘小军,杨杰等.基于SIFT的图像匹配方法.红外与激光工程,2008,37(7)：156-160.
刘新宇,张红涛.模糊识别技术在大田害虫检测系统中的应用.农机化研究,2006,(12)：192-194.
刘忠伟,章毓晋.综合利用颜色和纹理特征的图像检索.通信学报,1999,20(5)：36-40.
罗希平,田捷等.图像分割方法综述.模式识别与人工智能,1999,12(3)：300-312.
毛韶阳,林肯立.优化k-means初始聚类中心研究.计算机工程与应用,2007,43(22)：179-181.
孟章荣.各种颜色模型选用需求分析.中国图像图形学报,1996,1(3)：238-242.
闵克学.蚁群-粒子群优化算法混合求解TSP问题.[硕士学位论文]吉林大学,2005.
牛少平.计算机视频监控技术研究.[硕士学位论文]西北工业大学电子信息学院,2006.
欧萍,贺电.遗传算法粒在二维最大熵值图像分割中的应用.计算机仿真,2011,28(1)：29-298.
邱道尹,王要沛,张孝远,王志迁,顾波.农田害虫的图像预处理研究.华北水利水电学院学报,2007,28(6)：3941.
邱道尹,张红涛,陈铁军等.模糊识别技术在储粮害虫检测中的应用.农业系统科学与综合研究,2002,18(2)：122-125.
邱道尹,张红涛,刘新宇等.基于机器视觉的大田害虫检测系统.农业机械学报,2007,38(1)：120-122.
沈佐锐,于新文.昆虫数学形态学研究及其应用展望.昆虫学报,1998,41(增刊)：140-148.
沈佐锐.全国第五届昆虫区系分类与多样性学术研讨会,大会特邀报告.北京,2005,8月15-20日.
石军.“感知中国”促进中国物联网加速发展.通信管理与技术,2009,10(5)：1-3.
史忠植.知识发现.北京：清华大学出版社,2002.
苏杰,王丙勤,郭立.数字图像的纹理特征提取与分类研究.电子测量技术,2008,31(5)：52-55.
孙冠英,陈学新,程家安.LUCD多途径的分类检索和诊断专家系统.动物分类学报,2002,27(4)：871-874.
谭菊,张友钟.基于灰度共生矩阵的纹理特征景物识别.重庆文理学院学报(自然科学版) 2010,29(1)：66-68.
王爱民,沈兰荪.图像分割研究综述.测控技术,2000,(5)：1-6.
王东.枣虫害图像自动识别关键技术研究.[硕士学位论文]河北农业大学,2011.
王培珍.基于二维阈值化与FCM相混合的图像迅速分割方法.中国图像图形学报,1998：3(9)：735-738.
工水璋,冀小平,闫文娟.基于小波变换的纹理特征提取.科技情报开发与经济,2008,18(11)：149-150.
王志迁,王要沛.基于径向基函数神经网络农田害虫分类器设计研究.科技信息,2008(9)：8-9.
吴今培.基于核函数的主成份分析及应用.系统工程,2005,23(2)：17-20.
夏定元.基于内容的图像检索通用技术研究及应用.[博士学位论文]华中科技大学,2004.
萧嵘,王继成,张福炎.支持向量机理论综述.计算机科学,2000,26(3)：1-3.
邢志卿,付兴,房骏等.物联网技术在现代农业生产中的应用研究.农业技术与装备,2010,4(188)：16-17.
徐晓国,莫建初,程家安.基于web的等翅目昆虫分类系统的设计和开发.昆虫分类学报,2004,26(2)：86-90.
杨红珍,张建伟,李湘涛,沈佐锐.基于图像的昆虫远程自动识别系统的研究.农业工程学报,2008,24(1)：188-192.
杨晖,曲秀杰.图像分割方法综述.电脑开发与应用,2005,18(3)：21-23.
杨玲玲.水稻飞虱自动识别技术的研究.[硕士学位论文]南京农业大学,2008.
杨金龙.图像分割算法研究与实现.[硕士学位论文]西北师范大学,2009.
杨群.基于直方图和小波变换的图像分割方法的研究.[硕士学位论文]南昌大学计算机学院,2006.
杨圣云,袁德辉,赖国明.一种新的聚类初始化方法.计算机应用与软件,2007,8(24)：51-52.
叶志伟,郑肇葆,万幼川,虞欣.基于蚁群优化的特征选择新方法.武汉大学学报(信息科学版),2007,32(12)：1127-1130.
应义斌,傅宾忠,蒋亦元等.机器视觉技术在农业生产自动化中的应用.农业工程学报,1999,15(3)：199-203.
应义斌,饶秀勤.机器视觉技术在农产品品质自动识别中的应用.农业工程学报,2000,16(1)：4-17.
应义斌,章文英,蒋亦元等.机器视觉技术在农产品收获和加工自动化中的应用.农业机械学报,2000,31(3)：112-115.
于新文,沈佐锐.昆虫数字图像的分割技术研究.农业工程学报,2001,17(3)：137-141.
于新文,沈佐锐等.昆虫图像几何形状特征的提取技术研究.中国农业大学学报,2003,8(3)：47-50.
张大奇,曲仕茹,石爽.基于序列图像运动分割的车辆边界轮廓提取算法.交通运输学报, 2009,9(3)：117-121.
张法全,常永英,崔光照.基于小波变换的农田害虫图像预处理研究.郑州轻工业学院学报(自然科学版),2005,20(1)：13-16.
张红梅.基于支持向量机的仓储物害虫分类识别研究.计算机工程与应用,2005,9：216-218.
张红涛,毛罕平,邱道尹.储粮害虫图像识别中的特征提取.农业工程学报,2009,25(2)：126-130.
张红涛,毛罕平,邱道尹.储粮害虫图像识别中的特征提取.农业工程学报,2009,25(2)：126-130.
张杰慧,何中市,王健,黄学全.基于自适应蚁群算法的组合式特征选择算法.系统仿真学报,2009,21(6)：1605-1609.
张娟,黄心渊.基于图像分析的梅花品种识别研究.北京林业大学学报,2012,34(1)：96：104.
张学工译.统计学习理论的本质.北京：清华大学出版社,2000.
张学工.关于统计学习理论与支持向量机.自动化学报,2000,26(1)：32-42.
张艳芳,李晋宏,曹丹阳,魏金强.基于CF-树的k-means聚类算法的改进.软件导刊,2005,15(5)：42-45.
章毓敏.图像分割(第一版).北京：科学出版社,2001.
赵汗青,沈佐锐,于新文.数学形态学应用于昆虫自动鉴别的研究.中国农业大学学报,2002,7(3)：28-42.
赵汗青,沈佐锐,于新文.数学形态学在昆虫分类学上的应用研究Ⅱ.在总科阶元上的应用研究.昆虫学报,2003a,46(2)：201-208.
赵汗青,沈佐锐,于新文.数学形态学在昆虫分类学上的应用研究Ⅰ.在目级阶元上的应用研究.昆虫学报,2003b,46(1)：45-50.
赵汗青,沈佐锐,于新文.数学形态学在昆虫分类学上的应用研究Ⅲ.在科阶元上的应用研究.昆虫学报,2003c,46(1)：339-344.