基于机器视觉的杂草对准喷药控制系统研究
|
摘要
化学除草剂的使用方式普遍为粗放式的大面积喷洒,一方面容易造成人身伤害,另一方面因为喷药时不是精确对准目标物,往往也喷在了无目标的土壤上。这种喷药方式不仅浪费了农药,还造成了生态环境危害,增加了作物的农药残留量,随着农药的大量使用,其残留在农作物和土壤中的农药对人身和环境的危害也与日俱增,这跟现代农业要求精准高效、绿色环保的要求是背道而驰的。为了减少成本和保护环境,人们开展了除草剂的精确喷药技术的研究,希望替代传统的大面积喷药技术,以降低人的劳动强度,提高药效和保护农田环境。
杂草在田间的分布是随机的,不均匀的,而且具有簇生性。正是这种杂草分布的不连续性,为精确施药技术的可行性提供了依据。因为条播作物的行距基本恒定,条播作物都成行排列,从而有利于基于机器视觉的杂草识别,在总结国内外相关研究的基础上,本文以条播作物-小麦为主要研究对象,设计和开发了基于机器视觉的杂草对准喷药控制系统。本文的主要研究内容如下:
1.研究了基于颜色特征的图像背景分割,对十三个分割因子就分割质量和分割所需的时间进行了对比研究。通过研究发现过绿的2G-R-B是最佳的分割因子。
2.自动取阈值法分割图像能够大大提高图像处理的实时性,本文设计了最大类间方差和最小错误率相结合的自动取阈值算法,并与最大方差自动取阈值法和最小错误率自动取阈值法进行了对比研究,实验证明该算法好于其它两个算法。
3.针对杂草类植物的形态结构具有自相似特征,研究了杂草整体形态和杂草叶的分形特征,设计了计算分形维数的算法和程序,对杂草分形维数的分析显示,运用杂草的分形维数能够有效地识别杂草。
4.本文分析和讨论了基于灰度共生矩阵的纹理统计值的计算方法以及在不同光照条件下的杂草和麦苗的纹理统计值,结果显示杂草和麦苗的纹理统计值对光照不是很敏感,能够运用杂草和麦苗的纹理统计值识别杂草。
5.针对条播作物的行距基本恒定,条播作物都成行排列的位置特征,本文着重研究了作物中心行的提取方法,设计了改进的像素直方图法提取作物中心行的算法。通过分段提取作物中心行,有效地提高了算法的田间适应度。还研究了另外三种提取作物中心行的方法。
6.针对人工神经网络在模式识别上的优势,设计了三层BP人工神经网络,并运用三层BP人工神经网络进行了杂草识别的实验。结果显示,无论是基于纹理还是基于分形,运用人工神经网络都能够很好地识别杂草。
7.设计和开发了基于机器视觉的麦田除草剂对准喷药控制系统,该系统的视觉子系统首先用过绿的2G-R-B进行植物与土壤背景的分割,其次用改进像素直方图法识别作物中心行,用阈值法识别行间杂草区域,用BP神经网络基于分形和纹理的共同特征识别行内杂草区域,设计并开发了计算机与PLC的通信程序,设计了PLC的运行程序。设计并装配了喷药执行系统。
8.在校园里的一块水泥地上的模拟麦田进行了除草剂的对准杂草喷药控制的实验,实验结果表明喷头能够有选择地对准杂草区域喷药。
The usage method of chemistry herbicide is wide-spread to spray, spraying isn't to aim at a target thing accurately, but usually spraying at the soil without weed, so plenty of herbicide is wasted , resulting in not only the Human body hurt easily, but also the ecosystem environment crisis and increasing agricultural chemicals residue. Along with a great deal of usage of pesticide and herbicide, the hurt to the human body and the environment increase with each passing day. It is opposite to the modern agriculture which require precision, efficiency and Environment friendliness. For reducing cost and protecting environmental, the research of spraying precisely is done by more and more people, hoping it can take the place of the traditional spraying method, so the labor condition and the efficiency of herbicide can be improved and the pollution of environment can be avoided.
The distributing of weed in the field is random and asymmetry. The discontinuity of weed distributing provide a basis for the precise-spraying. Because the constant row spacing is good for weed identification, the spraying control system aiming at weed in row wheat based on machine vision was designed and developed on the basis of research on abroad and domestic. The contents of the study could be briefly summarized as follows:
1.Image segmentation based on color characteristic is researched. The segmented result and consuming time about 13 factors of color were researched contrastively. The research result was the exceed green 2G-R-B is the best segmented factor.
2.Image segmentation based on taking threshold automatically can process the image in time. The algorithm of combining maximum variance and minimum error is designed. The contrast research certificate the algorithm is better to the other two algorithm.
3.Aiming at the alike characteristic of weed structure, the weed and weed leaf fractal characteristic was researched and algorithm of calculating fractal dimension is designed. The analysis of weed fractal dimension verified it can be used for weed identification effectively.
4.The calculating method of statistical texture value based on gray co-occurrence and the texture value of weed and wheat under different sunshine were analysised. The texture value is not sensitive to the sunshine and it can be used for weed identification.
5.Aiming at the position characteristic of constant spacing, the method of obtaining the center line of crop rows is researched. The improved pixel lateral histogram algorithm for extracting the center of crop row is designed. The infield adaptability of the algorithm was enhanced by subsection computation. Other three method of extracting the center of crop row is also studied.
6. Aiming at advantage of pattern recognition of ANN, three layer BP ANN is designed for the experiment of weed identification. The experimental result is that the ANN cold be used for weed identification based on texture value or/and fractal dimension.
7.The spraying control system aiming at weed based on machine vision was designed and developed. The index of 2G-R-B is used for segmentation and the method of improved pixel lateral histogram is used for extracting the center of crop row,the method of threthold is also used for identificating between-row weed and BP ANN based on fratal and texture is used for identificating in-row weed. PLC correspondence and performing procedure is desi -gned and developed. Spraying performing system is designed and assembled.
8. The experiment was done on a plot modifying wheat field, the result verified that the control system is able to spray selectively.
杂草在田间的分布是随机的,不均匀的,而且具有簇生性。正是这种杂草分布的不连续性,为精确施药技术的可行性提供了依据。因为条播作物的行距基本恒定,条播作物都成行排列,从而有利于基于机器视觉的杂草识别,在总结国内外相关研究的基础上,本文以条播作物-小麦为主要研究对象,设计和开发了基于机器视觉的杂草对准喷药控制系统。本文的主要研究内容如下:
1.研究了基于颜色特征的图像背景分割,对十三个分割因子就分割质量和分割所需的时间进行了对比研究。通过研究发现过绿的2G-R-B是最佳的分割因子。
2.自动取阈值法分割图像能够大大提高图像处理的实时性,本文设计了最大类间方差和最小错误率相结合的自动取阈值算法,并与最大方差自动取阈值法和最小错误率自动取阈值法进行了对比研究,实验证明该算法好于其它两个算法。
3.针对杂草类植物的形态结构具有自相似特征,研究了杂草整体形态和杂草叶的分形特征,设计了计算分形维数的算法和程序,对杂草分形维数的分析显示,运用杂草的分形维数能够有效地识别杂草。
4.本文分析和讨论了基于灰度共生矩阵的纹理统计值的计算方法以及在不同光照条件下的杂草和麦苗的纹理统计值,结果显示杂草和麦苗的纹理统计值对光照不是很敏感,能够运用杂草和麦苗的纹理统计值识别杂草。
5.针对条播作物的行距基本恒定,条播作物都成行排列的位置特征,本文着重研究了作物中心行的提取方法,设计了改进的像素直方图法提取作物中心行的算法。通过分段提取作物中心行,有效地提高了算法的田间适应度。还研究了另外三种提取作物中心行的方法。
6.针对人工神经网络在模式识别上的优势,设计了三层BP人工神经网络,并运用三层BP人工神经网络进行了杂草识别的实验。结果显示,无论是基于纹理还是基于分形,运用人工神经网络都能够很好地识别杂草。
7.设计和开发了基于机器视觉的麦田除草剂对准喷药控制系统,该系统的视觉子系统首先用过绿的2G-R-B进行植物与土壤背景的分割,其次用改进像素直方图法识别作物中心行,用阈值法识别行间杂草区域,用BP神经网络基于分形和纹理的共同特征识别行内杂草区域,设计并开发了计算机与PLC的通信程序,设计了PLC的运行程序。设计并装配了喷药执行系统。
8.在校园里的一块水泥地上的模拟麦田进行了除草剂的对准杂草喷药控制的实验,实验结果表明喷头能够有选择地对准杂草区域喷药。
The usage method of chemistry herbicide is wide-spread to spray, spraying isn't to aim at a target thing accurately, but usually spraying at the soil without weed, so plenty of herbicide is wasted , resulting in not only the Human body hurt easily, but also the ecosystem environment crisis and increasing agricultural chemicals residue. Along with a great deal of usage of pesticide and herbicide, the hurt to the human body and the environment increase with each passing day. It is opposite to the modern agriculture which require precision, efficiency and Environment friendliness. For reducing cost and protecting environmental, the research of spraying precisely is done by more and more people, hoping it can take the place of the traditional spraying method, so the labor condition and the efficiency of herbicide can be improved and the pollution of environment can be avoided.
The distributing of weed in the field is random and asymmetry. The discontinuity of weed distributing provide a basis for the precise-spraying. Because the constant row spacing is good for weed identification, the spraying control system aiming at weed in row wheat based on machine vision was designed and developed on the basis of research on abroad and domestic. The contents of the study could be briefly summarized as follows:
1.Image segmentation based on color characteristic is researched. The segmented result and consuming time about 13 factors of color were researched contrastively. The research result was the exceed green 2G-R-B is the best segmented factor.
2.Image segmentation based on taking threshold automatically can process the image in time. The algorithm of combining maximum variance and minimum error is designed. The contrast research certificate the algorithm is better to the other two algorithm.
3.Aiming at the alike characteristic of weed structure, the weed and weed leaf fractal characteristic was researched and algorithm of calculating fractal dimension is designed. The analysis of weed fractal dimension verified it can be used for weed identification effectively.
4.The calculating method of statistical texture value based on gray co-occurrence and the texture value of weed and wheat under different sunshine were analysised. The texture value is not sensitive to the sunshine and it can be used for weed identification.
5.Aiming at the position characteristic of constant spacing, the method of obtaining the center line of crop rows is researched. The improved pixel lateral histogram algorithm for extracting the center of crop row is designed. The infield adaptability of the algorithm was enhanced by subsection computation. Other three method of extracting the center of crop row is also studied.
6. Aiming at advantage of pattern recognition of ANN, three layer BP ANN is designed for the experiment of weed identification. The experimental result is that the ANN cold be used for weed identification based on texture value or/and fractal dimension.
7.The spraying control system aiming at weed based on machine vision was designed and developed. The index of 2G-R-B is used for segmentation and the method of improved pixel lateral histogram is used for extracting the center of crop row,the method of threthold is also used for identificating between-row weed and BP ANN based on fratal and texture is used for identificating in-row weed. PLC correspondence and performing procedure is desi -gned and developed. Spraying performing system is designed and assembled.
8. The experiment was done on a plot modifying wheat field, the result verified that the control system is able to spray selectively.
引文
[1]Pedersen S M,Fountas S,Have H,et al.Agricultural robots-system analysis and economic feasibility[J].Precision Agric(2006) 7:295-308.
[2]Marr D.Vision[M].W H Freeman and Company,1982
[3]贾云得.机器视觉[M].北京:科学出版社,2003
[4]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
[5]刘颍.我国农药使用现状、原因及对策研究.国土与自然资源研究,2005(4):50-51
[6]何雄奎.改变我国植保机械和施药技术严重落后的现状.农业工程学报,2004,20(1):13-15
[7]贾乃新,王淑华,刘海凤.病虫草害防治中的精准施药问题.农业与技术,2004(3):101-105
[8]马辉.我国应大力发展农药精准施药技术.农药,2002,41(6):47
[9]王月青,毛文华等.麦田杂草的实时识别系统研究[J].农机化研究,2004,11
[10]郭振东.农田杂草的危害与防除[J],河南农业,2003,9
[11]徐鹤岭.五大作物农田杂草群落种群延替研究[C]:面向21世纪中国农田杂草可持续治理,中国植物学会杂草学分会.南宁:广西民族出版社,1999,5
[12]苏少泉,宋祖顺.中国农田杂草化学防治[M].北京:中国农业出版社,1996
[13]Brian P,Cousens R.The effect of weed distribution on predictions of yield loss[J].Journal of Applied Ecology.1990,27(2):735-742
[14]Thornton P K,Fawcett R H,Dent J B,et al..Spatial weed distribution and economic thresholds for weed control[J].Crop Protection.1990,9,337-342
[15]Stafford J V.The role of technology in the emergence and current status of precision agriculture,in:A.Srinivasan(Ed.),Handbook of Precision Agriculture.Food Products Press,New York,2006,pp.19-56
[16]姬长英.农业生产过程智能化的发展与展望,农业机械学报,1999,30(1):106-110
[17]Heisel T,Chrisrensen S,Walter A M.Weed managing model for patch spraying in cereal[C].In:Precision Agriculture.Proceedings of the 3~(rd) International Conference,edited by P.C.Robert,R.H.Rust and.E.Larson(ASA,Madison,WI,USA),1996,pp.99-1007.
[18]Christensen S,Heisel T,Walter A M.Patch spraying in cereals[C].In:Proceedings of the 2nd International Weed Control Congress,edited by H.Brown,G.W.et al(Department of Weed Control and Pesticide Ecology,Flakkebjerg,Denmark),1996,pp.963-968
[19]Timmermann C,Gerhards R,Kuhbauch W.The economic impact of site-specific weed control[J].Precision Agriculture.2003,4,249-260
[20]Dicke D,Gerhards R,Buechse A et al.Modeling Spatial and Temporal Dynamics of Chenopodium album L.Under the Influence of Site-specific Weed Control[J].Crop Protection.2007(26) 206-211
[21] Martin M, Williams II. Two-year weed seedling population responses to a post-emergent method of site-specific weed management [J]. Precision Agriculture. 2000 (2): 247-263
[22] Lamb D W, Weedon M. Evaluating the accuracy of mapping weeds in fallow fields using airborne digital imagery[J].Weed Research. 1998, 38,443-451
[23] Lamb D W, Brown R B. Remotr-sensing and mapping of weeds in crops[J]. Journal of Agricultural Engineering Research. 2001,78(2), 117-125
[24] Stafford J V, Bars J M L, Ambler B. A hand-held data logger with integral GPS for producing weed maps by field walking[J]. Computers and electronics in agriculture. 1996 (14),235-247
[25] Menges R M, Nixon P R, Richardson A J.. Light reflectance and remote sensing of weeds in agronomic and horticultural crops[J]. Weed Science. 1985,33(4), 569-581
[26] Everitt J H, Escobar D E., Villarreal R et al.Intergetaion of airborne video, global positioning system, and geographic information technologies for detecting and mapping two woody legumes on rangelands[J]. Weed Technology. 1993b,7(4),981-978
[27] Anderson, G.L., Everitt, J.H., Richardson, A.J. et al. Using satellite data to map false broomweed (Ericameria ausetrotexana) infestations on south Texas rangelands[J]. Weed Technology. 1993, 7(4),865-871
[28] Lass L W, Carson H W,Callihan R H. Detection of yellow starthistle (Centaurea solstitalis) and commom St. Johnswort(Hypericum perforatum) with multispectral digital imagery [J]. Weed Technology. 1996,10(3), 466-474
[29] Medlin C R, Shaw D R, Gerard P D, et al. Using remote sensing to detect weed infestations in Glycine max[J]. Weed Science. 2000, 48, 393-398
[30] Varner B L, Gress T A, Copenhaver K et al. Detection of cockleburs (Xanthium strumarium L.) in soybeans using hyperspectral imagery[C]. In: Proceedings of the 5th International Conference on Precision Agriculture(ASA-CSSA-SSSA, Madison, WL, USA) .2000
[31] Zhang N, Kuklman D K. Weed detection in soybean and small grain fields[J]. Standard Research Proposal. Biological and Agricultural Engineering Department. Kansas State University. Manhattan, Kansas.
[32] Tang L, Tian L,Steward B L. Color image segmentation with genetic algorithm for in field weed sensing[J]. Transactionof the ASAE. 2000 ,43(4) :1019 — 1027
[33] Steward B L, Tian L. Machine-vision weed density estimation for real-time outdoor lighting conditions[J]. Transaction of ASAE. 1999,vol.42(6): 1897-1909
[34] Guyer D E, Miles G E, Schreiber M M, et al. Machine vision and image processing for plant identification[J]. Trans of the ASAE. 1986 ,29(6) :1500-1507
[35] Guyer D E, Miles G E, Gaultney L D et al. Application of machine vision to shape analysis in leaf and plant identification[J].Transactions of ASAE.1993,36(1): 163-171
[36] Zhang N, Chaisattapagon C. Effective criteria for weed identification in wheat fields using machine vision[J]. Transactions of ASAE, 1995, 38(3):965-975
[37] Woebbecke D M, Meyer G E, Bargen K V et al. Shape features for identifying young weeds using image analysis[J]. Transactions of the ASAE. 1995, 38 (1): 271-281
[38] Yonekawa S. Identification of idealized leaf types using simple dimensionless shaper factors by image analysis[J]. TRansaction of the ASAE. 1996,39(4): 1525-1533
[39] Lee W S, Slaughter D C, Giles D K. Robotic Weed control system for tomatoes[J]. Precision Agriculture. 1999,1,95-113
[40] Tellaeche A, Burgos-Artizzu X P, Pajares G et al. A new vision-based approach to differential spraying in precision agriculture[J]. Computers and electronics in agriculture. available online www.elsevier.com/locate/compag.
[41] Perez A J, Lopez F, Benlloch J V et al. Colour and shape analysis techniques for weed detection in cerealfields[J]. Computers and electronics in agriculture. 2000 (25)197-212.
[42] Hayes J C, Han Y J.Comparison of crop cover measuring systems[J]. ASAE. 1989, Paper No.89-2663
[43] Meyer G E, Mehta T, Kocher M R et al. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying[J]. Transactions of the ASAE. 1998,41( 4): 1189-1197
[44] Burks T F, Shearer S A, Gates R S et al. Backpropagation neural network design and evaluation for classifying weed species using color image texture[J]. Transactions of the ASAE.2000,43(4): 1029-1037
[45] Burks T F, Shearer S A, Payne F A. Classification of weed species using color texture features and discriminant analysis[J]. Transactions of ASAE. 2000,43 (2): 441-448
[46 ] Tang L, Tian L F, Steward B L et al. Texture-based weed classification using Gabor wavelets and neural network for real-time selective herbicide applications[J]. ASAE. 1999 ,Paper No.993036
[47] shearer S A, Holmes R G. Plant identification using color co-occurrence matrices[J]. Transactions of the ASAE. 1990,33(6):2037-2044
[48] Gebhardt S, Kuehbauch W. A new algorithm for automatic rumex obtusifolius detection in digital images using color and texture features and the influence of image resolution[J]. Precision Agric.2007(8):1-13.
[49] Baret F. Guyot G. Potential and limits of vegetation indices for LAI and PAR assessment[J]. Remote Sens.Environ.1991, 35, 161-173.
[50] Felton W L, Doss A F, Nash P G et al. A microprocessor controlled technology to selectively spot spray weeds[C]. Proc. of the Automated Agricultural for the 21Century Symposium, Chicago, IL.1991, 427-432
[51] Shearer S A, Thomasson J A, Mcneill S G. Filter selection for NIR sensing of plant and soil materials[J]. Transaction of the ASAE. 1996,39(3):1209-1214
[52] Han Y J, Hayes J C. Soil cover determination using color image analysis[J]. Transaction of the ASAE ,1990 ,33(4) :1402-1407
[53] Hans-R L, Hartmut B, Helmut S A. Special vegetation index for the weed detection in sensor based precision agriculture[J]. Environmental Monitoring and Assessment. 2006 (117):505-518
[54] Borregaard T, Nielsen H, Norgaard L et al. Crop-weed discrim ination by line imagineing spectroscopy[J]. Journal of Agricultural Engineering Rearsch. 2000,75(4):389-400.
[55] Feyaerts F. Multi-spectral vision system for weed detection under field conditions using controlled lighting[J]. Transactions of the ASAE, 2001,78(3):233-243
[56] Ning Wang, Naiqian Zhang, Yurui Sun. Development of a spectral-based weed sensor[C]. ASAE Annual International Meeting, 1999
[57] Franz E, Gebhardt M R, Unklesbay K. B.. The use of local spectral properties of leaves as an aid for identifying weed seedlings in digital images[J]. Transactions of the ASAE.1991b,34(2):682-687
[58] Nobel S D, Crowe T G. Analysis of crop and weed leaf diffuse reflectance spectra[J]. Transactions of the ASAE 48(6),2379-2387.
[59] Slaughter D C. Development of a robotic system for non-chemical weed control. Report to Califonia Tomato Research Institute. Sacramento, Calif. 1994
[60] Giles D K, Slaughter D C. Precison band spraying with machine-vision guidance and adjustable yaw nozzles[J]. Transactions of the ASAE,1997,40(1):29-36
[61] Olsen H J. Determination of row position in small-grain crops by analysis of video images[J]. Computers and electronics in agriculture. 1995,12(2):147-162
[62] Marchand J A. Tracking of row structure in three crops using image analysis [J]. Computers and Electronics in Agriculture. 1996,15:161～179
[63] Southall B, Marchant J A, Hague T. Model Based Tracking for Navigation and Segmentation[C]. Computer Vision. 5~(th)European Conference on Computer Vision, Freiburg,Germany,June 1998(1)
[64] Hooper A W, Harries G O, Ambler B. A photoelectric sensor for distinguishing between plant material and soil[J]. Journal of Agricultural Engineering Research. 1976,21,145-155
[65] Hanks J E, Beck J L.. Sensor-controlled hooded sprayer for row crops[J]. Weed Technology. 1998,12, 308-314
[66] Blackshaw R E, Moonlar L J, Chevalier D F et al. Factors affecting the operation of the weed-sensing Detectspray system[J]. Weed Science. 1998a, 46,127-131
[67] Blackshaw R E, Molnar L J, Lindwall C W.Merits of a weed-sensing sprayer to control weeds in conservation fallow and cropping systems[J]. Weed Science. 1998b, 46, 120-126 [68] Ruixiu Sui,. Thomasson J A, Hanks J et al. Ground-based sensing system for weed mapping in cotton[J]. Computer and Electronics in Agriculture. Available at www.sciencedirect.com
[69] Lei Tian, Slaughter D C. Environmentally adaptive segmentation algorithm for outdoor image segmentation[J]. Computers and electronics in agriculture. 1998,21(3):153-168
[70] Marchant J A, Tillett D D, Brivot R. Real-time segmentation of plants and weeds[J]. Real-time imaging. 1998, 4(4),243-253
[71] Pomar J, Hidalgo I. An intelligent multimedia system for identification of weed seedlings [J]. Computers and electronics in agriculture. 1998,19(3):249-264
[72] Sacnz A J, Marchant J A. Fusing 3D information for crop/weeds classification[C]. Pattern Recognition. Proceedings. 15th International Conference. 4:295-298.IEEE,2000
[73] Mann A G, Rabatel G, Assemat L et al. Weed leaf image segmentation by deformable templates. J.Agric.Engng.Res. 2001, 80(2):139-146
[74] Cho S I, Lee D S, Jeong JY. Weed-plant discrimination by machine vision and artificial neural network[J]. Biosystems Engineering. 2002, 83(3):275-280
[75] Yang C C, Prasher S O, Landry J et al. Development of neural networks for weed recognition in corn fields[J]. Transactions of the ASAE. 2002 45(3):859-864
[76] Marchant J A, Onyango C M. Comparison of a Bayesian classifier with a multiplayer feed-forward neural network using the example of plant/weed/soil discrimination[J]. Computer and Electronics in agriculture. 2003 39(1):3-22
[77] Granitto P M, Verdes P F, Ceccatto H A. Large-scale investigation of weed seed identification by machine vision[J]. Computers and electronics in agriculture. 2005, 47(1):15-24
[78] Burks T F, Shearer S A, Heath J R. Evaluation of neural-network classifiers for weed species discrimination[J]. Biosystems Engineering. 2005, 91(3):293-304
[79] Tillet N D, Hagure T. Inter-row vision guidance for mechanical weed control in sugar beet[J]. Computers and Electronics in Agricultrue. 2002,33:163～ 177
[80] Tian L, Reid J R? Hummel J W. Development of precision sprayer for site-specific weed management[J]. Transaction of ASAE, 1999, 42(4): 1344-1351
[81] Lei Tian. Development of a sensor-based precision herbicide application system t[J]. Computers and Electronics in agriculture. 2002,36,133-149
[82]陈勇.自动施药机器人及可变量控制系统研究[D].南京:南京林业大学,2005
[83]Giles D K,Slaughter D C.Precision band spraying with machine-vision guidance and adjust able yaw nozzles[J].Transactions of the ASAE,1997,40(1) 132-140
[84]Dohi M.Robot and its application to weed control in hill space.[J].Journal of the Japanese Society of Agricultural Machinary.1993,55(6):77-84
[85]Gerhards R,Sokefeld M,Timmermann C et al.Site-specific weed control in maize,sugar beet,winter wheat and winter barley[J].Precision Agriculture.2002,3,25-35
[86]Nordmeyer H.Patchy weed distribution and site-specific weed control in winter cereals[J].Precision Agriculture.2006,7,219-231
[87]Tellaeche A,Burgos-Artizzu X P,Pajares G et al.A vision-based method for weeds identification through the Bayesian decision theory[J].Pattern Recognition.available online www.elsevier,com/locate/pr
[88]龙满生.玉米苗期杂草识别的机器视觉研究[D].杨凌:西北农林科技大学,2002
[89]相阿荣.识别杂草和土壤背景物的图像处理方法研究[D]。北京:中国农业大学,2001
[90]纪寿文.应用计算机图象处理技术识别玉米苗期田间杂草的研究[J].农业工程学报,2000,17(2):154-155
[91]纪良文,赵德安,吴春笃.喷药机器人辅助视觉系统中超声测距数据的采集与处理[J].江苏理工大学学报,1999,20(3):86-90
[92]毛文华.基于机器视觉的田间杂草识别技术的研究[D].北京:中国农业大学,2004
[93]张健钦.计算机视觉技术在杂草识别中的应用研究[D].保定:河北农业大学,2003
[94]尚卓.图像处理与自动控制技术在农药喷洒机中的研究[D].北京:中国农业大学,2003
[95]葛玉峰.基于机器视觉的室内模拟农药精确对靶施用系统研究[D].南京:南京林业大学,2003
[96]王荣本,纪寿文.基于机器视觉的玉米施肥智能机器系统设计概述[J]。农业工程学报,2001(3):151-153
[97]刘振恒.应用图像处理技术识别田间杂草方法的研究[D].沈阳:东北农业大学,2004
[98]刘敏.基于分形的田间杂草图像分析与识别的研究[D].北京:北京工业大学,2002
[99]毛文华,王一鸣,张小超等.基于机器视觉的苗期杂草实时分割算法[J].农业机械学报,2005,36(1)83-86
[100]吕俊伟,马成林,于永胜.采用多光谱图像融合提高作物和杂草灰度比值[J].农业工程学报,2005,21(11):99-102
[101]吕朝辉,陈晓光,吴文福等.用BP神经网络进行秧苗图像分割[J].农业工程学报,2001,17(3):146-148
[102]孙明,凌云.基于计算机视觉的萝卜幼苗自动识别技术[J].农业机械学报,2002,33(5):75-77
[1]林开颜,吴军辉,徐立鸣.彩色图像分割方法综述[J].中国图象图学报,2005,10(1):1-10
[2]Tremeau A,Borel N.A region growing and merging algorithm to color segmentation[J].Pattern Recognition,1997,30(7):1191-1203
[3]Cheng H D,Sun Y.A hierarchical approach to color image segmentation using homogeneity[J].IEEE Transactions on Image Processing,2000,9(12):2071-2082
[4]吕俊伟,马成林,于永胜.采用多光谱图像融合提高作物和杂草灰度比值[J].农业工程学报.第21卷第11期,99-102.
[5]钱永兰,杨邦杰,雷廷武.用基于HIS变换的SPOT-5遥感图像融合进行作物识别[J].农业工程学报.第21卷第1期,102-105.
[6]崔艳丽,程鹏飞,董晓志等.温室植物病害的图像处理及特征值提取方法的研究[J].农业工程学报.第21卷增刊,32-34.
[7]刘木华,赵杰文,邹小波.牛胸椎骨图像中软骨和硬骨区域的自动分割技术[J].农业工程学报.2005年08期,110-113.
[8]吕朝辉,陈晓光,吴文福等.机器视觉田间植物检测与识别技术[J].吉林工业大学自然科学学报.2001年7月,第31卷第3期,90-94
[9]张全海,施鹏飞.基于HSV空间彩色图像的边缘提取算法[J].计算机仿真.2000.9(7),122-125
[10]李秀智.基于机器视觉的苹果分级系统研究[D].南京:南京农业大学,2003
[11]饶洪辉.基于机器视觉的作物对行喷药控制系统研究[D].南京:南京农业大学,2006
[1]Tyler S W,Wheatcraft S W.Application of fractal mathematics to soil water retention estimation[J].Soil Science Society of America Journal,1989,53:987-996
[2]冯斌,汪懋华.基于颜色分形的水果计算机视觉分级技术[J].农业工程学报,2002年3月第18卷第2期:141-144
[3]Richard P T,Adam P M,David J.Fractal analysis of Pollocks drip paintings[J].Nature,1999,399(3):422
[4]David A E,Llarion V M,Werner P et al.Mechanisms of extensive spatiotemporal chaos in Rayleigh Benard convection[J],nature,2000,404(13):733-736
[5]法尔科内.分形几何—数学基础及其应用[M].谢和平编译.重庆:重庆大学出版社,1991.
[6]冯志刚,周宏伟.图像的分形维数计算方法及其应用[J]。江苏理工大学学报(自然科学版)。2001年11月:92-95
[7]黎云祥,刘玉成,钟章成.植物种群生态学中的构件理论[J].生态学杂志,1995,14(6):35-41
[8]董连科编著.分形理论及其应用[M].沈阳:辽宁科学技术出版社,1991
[9]董远,胡光锐.图像分形维数计算技术[J].计算机应用与软件,2001年第18卷第6期:61-65
[10]Levine M D.Vision in Man and Machine[M].McGraw-Hill Book Company,New York,NY,1985
[11]Julesz B,Visual Pattern Discrimination[J].IRE Transactions Information Theory.Vol 8(2),Feb.1962,84-92
[12]Haralick R M,Shanmugam K.Combined Spectral and Spatial Processing of ERTS Imagery Data.Journal of Remote Sensing of the Enviroment,Vol 3,1974,pp3-13
[13]Shear S A.Plant identification using color co-occurrence matrices derived from digitized images[D].Phd.Thesis,Ohio State University.1986
[14]单成钢,廖树华,龚宇等.冬小麦田间图像的群体纹理性研究[J]。麦类作物学报,2006,26(5):88-91
[15]张宏林.Visual C++数字图像模式识别技术及工程实践[M].北京:人民邮电出版社,2003
[16]毛文华,王一鸣,张小超等.基于机器视觉的田间杂草识别技术研究进展[J].农业工程学报,2004年9月第20卷第5期:43-46
[17]相阿荣.识别杂草和土壤背景物的图像处理方法研究[D].北京:中国农业大学,2001
[18]Yonekawa S,Sakai N,Kitani O.Identification of idealized leaf types using simple dimensionless shape factors by image analysis[J].Transactions of the ASAE,1996,39(4):1523-1533
[19]毛文华.基于机器视觉的田间杂草识别技术研究[D].北京:中国农业大学,2004
[20]傅卓,章毓晋.一种新的亚像素边缘检测方法及其性能研究.电脑应用技术.1995,35:1-5
[21]王四龙,宁书年.利用霍夫变换机制提取重磁数据构造信息.煤炭学报,1998,23(4):242-346
[22]周俊.农用轮式移动机器人视觉导航系统的研究[D].南京:南京农业大学,2003
[23]曾接贤,张桂梅,储珺等.霍夫变换与最小二乘法相结合的直线拟合[J].南昌航空工业学报,2003,17(4):9-13
[24]Lipton.图像处理技术[M].http://www.jqsj.com/download/linedetection.pdf,2002
[25]饶洪辉.基于机器视觉的作物对行喷药控制系统研究.南京:南京农业大学,2006
[26]戴渊明,张翔,王再富.基于改进概率霍夫变换算法的车道检测方法研究[J].杭州电子科技大学报2006年第五期:91-95
[27]王月青,毛文华,王一鸣.麦田杂草的适时识别系统研究[J].农机化研究,2004年第6期:63-66
[1] Lee W S, Slaughter D. Plant recognition using hardware-based neural network[J]. ASAE Paper 1998,No.98-3040
[2] Yang C, Prasher S, Landry J. Application of artificial neural networks to image recognition in precision farming[J]. ASAE Paper No.98-3039
[3] Burks T F, Shearer S A,Gates R S et al. Backpropogation neural network design and evaluation for classiffing weed species using color image texture feature[J].Transactions of ASAE, 200043(4), 1029-1037
[4] Burks T F, Shearer S A, Heath J R et al. Evaluation of neural network classifiers for weed species discrimination[J]. Biosystems Engineering. 2005 91(3):293-304
[5] Ismail Kavdir. Discrimination of sunflower, weed and soil by artificial neural networks[J].Computer and Electronocs in Agriculture. 2004,44,153-160
[1]Faki M S,Zhang N,Peterson D E.Factors affecting color-based weed detection[J].Transaction of the ASAE,2000,43(4):1001-1009
[2]宋伯生编著.PLC编程理论、算法及技巧[M].北京:机械工业出版社,2005
[3]郑萍主编.现代电气控制技术[M].重庆:重庆大学出版社,2001,111-113
[4]俞国亮主编.PLC原理与应用[M].北京:清华大学出版社,2005,221-259
[2]Marr D.Vision[M].W H Freeman and Company,1982
[3]贾云得.机器视觉[M].北京:科学出版社,2003
[4]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
[5]刘颍.我国农药使用现状、原因及对策研究.国土与自然资源研究,2005(4):50-51
[6]何雄奎.改变我国植保机械和施药技术严重落后的现状.农业工程学报,2004,20(1):13-15
[7]贾乃新,王淑华,刘海凤.病虫草害防治中的精准施药问题.农业与技术,2004(3):101-105
[8]马辉.我国应大力发展农药精准施药技术.农药,2002,41(6):47
[9]王月青,毛文华等.麦田杂草的实时识别系统研究[J].农机化研究,2004,11
[10]郭振东.农田杂草的危害与防除[J],河南农业,2003,9
[11]徐鹤岭.五大作物农田杂草群落种群延替研究[C]:面向21世纪中国农田杂草可持续治理,中国植物学会杂草学分会.南宁:广西民族出版社,1999,5
[12]苏少泉,宋祖顺.中国农田杂草化学防治[M].北京:中国农业出版社,1996
[13]Brian P,Cousens R.The effect of weed distribution on predictions of yield loss[J].Journal of Applied Ecology.1990,27(2):735-742
[14]Thornton P K,Fawcett R H,Dent J B,et al..Spatial weed distribution and economic thresholds for weed control[J].Crop Protection.1990,9,337-342
[15]Stafford J V.The role of technology in the emergence and current status of precision agriculture,in:A.Srinivasan(Ed.),Handbook of Precision Agriculture.Food Products Press,New York,2006,pp.19-56
[16]姬长英.农业生产过程智能化的发展与展望,农业机械学报,1999,30(1):106-110
[17]Heisel T,Chrisrensen S,Walter A M.Weed managing model for patch spraying in cereal[C].In:Precision Agriculture.Proceedings of the 3~(rd) International Conference,edited by P.C.Robert,R.H.Rust and.E.Larson(ASA,Madison,WI,USA),1996,pp.99-1007.
[18]Christensen S,Heisel T,Walter A M.Patch spraying in cereals[C].In:Proceedings of the 2nd International Weed Control Congress,edited by H.Brown,G.W.et al(Department of Weed Control and Pesticide Ecology,Flakkebjerg,Denmark),1996,pp.963-968
[19]Timmermann C,Gerhards R,Kuhbauch W.The economic impact of site-specific weed control[J].Precision Agriculture.2003,4,249-260
[20]Dicke D,Gerhards R,Buechse A et al.Modeling Spatial and Temporal Dynamics of Chenopodium album L.Under the Influence of Site-specific Weed Control[J].Crop Protection.2007(26) 206-211
[21] Martin M, Williams II. Two-year weed seedling population responses to a post-emergent method of site-specific weed management [J]. Precision Agriculture. 2000 (2): 247-263
[22] Lamb D W, Weedon M. Evaluating the accuracy of mapping weeds in fallow fields using airborne digital imagery[J].Weed Research. 1998, 38,443-451
[23] Lamb D W, Brown R B. Remotr-sensing and mapping of weeds in crops[J]. Journal of Agricultural Engineering Research. 2001,78(2), 117-125
[24] Stafford J V, Bars J M L, Ambler B. A hand-held data logger with integral GPS for producing weed maps by field walking[J]. Computers and electronics in agriculture. 1996 (14),235-247
[25] Menges R M, Nixon P R, Richardson A J.. Light reflectance and remote sensing of weeds in agronomic and horticultural crops[J]. Weed Science. 1985,33(4), 569-581
[26] Everitt J H, Escobar D E., Villarreal R et al.Intergetaion of airborne video, global positioning system, and geographic information technologies for detecting and mapping two woody legumes on rangelands[J]. Weed Technology. 1993b,7(4),981-978
[27] Anderson, G.L., Everitt, J.H., Richardson, A.J. et al. Using satellite data to map false broomweed (Ericameria ausetrotexana) infestations on south Texas rangelands[J]. Weed Technology. 1993, 7(4),865-871
[28] Lass L W, Carson H W,Callihan R H. Detection of yellow starthistle (Centaurea solstitalis) and commom St. Johnswort(Hypericum perforatum) with multispectral digital imagery [J]. Weed Technology. 1996,10(3), 466-474
[29] Medlin C R, Shaw D R, Gerard P D, et al. Using remote sensing to detect weed infestations in Glycine max[J]. Weed Science. 2000, 48, 393-398
[30] Varner B L, Gress T A, Copenhaver K et al. Detection of cockleburs (Xanthium strumarium L.) in soybeans using hyperspectral imagery[C]. In: Proceedings of the 5th International Conference on Precision Agriculture(ASA-CSSA-SSSA, Madison, WL, USA) .2000
[31] Zhang N, Kuklman D K. Weed detection in soybean and small grain fields[J]. Standard Research Proposal. Biological and Agricultural Engineering Department. Kansas State University. Manhattan, Kansas.
[32] Tang L, Tian L,Steward B L. Color image segmentation with genetic algorithm for in field weed sensing[J]. Transactionof the ASAE. 2000 ,43(4) :1019 — 1027
[33] Steward B L, Tian L. Machine-vision weed density estimation for real-time outdoor lighting conditions[J]. Transaction of ASAE. 1999,vol.42(6): 1897-1909
[34] Guyer D E, Miles G E, Schreiber M M, et al. Machine vision and image processing for plant identification[J]. Trans of the ASAE. 1986 ,29(6) :1500-1507
[35] Guyer D E, Miles G E, Gaultney L D et al. Application of machine vision to shape analysis in leaf and plant identification[J].Transactions of ASAE.1993,36(1): 163-171
[36] Zhang N, Chaisattapagon C. Effective criteria for weed identification in wheat fields using machine vision[J]. Transactions of ASAE, 1995, 38(3):965-975
[37] Woebbecke D M, Meyer G E, Bargen K V et al. Shape features for identifying young weeds using image analysis[J]. Transactions of the ASAE. 1995, 38 (1): 271-281
[38] Yonekawa S. Identification of idealized leaf types using simple dimensionless shaper factors by image analysis[J]. TRansaction of the ASAE. 1996,39(4): 1525-1533
[39] Lee W S, Slaughter D C, Giles D K. Robotic Weed control system for tomatoes[J]. Precision Agriculture. 1999,1,95-113
[40] Tellaeche A, Burgos-Artizzu X P, Pajares G et al. A new vision-based approach to differential spraying in precision agriculture[J]. Computers and electronics in agriculture. available online www.elsevier.com/locate/compag.
[41] Perez A J, Lopez F, Benlloch J V et al. Colour and shape analysis techniques for weed detection in cerealfields[J]. Computers and electronics in agriculture. 2000 (25)197-212.
[42] Hayes J C, Han Y J.Comparison of crop cover measuring systems[J]. ASAE. 1989, Paper No.89-2663
[43] Meyer G E, Mehta T, Kocher M R et al. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying[J]. Transactions of the ASAE. 1998,41( 4): 1189-1197
[44] Burks T F, Shearer S A, Gates R S et al. Backpropagation neural network design and evaluation for classifying weed species using color image texture[J]. Transactions of the ASAE.2000,43(4): 1029-1037
[45] Burks T F, Shearer S A, Payne F A. Classification of weed species using color texture features and discriminant analysis[J]. Transactions of ASAE. 2000,43 (2): 441-448
[46 ] Tang L, Tian L F, Steward B L et al. Texture-based weed classification using Gabor wavelets and neural network for real-time selective herbicide applications[J]. ASAE. 1999 ,Paper No.993036
[47] shearer S A, Holmes R G. Plant identification using color co-occurrence matrices[J]. Transactions of the ASAE. 1990,33(6):2037-2044
[48] Gebhardt S, Kuehbauch W. A new algorithm for automatic rumex obtusifolius detection in digital images using color and texture features and the influence of image resolution[J]. Precision Agric.2007(8):1-13.
[49] Baret F. Guyot G. Potential and limits of vegetation indices for LAI and PAR assessment[J]. Remote Sens.Environ.1991, 35, 161-173.
[50] Felton W L, Doss A F, Nash P G et al. A microprocessor controlled technology to selectively spot spray weeds[C]. Proc. of the Automated Agricultural for the 21Century Symposium, Chicago, IL.1991, 427-432
[51] Shearer S A, Thomasson J A, Mcneill S G. Filter selection for NIR sensing of plant and soil materials[J]. Transaction of the ASAE. 1996,39(3):1209-1214
[52] Han Y J, Hayes J C. Soil cover determination using color image analysis[J]. Transaction of the ASAE ,1990 ,33(4) :1402-1407
[53] Hans-R L, Hartmut B, Helmut S A. Special vegetation index for the weed detection in sensor based precision agriculture[J]. Environmental Monitoring and Assessment. 2006 (117):505-518
[54] Borregaard T, Nielsen H, Norgaard L et al. Crop-weed discrim ination by line imagineing spectroscopy[J]. Journal of Agricultural Engineering Rearsch. 2000,75(4):389-400.
[55] Feyaerts F. Multi-spectral vision system for weed detection under field conditions using controlled lighting[J]. Transactions of the ASAE, 2001,78(3):233-243
[56] Ning Wang, Naiqian Zhang, Yurui Sun. Development of a spectral-based weed sensor[C]. ASAE Annual International Meeting, 1999
[57] Franz E, Gebhardt M R, Unklesbay K. B.. The use of local spectral properties of leaves as an aid for identifying weed seedlings in digital images[J]. Transactions of the ASAE.1991b,34(2):682-687
[58] Nobel S D, Crowe T G. Analysis of crop and weed leaf diffuse reflectance spectra[J]. Transactions of the ASAE 48(6),2379-2387.
[59] Slaughter D C. Development of a robotic system for non-chemical weed control. Report to Califonia Tomato Research Institute. Sacramento, Calif. 1994
[60] Giles D K, Slaughter D C. Precison band spraying with machine-vision guidance and adjustable yaw nozzles[J]. Transactions of the ASAE,1997,40(1):29-36
[61] Olsen H J. Determination of row position in small-grain crops by analysis of video images[J]. Computers and electronics in agriculture. 1995,12(2):147-162
[62] Marchand J A. Tracking of row structure in three crops using image analysis [J]. Computers and Electronics in Agriculture. 1996,15:161～179
[63] Southall B, Marchant J A, Hague T. Model Based Tracking for Navigation and Segmentation[C]. Computer Vision. 5~(th)European Conference on Computer Vision, Freiburg,Germany,June 1998(1)
[64] Hooper A W, Harries G O, Ambler B. A photoelectric sensor for distinguishing between plant material and soil[J]. Journal of Agricultural Engineering Research. 1976,21,145-155
[65] Hanks J E, Beck J L.. Sensor-controlled hooded sprayer for row crops[J]. Weed Technology. 1998,12, 308-314
[66] Blackshaw R E, Moonlar L J, Chevalier D F et al. Factors affecting the operation of the weed-sensing Detectspray system[J]. Weed Science. 1998a, 46,127-131
[67] Blackshaw R E, Molnar L J, Lindwall C W.Merits of a weed-sensing sprayer to control weeds in conservation fallow and cropping systems[J]. Weed Science. 1998b, 46, 120-126 [68] Ruixiu Sui,. Thomasson J A, Hanks J et al. Ground-based sensing system for weed mapping in cotton[J]. Computer and Electronics in Agriculture. Available at www.sciencedirect.com
[69] Lei Tian, Slaughter D C. Environmentally adaptive segmentation algorithm for outdoor image segmentation[J]. Computers and electronics in agriculture. 1998,21(3):153-168
[70] Marchant J A, Tillett D D, Brivot R. Real-time segmentation of plants and weeds[J]. Real-time imaging. 1998, 4(4),243-253
[71] Pomar J, Hidalgo I. An intelligent multimedia system for identification of weed seedlings [J]. Computers and electronics in agriculture. 1998,19(3):249-264
[72] Sacnz A J, Marchant J A. Fusing 3D information for crop/weeds classification[C]. Pattern Recognition. Proceedings. 15th International Conference. 4:295-298.IEEE,2000
[73] Mann A G, Rabatel G, Assemat L et al. Weed leaf image segmentation by deformable templates. J.Agric.Engng.Res. 2001, 80(2):139-146
[74] Cho S I, Lee D S, Jeong JY. Weed-plant discrimination by machine vision and artificial neural network[J]. Biosystems Engineering. 2002, 83(3):275-280
[75] Yang C C, Prasher S O, Landry J et al. Development of neural networks for weed recognition in corn fields[J]. Transactions of the ASAE. 2002 45(3):859-864
[76] Marchant J A, Onyango C M. Comparison of a Bayesian classifier with a multiplayer feed-forward neural network using the example of plant/weed/soil discrimination[J]. Computer and Electronics in agriculture. 2003 39(1):3-22
[77] Granitto P M, Verdes P F, Ceccatto H A. Large-scale investigation of weed seed identification by machine vision[J]. Computers and electronics in agriculture. 2005, 47(1):15-24
[78] Burks T F, Shearer S A, Heath J R. Evaluation of neural-network classifiers for weed species discrimination[J]. Biosystems Engineering. 2005, 91(3):293-304
[79] Tillet N D, Hagure T. Inter-row vision guidance for mechanical weed control in sugar beet[J]. Computers and Electronics in Agricultrue. 2002,33:163～ 177
[80] Tian L, Reid J R? Hummel J W. Development of precision sprayer for site-specific weed management[J]. Transaction of ASAE, 1999, 42(4): 1344-1351
[81] Lei Tian. Development of a sensor-based precision herbicide application system t[J]. Computers and Electronics in agriculture. 2002,36,133-149
[82]陈勇.自动施药机器人及可变量控制系统研究[D].南京:南京林业大学,2005
[83]Giles D K,Slaughter D C.Precision band spraying with machine-vision guidance and adjust able yaw nozzles[J].Transactions of the ASAE,1997,40(1) 132-140
[84]Dohi M.Robot and its application to weed control in hill space.[J].Journal of the Japanese Society of Agricultural Machinary.1993,55(6):77-84
[85]Gerhards R,Sokefeld M,Timmermann C et al.Site-specific weed control in maize,sugar beet,winter wheat and winter barley[J].Precision Agriculture.2002,3,25-35
[86]Nordmeyer H.Patchy weed distribution and site-specific weed control in winter cereals[J].Precision Agriculture.2006,7,219-231
[87]Tellaeche A,Burgos-Artizzu X P,Pajares G et al.A vision-based method for weeds identification through the Bayesian decision theory[J].Pattern Recognition.available online www.elsevier,com/locate/pr
[88]龙满生.玉米苗期杂草识别的机器视觉研究[D].杨凌:西北农林科技大学,2002
[89]相阿荣.识别杂草和土壤背景物的图像处理方法研究[D]。北京:中国农业大学,2001
[90]纪寿文.应用计算机图象处理技术识别玉米苗期田间杂草的研究[J].农业工程学报,2000,17(2):154-155
[91]纪良文,赵德安,吴春笃.喷药机器人辅助视觉系统中超声测距数据的采集与处理[J].江苏理工大学学报,1999,20(3):86-90
[92]毛文华.基于机器视觉的田间杂草识别技术的研究[D].北京:中国农业大学,2004
[93]张健钦.计算机视觉技术在杂草识别中的应用研究[D].保定:河北农业大学,2003
[94]尚卓.图像处理与自动控制技术在农药喷洒机中的研究[D].北京:中国农业大学,2003
[95]葛玉峰.基于机器视觉的室内模拟农药精确对靶施用系统研究[D].南京:南京林业大学,2003
[96]王荣本,纪寿文.基于机器视觉的玉米施肥智能机器系统设计概述[J]。农业工程学报,2001(3):151-153
[97]刘振恒.应用图像处理技术识别田间杂草方法的研究[D].沈阳:东北农业大学,2004
[98]刘敏.基于分形的田间杂草图像分析与识别的研究[D].北京:北京工业大学,2002
[99]毛文华,王一鸣,张小超等.基于机器视觉的苗期杂草实时分割算法[J].农业机械学报,2005,36(1)83-86
[100]吕俊伟,马成林,于永胜.采用多光谱图像融合提高作物和杂草灰度比值[J].农业工程学报,2005,21(11):99-102
[101]吕朝辉,陈晓光,吴文福等.用BP神经网络进行秧苗图像分割[J].农业工程学报,2001,17(3):146-148
[102]孙明,凌云.基于计算机视觉的萝卜幼苗自动识别技术[J].农业机械学报,2002,33(5):75-77
[1]林开颜,吴军辉,徐立鸣.彩色图像分割方法综述[J].中国图象图学报,2005,10(1):1-10
[2]Tremeau A,Borel N.A region growing and merging algorithm to color segmentation[J].Pattern Recognition,1997,30(7):1191-1203
[3]Cheng H D,Sun Y.A hierarchical approach to color image segmentation using homogeneity[J].IEEE Transactions on Image Processing,2000,9(12):2071-2082
[4]吕俊伟,马成林,于永胜.采用多光谱图像融合提高作物和杂草灰度比值[J].农业工程学报.第21卷第11期,99-102.
[5]钱永兰,杨邦杰,雷廷武.用基于HIS变换的SPOT-5遥感图像融合进行作物识别[J].农业工程学报.第21卷第1期,102-105.
[6]崔艳丽,程鹏飞,董晓志等.温室植物病害的图像处理及特征值提取方法的研究[J].农业工程学报.第21卷增刊,32-34.
[7]刘木华,赵杰文,邹小波.牛胸椎骨图像中软骨和硬骨区域的自动分割技术[J].农业工程学报.2005年08期,110-113.
[8]吕朝辉,陈晓光,吴文福等.机器视觉田间植物检测与识别技术[J].吉林工业大学自然科学学报.2001年7月,第31卷第3期,90-94
[9]张全海,施鹏飞.基于HSV空间彩色图像的边缘提取算法[J].计算机仿真.2000.9(7),122-125
[10]李秀智.基于机器视觉的苹果分级系统研究[D].南京:南京农业大学,2003
[11]饶洪辉.基于机器视觉的作物对行喷药控制系统研究[D].南京:南京农业大学,2006
[1]Tyler S W,Wheatcraft S W.Application of fractal mathematics to soil water retention estimation[J].Soil Science Society of America Journal,1989,53:987-996
[2]冯斌,汪懋华.基于颜色分形的水果计算机视觉分级技术[J].农业工程学报,2002年3月第18卷第2期:141-144
[3]Richard P T,Adam P M,David J.Fractal analysis of Pollocks drip paintings[J].Nature,1999,399(3):422
[4]David A E,Llarion V M,Werner P et al.Mechanisms of extensive spatiotemporal chaos in Rayleigh Benard convection[J],nature,2000,404(13):733-736
[5]法尔科内.分形几何—数学基础及其应用[M].谢和平编译.重庆:重庆大学出版社,1991.
[6]冯志刚,周宏伟.图像的分形维数计算方法及其应用[J]。江苏理工大学学报(自然科学版)。2001年11月:92-95
[7]黎云祥,刘玉成,钟章成.植物种群生态学中的构件理论[J].生态学杂志,1995,14(6):35-41
[8]董连科编著.分形理论及其应用[M].沈阳:辽宁科学技术出版社,1991
[9]董远,胡光锐.图像分形维数计算技术[J].计算机应用与软件,2001年第18卷第6期:61-65
[10]Levine M D.Vision in Man and Machine[M].McGraw-Hill Book Company,New York,NY,1985
[11]Julesz B,Visual Pattern Discrimination[J].IRE Transactions Information Theory.Vol 8(2),Feb.1962,84-92
[12]Haralick R M,Shanmugam K.Combined Spectral and Spatial Processing of ERTS Imagery Data.Journal of Remote Sensing of the Enviroment,Vol 3,1974,pp3-13
[13]Shear S A.Plant identification using color co-occurrence matrices derived from digitized images[D].Phd.Thesis,Ohio State University.1986
[14]单成钢,廖树华,龚宇等.冬小麦田间图像的群体纹理性研究[J]。麦类作物学报,2006,26(5):88-91
[15]张宏林.Visual C++数字图像模式识别技术及工程实践[M].北京:人民邮电出版社,2003
[16]毛文华,王一鸣,张小超等.基于机器视觉的田间杂草识别技术研究进展[J].农业工程学报,2004年9月第20卷第5期:43-46
[17]相阿荣.识别杂草和土壤背景物的图像处理方法研究[D].北京:中国农业大学,2001
[18]Yonekawa S,Sakai N,Kitani O.Identification of idealized leaf types using simple dimensionless shape factors by image analysis[J].Transactions of the ASAE,1996,39(4):1523-1533
[19]毛文华.基于机器视觉的田间杂草识别技术研究[D].北京:中国农业大学,2004
[20]傅卓,章毓晋.一种新的亚像素边缘检测方法及其性能研究.电脑应用技术.1995,35:1-5
[21]王四龙,宁书年.利用霍夫变换机制提取重磁数据构造信息.煤炭学报,1998,23(4):242-346
[22]周俊.农用轮式移动机器人视觉导航系统的研究[D].南京:南京农业大学,2003
[23]曾接贤,张桂梅,储珺等.霍夫变换与最小二乘法相结合的直线拟合[J].南昌航空工业学报,2003,17(4):9-13
[24]Lipton.图像处理技术[M].http://www.jqsj.com/download/linedetection.pdf,2002
[25]饶洪辉.基于机器视觉的作物对行喷药控制系统研究.南京:南京农业大学,2006
[26]戴渊明,张翔,王再富.基于改进概率霍夫变换算法的车道检测方法研究[J].杭州电子科技大学报2006年第五期:91-95
[27]王月青,毛文华,王一鸣.麦田杂草的适时识别系统研究[J].农机化研究,2004年第6期:63-66
[1] Lee W S, Slaughter D. Plant recognition using hardware-based neural network[J]. ASAE Paper 1998,No.98-3040
[2] Yang C, Prasher S, Landry J. Application of artificial neural networks to image recognition in precision farming[J]. ASAE Paper No.98-3039
[3] Burks T F, Shearer S A,Gates R S et al. Backpropogation neural network design and evaluation for classiffing weed species using color image texture feature[J].Transactions of ASAE, 200043(4), 1029-1037
[4] Burks T F, Shearer S A, Heath J R et al. Evaluation of neural network classifiers for weed species discrimination[J]. Biosystems Engineering. 2005 91(3):293-304
[5] Ismail Kavdir. Discrimination of sunflower, weed and soil by artificial neural networks[J].Computer and Electronocs in Agriculture. 2004,44,153-160
[1]Faki M S,Zhang N,Peterson D E.Factors affecting color-based weed detection[J].Transaction of the ASAE,2000,43(4):1001-1009
[2]宋伯生编著.PLC编程理论、算法及技巧[M].北京:机械工业出版社,2005
[3]郑萍主编.现代电气控制技术[M].重庆:重庆大学出版社,2001,111-113
[4]俞国亮主编.PLC原理与应用[M].北京:清华大学出版社,2005,221-259
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |