机器视觉田间植物检测与识别技术

摘要/Abstract

摘要： 在田间自动检测和识别植物(农作物和杂草)是对农作物进行防病、防虫和杂草控制的重要前提条件.本文综述了机器视觉田间植物检测和识别技术的国外最新研究方法和成果,以促进我国在该领域的应用和发展.

Abstract: Automated detection and identification of field plants(including crops and weeds) are very important to control pests,diseases,and weeds with machine vision.In order to promote the research and the application of this technique in China,the foreign research methods and advancements of application of machine vision technique in detecting and identifying field plants,the paper reviews and presents all the relevant information,which could be used for the references to the researchers who do the similar studies.

Key words: machine vision, plants, detecting, identifying

中图分类号:

吕朝辉, 陈晓光, 吴文福, 赵红霞. 机器视觉田间植物检测与识别技术[J]. 吉林大学学报(工学版), 2001, (3): 90-94.

L? Zhao-hui, CHEN Xiao-guang, WU Wen-fu, ZHAO Hong-xia . The Technique of Detecting and Identifying Field Plants by Machine Vision[J]. 吉林大学学报(工学版), 2001, (3): 90-94.

参考文献

[1] Zhang N, Chaisattapagon C. Effective criteria for weed identification in wheat fields using machine vision［J］. Transaction of the ASAE, 1995, 38(3):965～974.
[2] Thompson J F, Stafford J V, Miller P C H. Potential for automatic weed detection and selective herbicide application［J］.Crop Prod., 1991, 10(4) :254～259.
[3] 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.
[4] Elachi C. Introduction to the physics and technique of remote sensing［M］. New York. John & Sons, 1987.
[5] Han Y J, Hayes J C. Soil cover determination using color image analysis［J］. Transaction of the ASAE, 1990, 33(4): 1402～1407.
[6] Tarbell K A, Reid J F. A computer vision system for characterizing corn growth and development［J］. Transaction of the ASAE, 1991,34(5) :2245～2255.
[7] Gonzalez R C, Woods R E. Digital image processing［M］. Addison-Wesley Publishing Co. Inc., 1992.
[8] Weobbecke D M, Meyer G E, Von Bargen K, et al. Color indices for weed identification under various soil, residue, and lighting conditions［J］. Transaction of the ASAE, 1995 , 38(1): 259～269.
[9] 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.
[10] Meyer G E, Mehta T, Kocher M F, et al. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying［J］. Transaction of the ASAE, 1998, 41 (4): 1189～1197.
[11] Burks T F, Shearer S A, Payne F A. Classification of weed species using color texture features and discriminant analysis［J］. Transaction of the ASAE, 2000, 43 (2): 441～448.
[12] Hayes J C, Han Y J. Comparison of crop cover measyring system［A］. ASAE paper［C］ , St. Joseph, Mich., 1989.
[13] Tian L, Slaughter D C, Norris R F. Outdoor field machine vision identification of tomato seedlings for automated weed control［J］. Transaction of the ASAE, 1997, 40(6): 1761～1768.
[14] Tang L, Tian L, Steward B L. Color image segmentation with genetic algorithm for in-field weed sensing［J］. Transaction of the ASAE, 2000, 43(4): 1019～1027.
[15] EI-Faki M S, Zhang N, Peterson D E. Factors affecting color-based weed detection［J］. Transaction of the ASAE, 2000, 43(4): 1001～1009.
[16] Shearer S A, Holmes R G. Plant identification using color color-occurrence matrices［J］. Transaction of the ASAE, 1990, 33(6) :2037～2044.
[17] Franz E, Gebhardt M R, Unklesbay K B. Shape description of completely visible and partially occluded leaves for identifying plants in digital images［J］. Transaction of the ASAE, 1991,34(2):673～681.
[18] Weobbecke D M, Meyer G E, Von Bargen K, et al. Shape features for identifying young weeds using image analysis［J］.Transaction of the ASAE, 1995, 38(1): 271～281.
[19] Yonekawa S, Sakai N, Kitani O. Identification of idealized leaf types using simple dimensionless shape factors by image analysis［J］. Transaction of the ASAE, 1996, 39(4): 1525～1533.
[20] Tang 1, Tian F, Steward B 1, et al. Texture-based weed clas4fication using gabor wavelets and neural network for realtime selective herbicide application［A］. ASAE paper［C］ , St. Joseph, Mich., 1997.
[21] Cardina J, Sparrow D H, McCoy E L. Analysis of spatial distribution of common lambsquarters in no-till soybean［J］.Weed Sci., 1995, 43(2): 258～268.
[22] Benlloch J V, Rodas A. Dynamic model to detect weeds in cereals under actual field condition ［M］. In proc. SPIE, 1998.

相关文章 15

[1]	刘富,宗宇轩,康冰,张益萌,林彩霞,赵宏伟. 基于优化纹理特征的手背静脉识别系统[J]. 吉林大学学报(工学版), 2018, 48(6): 1844-1850.
[2]	刘恩泽,吴文福. 基于机器视觉的农作物表面多特征决策融合病变判断算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1873-1878.
[3]	苏寒松,代志涛,刘高华,张倩芳. 结合吸收Markov链和流行排序的显著性区域检测[J]. 吉林大学学报(工学版), 2018, 48(6): 1887-1894.
[4]	黄勇,杨德运,乔赛,慕振国. 高分辨合成孔径雷达图像的耦合传统恒虚警目标检测[J]. 吉林大学学报(工学版), 2018, 48(6): 1904-1909.
[5]	宋军, 石雪飞, 阮欣. 大体积混凝土热学参数识别的优化[J]. 吉林大学学报(工学版), 2018, 48(5): 1418-1425.
[6]	刘富, 兰旭腾, 侯涛, 康冰, 刘云, 林彩霞. 基于优化k-mer频率的宏基因组聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1593-1599.
[7]	刘元宁, 刘帅, 朱晓冬, 陈一浩, 郑少阁, 沈椿壮. 基于高斯拉普拉斯算子与自适应优化伽柏滤波的虹膜识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1606-1613.
[8]	车翔玖, 王利, 郭晓新. 基于多尺度特征融合的边界检测算法[J]. 吉林大学学报(工学版), 2018, 48(5): 1621-1628.
[9]	刘坤, 刘勇, 闫建超, 吉硕, 孙震源, 徐洪伟. 基于体外传感检测的人体站起动力学分析[J]. 吉林大学学报(工学版), 2018, 48(4): 1140-1146.
[10]	董惠娟, 于震, 樊继壮. 基于激光测振仪的非轴对称超声驻波声场的识别[J]. 吉林大学学报(工学版), 2018, 48(4): 1191-1198.
[11]	张浩, 占萌苹, 郭刘香, 李誌, 刘元宁, 张春鹤, 常浩武, 王志强. 基于高通量数据的人体外源性植物miRNA跨界调控建模[J]. 吉林大学学报(工学版), 2018, 48(4): 1206-1213.
[12]	姚海洋, 王海燕, 张之琛, 申晓红. 双Duffing振子逆向联合信号检测模型[J]. 吉林大学学报(工学版), 2018, 48(4): 1282-1290.
[13]	李志慧, 胡永利, 赵永华, 马佳磊, 李海涛, 钟涛, 杨少辉. 基于车载的运动行人区域估计方法[J]. 吉林大学学报(工学版), 2018, 48(3): 694-703.
[14]	林金花, 王延杰, 王璐, 姚禹. 全局相机姿态优化下的快速表面重建[J]. 吉林大学学报(工学版), 2018, 48(3): 909-918.
[15]	耿庆田, 于繁华, 王宇婷, 高琦坤. 基于特征融合的车型检测新算法[J]. 吉林大学学报(工学版), 2018, 48(3): 929-935.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed