温室果蔬采摘机器人视觉信息获取方法及样机系统研究
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摘要
果蔬采摘机器人作业于温室非结构环境中,在多变的光线条件下,避让植株茎叶准确定位目标,完成果实无损伤抓取与切割。果蔬采摘信息获取是采摘机器人研究的难点与关键,根据果实与背景颜色差异,采摘对象可分为显著色差系果蔬与近色系果蔬,本文分别选取草莓和小型西瓜作为两类果蔬的典型代表,研究了采摘对象视觉信息获取方法,构建了果蔬采摘机器人系统。应用机器视觉、图像处理、光谱分析等技术,探索了果实识别、空间匹配及三维坐标定位方法,研制了采摘机器人模块化系统,并进行了温室采摘作业试验。主要研究内容和成果如下：
(1)研究了基于颜色和形态特征的草莓采摘信息获取方法。根据果实与背景在4种常用色彩空间下的颜色分布特征,选择色差最为显著的R、G通道进行图像分割,通过轮廓补偿法补全果萼遮挡区域,获得完整果实区域图像。利用图像蒙版滤除近色系背景干扰,提取非全熟果实的青色区域,完成草莓成熟度判定。根据采摘机器人末端执行器结构与草莓果梗空间位姿特征,设置采摘高度线与采摘点疑似矩形兴趣区,提取了采摘点图像坐标。对草莓果实采摘信息获取方法进行试验,结果表明草莓识别成功率为94.2%,采摘点定位准确率为93.0%。
(2)研究了基于近红外图像的小型西瓜采摘信息获取方法。通过比较立体种植模式下小型西瓜果实、茎、叶片的光谱反射特性差异,选择在850nm附近波段下采集西瓜近红外灰度图像。根据果实与背景灰度分布特征,利用改进的Otsu算法完成了图像分割。采用“米”字型匹配模板识别获得“浓缩版”西瓜果实区域,有效降低阈值分割后果实粘连与小面积干扰影响。根据果实与果梗的空间姿态及相对位置特征,采用分块定位法获得切割点图像坐标。对小型西瓜采摘信息获取方法进行试验,结果表明：不同光照条件下,小型西瓜平均识别成功率为86%,采摘点与切割点定位准确率分别为93.0%和88.4%,为近色系果蔬采摘信息获取提供了一种技术思路。
(3)研究了基于双层约束的采摘点空间匹配策略,探索了基于双目立体视觉的采摘点三维信息获取方法。根据草莓形态特征,利用果实区域与采摘点唯一对应关系,提出了基于全局特征与关系特征的草莓区域匹配方法,完成采摘点初次遴选；利用基于极线约束的采摘点空间匹配方法,完成匹配对象二次遴选。搭建了交叉式双目立体视觉硬件系统,完成了摄像机内、外部参数及手眼相对位置参数标定,建立了图像坐标系、摄像机坐标系及机械臂坐标系间的相互转换关系,获得了草莓采摘点三维坐标计算流程。
(4)搭建了采摘机器人模块化样机系统,该机器人主要由双目立体视觉系统、机械手系统、中央控制器、导航行走平台、能源系统及其他附件组成。通过归一化色差2r-g-b分割图像并获取了导航线偏移信息,对四自由度关节型机械臂进行运动学逆解分析,获得目标位姿下各关节旋转角度参数。规划了机器人采摘动作流程,对采摘机器人进行了温室作业试验,机器人采摘成功率为86%,单次采摘循环耗时28s,机器人各功能模块运行良好,能够较好的适应温室作业环境。
The fruit and vegetable harvesting robot worked in unstructured environment of greenhouse. Under the condition of complex light, the robot should position the target accurately and avoid touching the stem and leaf. And then the fruit would be grasped and cut nondestractively. Information acquisition of fruit was the difficulty and key of harvesting robot. According to the corlor difference between target and background, the study object could be classified into similar-color fruit and different-corlor fruit. Two representative fruits——strawberry and mini-watermelon were selected as study objects, and the havesting information acquisition of fruit was studied, and a fruit havesting robot system was developed. The techniques of machine vision, image processing, spectral analysis were applied to solve fruit recognition, spatial matching and three-dimensional coordinate location. At last, a modular prototype of havesting robot system was developed, and a trail of the robot was done. The main research contents and conclusions were as follows:
(1) The information acqusition method of havesting strawberry based on color and morphology was studied. Firstly, according to the color distribution characteristics of fruit and background in four common color spaces, color components (R and G) were selected to image segmentation. Secondly, the region occluded by fruit calyx was inpainted through contour compensation, and the whole fruit region was recognized. Thirdly, the near-color background interference was filtered by image mask processing, and green region of immature fruit was extracted to judge the strawberry immaturity level. Then, according to the structure of robot end-effector and the spatial pose characteristics of strawberry stem, the picking line and rectangle region of interest were set to extract the image coordinate of picking point. At last, the experiment resut showed that the correct recognition rate of fruit was94.2%, as well as the rates of the picking point was93.0%.
(2) The information acqusition method of havesting mini-watermelon based on near infrared image was studied. Firstly, the spectral characterisics of mini-watermelon's fruit, stem and leaf were compared, and optimal wavebands near850nm were selected to capture near-infrared gray image. Secondly, According to the characteristics of fruit and backgroud's gray pixel distribution, an improved Otsu algorithm was developed to segment image. Thirdly, a matching template likes as circle was proposed to detect fruit region concentrated, and the region adhesion and small area interference were reduced effectively. Then, according to the characteristics of spatial pose and relative position of the fruit and stem, the image coordinate of cutting point was located by "block-location method". Finally, a trail was done to test the algorithm of acqusiting mini-watermelon's harvesting information, and the result show that the average correct recognition rate of fruit was86%under different illumation condition, as well as the rates of the picking point and the cutting point were93.0%and88.4%respectively. Meanwhile it provides a new technical idea for acqusiting the infromation of harvesting similar-color fruit.
(3) The spatial matching strategy of picking point based on double layers constraints was studied and the three-dimensional coordinate location of picking point was explored. Firstly, according to strawberry's morphology features and the corresponding relation between fruit region and picking point, the picking points were selected preliminary by a region matching method based on global features and relationship features. Secondly, the picking points were determinad by epipolar geometry constraint. Thirdly, the hardware system of binocular stereovision was developed, and then the intrinsic parameters and external parameters of camera and the hand-eye parameters were calibrated. After coordinate transformation model of the image, camera and manipulator was developed, the flow of locating picking point's three-dimensional coordinate was obtained.
(4) A modular prototype of havesting robot system was developed. The robot was constructed of binocular stereovision system, manipulator system, central controller, self-guided moving platform, battery system, and other appendix. Firstly, the image was segmented by normalized color difference (2r-g-b), and then the guide line was extracted. Secondly, the inverse kinematics of4-freedom joint type manipulator was analysised, and then the rotate angle of every joint at target position was obtained. Thirdly, the flow of the robot picking operation was planned, and a trail to test the robot performance was done, and the result show that the success rate of picking fruit was86%, and the execution time of a harvesting cycle was28s. Every fuctional modular of the robot run effectively and well adept to the working environment in greenhouse.
(1)研究了基于颜色和形态特征的草莓采摘信息获取方法。根据果实与背景在4种常用色彩空间下的颜色分布特征,选择色差最为显著的R、G通道进行图像分割,通过轮廓补偿法补全果萼遮挡区域,获得完整果实区域图像。利用图像蒙版滤除近色系背景干扰,提取非全熟果实的青色区域,完成草莓成熟度判定。根据采摘机器人末端执行器结构与草莓果梗空间位姿特征,设置采摘高度线与采摘点疑似矩形兴趣区,提取了采摘点图像坐标。对草莓果实采摘信息获取方法进行试验,结果表明草莓识别成功率为94.2%,采摘点定位准确率为93.0%。
(2)研究了基于近红外图像的小型西瓜采摘信息获取方法。通过比较立体种植模式下小型西瓜果实、茎、叶片的光谱反射特性差异,选择在850nm附近波段下采集西瓜近红外灰度图像。根据果实与背景灰度分布特征,利用改进的Otsu算法完成了图像分割。采用“米”字型匹配模板识别获得“浓缩版”西瓜果实区域,有效降低阈值分割后果实粘连与小面积干扰影响。根据果实与果梗的空间姿态及相对位置特征,采用分块定位法获得切割点图像坐标。对小型西瓜采摘信息获取方法进行试验,结果表明：不同光照条件下,小型西瓜平均识别成功率为86%,采摘点与切割点定位准确率分别为93.0%和88.4%,为近色系果蔬采摘信息获取提供了一种技术思路。
(3)研究了基于双层约束的采摘点空间匹配策略,探索了基于双目立体视觉的采摘点三维信息获取方法。根据草莓形态特征,利用果实区域与采摘点唯一对应关系,提出了基于全局特征与关系特征的草莓区域匹配方法,完成采摘点初次遴选；利用基于极线约束的采摘点空间匹配方法,完成匹配对象二次遴选。搭建了交叉式双目立体视觉硬件系统,完成了摄像机内、外部参数及手眼相对位置参数标定,建立了图像坐标系、摄像机坐标系及机械臂坐标系间的相互转换关系,获得了草莓采摘点三维坐标计算流程。
(4)搭建了采摘机器人模块化样机系统,该机器人主要由双目立体视觉系统、机械手系统、中央控制器、导航行走平台、能源系统及其他附件组成。通过归一化色差2r-g-b分割图像并获取了导航线偏移信息,对四自由度关节型机械臂进行运动学逆解分析,获得目标位姿下各关节旋转角度参数。规划了机器人采摘动作流程,对采摘机器人进行了温室作业试验,机器人采摘成功率为86%,单次采摘循环耗时28s,机器人各功能模块运行良好,能够较好的适应温室作业环境。
The fruit and vegetable harvesting robot worked in unstructured environment of greenhouse. Under the condition of complex light, the robot should position the target accurately and avoid touching the stem and leaf. And then the fruit would be grasped and cut nondestractively. Information acquisition of fruit was the difficulty and key of harvesting robot. According to the corlor difference between target and background, the study object could be classified into similar-color fruit and different-corlor fruit. Two representative fruits——strawberry and mini-watermelon were selected as study objects, and the havesting information acquisition of fruit was studied, and a fruit havesting robot system was developed. The techniques of machine vision, image processing, spectral analysis were applied to solve fruit recognition, spatial matching and three-dimensional coordinate location. At last, a modular prototype of havesting robot system was developed, and a trail of the robot was done. The main research contents and conclusions were as follows:
(1) The information acqusition method of havesting strawberry based on color and morphology was studied. Firstly, according to the color distribution characteristics of fruit and background in four common color spaces, color components (R and G) were selected to image segmentation. Secondly, the region occluded by fruit calyx was inpainted through contour compensation, and the whole fruit region was recognized. Thirdly, the near-color background interference was filtered by image mask processing, and green region of immature fruit was extracted to judge the strawberry immaturity level. Then, according to the structure of robot end-effector and the spatial pose characteristics of strawberry stem, the picking line and rectangle region of interest were set to extract the image coordinate of picking point. At last, the experiment resut showed that the correct recognition rate of fruit was94.2%, as well as the rates of the picking point was93.0%.
(2) The information acqusition method of havesting mini-watermelon based on near infrared image was studied. Firstly, the spectral characterisics of mini-watermelon's fruit, stem and leaf were compared, and optimal wavebands near850nm were selected to capture near-infrared gray image. Secondly, According to the characteristics of fruit and backgroud's gray pixel distribution, an improved Otsu algorithm was developed to segment image. Thirdly, a matching template likes as circle was proposed to detect fruit region concentrated, and the region adhesion and small area interference were reduced effectively. Then, according to the characteristics of spatial pose and relative position of the fruit and stem, the image coordinate of cutting point was located by "block-location method". Finally, a trail was done to test the algorithm of acqusiting mini-watermelon's harvesting information, and the result show that the average correct recognition rate of fruit was86%under different illumation condition, as well as the rates of the picking point and the cutting point were93.0%and88.4%respectively. Meanwhile it provides a new technical idea for acqusiting the infromation of harvesting similar-color fruit.
(3) The spatial matching strategy of picking point based on double layers constraints was studied and the three-dimensional coordinate location of picking point was explored. Firstly, according to strawberry's morphology features and the corresponding relation between fruit region and picking point, the picking points were selected preliminary by a region matching method based on global features and relationship features. Secondly, the picking points were determinad by epipolar geometry constraint. Thirdly, the hardware system of binocular stereovision was developed, and then the intrinsic parameters and external parameters of camera and the hand-eye parameters were calibrated. After coordinate transformation model of the image, camera and manipulator was developed, the flow of locating picking point's three-dimensional coordinate was obtained.
(4) A modular prototype of havesting robot system was developed. The robot was constructed of binocular stereovision system, manipulator system, central controller, self-guided moving platform, battery system, and other appendix. Firstly, the image was segmented by normalized color difference (2r-g-b), and then the guide line was extracted. Secondly, the inverse kinematics of4-freedom joint type manipulator was analysised, and then the rotate angle of every joint at target position was obtained. Thirdly, the flow of the robot picking operation was planned, and a trail to test the robot performance was done, and the result show that the success rate of picking fruit was86%, and the execution time of a harvesting cycle was28s. Every fuctional modular of the robot run effectively and well adept to the working environment in greenhouse.
引文
[1]纪晓睿.浅议机电一体化的发展趋势.现代农业,2007,(5)：83.
[2]M W Hannan, T F Burks, D M Bulanon. A real-time machine vision algorithm for robotic citrus harvesting. Minneapolis, Minnesota:AS ABE Annual International Meeting,2007. AS ABE Paper NO: 073125.
[3]Radnaabazar Chinchuluun, Won Suk Lee. Citrus yield mapping system in natural outdoor scenes using the watershed transform. Portland, Oregon:ASABE Annual International Meeting,2006. ASABE Paper NO:063010.
[4]Palaniappan Annamalai, Won Suk Lee. Citrus yield mapping system using machine vision. Las Vegas, Nevada, USA:ASAE Annual International Meeting,2003. ASAE Paper NO:031002.
[5]D M Bulanon, T Kataoka, S Zhang, et al. Optimal thresholding for the automatic recognition of apple fruits. Sacramento, California, USA:ASAE Annual International Meeting,2001. ASAE Paper NO: 01-3133.
[6]司永胜,乔军,刘刚,等.苹果采摘机器人果实识别与定位方法.农业机械学报,2010,41(9)：148-153.
[7]赵金英,张铁中,杨丽,等.西红柿采摘机器人视觉系统的目标提取.农业机械学报,2006,37(10)：200-203.
[8]Yoshinori Gejima, Masateru Nagata, Hiyoshi Kenji. Judging of tomato maturity by utilizing a low-resolution RGB color system. Ottawa, Ontario, Canada:ASAE Annual International Meeting,2004.
ASAE Paper NO:046122.
[9]谢志勇,张铁中.基于RGB彩色模型的草莓图像色调分割算法.中国农业大学学报,2006,11(1)：84-86.
[10]Huanyu Jiang, Yongshi Peng, Chuan Shen, et al. Study of area-based stereovision method for locating tomato in greenhouse. Providence, Rhode Island:ASABE Annual International Meeting.2008, ASABE Paper NO:084883.
[11]赵晓霞,张铁中,陈兵旗.自然环境下桃子图像分割算法.农机化研究.2009(2)：107-111.
[12]Bin Li, Maohua Wang, Ning Wang. Development of a real-time fruit recognition system for pineapple harvesting robots. Pittsburgh, Pennsylvania:ASABE Annual International Meeting.2010, ASABE Paper NO:1009510.
[13]刘长林,张铁中,杨丽,等.茄子收获机器人视觉系统图像识别方法.农业机械学报,2008,39(11)：216-219.
[14]王勇,沈明霞,姬长英.基于色差信息的田间成熟棉花识别.浙江农业学报,2007,19(5)：385-388.
[15]Liyong Qi, Qinghua Yang, Guanjun Bao, et al. A dynamic threshold segmentation algorithm for cucumber identification in greenhouse. Tianjin, China:Image and Signal Processing,2009. IEEE Paper NO:1-4.
[16]丁亚兰,耿楠,周全程.基于图像的猕猴桃果实目标提取研究.微计算机信息,2009,25(6-3)：294-295.
[17]张建华.基于计算机视觉的硬皮甜瓜外部品质自动化分级研究：[硕士学位论文].兰州：甘肃农业大学,2009.
[18]H Zhang, B Chen, L Zhang. Detection algorithm for crop multi-centerlines based on machine vision. Transactions of the ASABE,2008,51(3):1089-1097.
[19]于扬,崔天时,董桂菊.基于颜色特征与直方图阈值相结合的田间青椒图像分割算法.微型机与应用,2010,(4)：51-53.
[20]Kevin E Kane, Won Suk Lee. Multispectral imaging for in-field green citrus identification. Minneapolis, Minnesota:ASABE Annual International Meeting,2007. ASABE Paper NO:073025.
[21]Kevin E Kane, Won Suk Lee. Spectral sensing of different citrus varieties for precision agriculture. Portland, Oregon:ASABE Annual International Meeting,2006. ASABE Paper NO:061065.
[22]蔡健荣,王建黑,陈全胜,等.光谱图像技术结合SAM算法识别自然场景下的成熟柑橘.光子学报,2009,38(12)：3171-3175.
[23]Seiichi Arima, Naoshi Kondo, Yasunori Shibano. Studies on cucumber harvesting robot (Part 3). Japan Journal of Agrieultural Maehinery,1995,57(1):51-58.
[24]E J van Henten, J Hemming, B A J Van Tuijl, et al. An autonomous robot for harvesting cucumbers in greenhouses. Autonomous Robots,2002,13(3):241-258,
[25]袁挺,许晨光,任永新,等.基于近红外图像的温室环境下黄瓜果实信息获取.光谱学与光谱分析,2009,29(8)：2054-2058.
[26]H Okamoto, T Murata, T Kataoka, et al. Weed detection using hyperspectral imaging. Kyoto, Japan: ASAE Annual International Meeting,2004. ASAE Paper NO:701P1004.
[27]E S Staab, D C Slaughter, Y Zhang, et al. Hyperspectral imaging system for precision weed control in processing tomato. Reno, Nevada:ASABE Annual International Meeting,2009. ASABE Paper NO: 096635.
[28]O Safren, V Alchanatis, V Ostrovsky, et al. Detection of green apples in hyperspectral images of apple-tree foliage using machine vision. Transactions of the ASABE,2007,50(6):2303-2313.
[29]Diwan P Ariana, Renfu Lu. Wavebands selection for a hyperspectral reflectance and transmittance imaging system for quality evaluation of pickling cucumbers. Reno, Nevada:ASABE Annual International Meeting,2009. ASABE Paper NO:096872.
[30]V K Chong, N Kondo, K,Ninomiya, et al. Comparison on eggplant fruit grading between nir-color camera and color camera. Kyoto, Japan:Proceedings of the international conference on automation technology for off-road equipment, ATOE,2004:387-393.
[31]Hoyoung Lee, Soo Hyun Park, Hak Sung Kim, et al. Development of band-selective 3CCD camera to detect surface defect on fruit. Pittsburgh, Pennsylvania:2010 ASABE Annual International Meeting, 2010. ASABE Paper NO:1009519.
[32]Chari V K Kandala, Jaya Sundaram, G Konda Naganathan, et al. Non destructive determination of peanut moisture content using near infrared reflectance spectroscopy. Pittsburgh, PA:ASABE Annual International Meeting,2010. ASABE Paper NO:1008520.
[33]Fen Dai, Tiansheng Hong, Kun Zhang, et al. Comparison of modeling methods in rapid estimation of sugar content, of apple based on near infrared spectrum. Pittsburgh, Pennsylvania:2010 ASABE Annual International Meeting,2010. ASABE Paper NO:1008620.
[34]Yankun Peng, Hui Huang, Wei Wang, et al. Prediction of chlorophyll content in wheat leaves using hyperspectral images. Pittsburgh, Pennsylvania:2010 ASABE Annual International Meeting,2010. ASABE Paper NO:1009492.
[35]张亚静,李民赞,乔军,等.一种基于图像特征和神经网络的苹果图像分割算法.光学学报,2009,28(11)：2104-2108.
[36]王津京,赵德安,姬伟,等.采摘机器人基于支持向量机苹果识别方法.农业机械学报,2009,40(1)：148-151.
[37]王津京,赵德安,姬伟,等.基于BP神经网络的苹果图像分割算法.农机化研究,2008(11)：19-21.
[38]王津京.基于支持向量机苹果采摘机器人视觉系统的研究：[硕士学位论文].镇江：江苏大学,2009.
[39]Tomowo Shiigi, Naoshi Kondo, Mitsutaka Kurita, et al. Strawberry harvesting robot for fruits grown on table top culture. Providence, Rhode Island:ASABE Annual International Meeting,2008. ASABE Paper NO:084046.
[40]张铁中,周天娟.草莓采摘机器人的研究：Ⅰ.基于BP神经网络的草莓图像分割.中国农业大学学报,2004,9(4)：65-68.
[41]谢志勇,张铁中,赵金英.基于Hough变换的成熟草莓识别技术.农业机械学报,2007,38(3)：106-109.
[42]C F Chien, T T Lin. Leaf area measurement of selected vegetable seedlings using elliptical Hough transform. Transactions of the ASAE,2002,45(5):1669-1677.
[43]C F Chien, T T Lin. Non-destructive growth measurement of selected vegetable seedlings using orthogonal images. Transactions of the ASAE,2005,48(5):1953-1961.
[44]Y Kim, P P Ling. Machine vision guided sensor positioning system for leaf temperature assessment. Transactions of the ASAE. American Society of Agricultural Engineers,2000,44(6):1941-1947.
[45]K Nishiwaki, T Togashi, K Amaha, et al. Estimate crop position using template matching in rice production. Sacramento, California, USA:ASAE Annual International Meeting,2001. ASAE Paper NO: 01-3103.
[46]郭峰,曹其新,谢国俊,等.基于OHTA颜色空间的瓜果轮廓提取方法.农业机械学报,2006,36(11)：113-116.
[47]Naoshi Kondo, Kazunori Ninomiya, Rajendra Peter, et al. Development of multi-product grading system. Tampa, Florida:ASAE Annual International Meeting,2005. ASAE Paper NO: 056094.
[48]Jun Qiao, Akira Sasao, Sakae Shibusawa, et al. Mobile fruit grading robot -concept and prototype. Ottawa, Ontario, Canada ASAE/CSAE Annual International Meeting,2004. ASAE/CSAE Paper NO: 043086.
[49]N Kondo, J Qiao, S Shibusawa, et al. Mobile type sweet pepper grading robot. Kyoto, Japan: Proceedings of the international conference on automation technology for off-road equipment, ATOE, 2004:480-486.
[50]Murali Regunathan, Won Suk Lee, et al. Citrus fruit identification and size determination using machine vision and ultrasonic sensors. Tampa, Florida:ASAE Annual International Meeting,2005. ASAE Paper NO:053017.
[51]Jiangbo Li, Xiuqin Rao, Yibin Ying, et al. Study on detection method for cut roses based on machine vision. Pittsburgh, Pennsylvania:AS ABE Annual International Meeting,2010. AS ABE Paper NO:1008777.
[52]Y Ying, H Jing, Y Tao, et al. Detecting stem and shape of pears using Fourier transformation and an artificial neural network. Transactions of the ASAE,2003,46(1):157-162.
[53]俞高红,骆健民,赵匀.基于序贯扫描算法的区域标记技术与蘑菇图像分割方法.农业工程学报,2006,22(4)：139-142.
[54]Huanyu Jiang, Yongshi Peng, Yibin Ying. Measurement of 3-D locations of ripe tomato by binocular stereo vision for tomato harvesting. Providence, Rhode Island:ASABE Annual International Meeting,2008. ASABE Paper NO:084880.
[55]蒋焕煜,彭永石,应义斌.双目立体视觉技术在果蔬采摘机器人中的应用.江苏大学学报：自然科学版,2008,29(5)：377-380.
[56]L Yang, J Dickinson, Q M J Wu, et al. A fruit recognition method for automatic harvesting. Mechatronics and Machine Vision in Practice,2007. M2VIP 2007.14th International Conference on. IEEE,2007:152-157.
[57]孙学岩.采摘机器人单摄像机测距系统设计与实验.传感器与微系统,2010(3)：80-81.
[58]Francisco Rovira-Mas, Qin Zhang, John F Reid. Stereo 3D crop maps from aerial images. Las Vegas, Nevada, USA:ASAE Annual International Meeting,2003. ASAE Paper NO:031003.
[59]F Rovira-Mds, J F Reid.3D density and density maps for stereo-based navigation. Kyoto, Japan: Proceedings of the international conference on automation technology for off-road equipment, ATOE, 2004:24-35.
[60]D M Bulanon, T Kataoka, H Okamoto, et al. Determining the 3-D location of the apple fruit during harvest. Kyoto, Japan:Proceedings of the international conference on automation technology for off-road equipment, ATOE,2004:91-97.
[61]Teruo Takahashi, Shuhuai Zhang, Hiroshi Fukuchi. Measurement of 3-D locations of fruit by binocular stereo vision for apple harvesting in an orchard. Chicago, Illinois, USA:Annual International Meeting/CIGRXVth World Congress,2002. ASAE Paper NO:021102.
[62]P Tarrio, A M Bernardos, J R Casar, et al. A harvesting robot for small fruit in bunches based on 3-D stereoscopic vision. Orlando, Florida USA:Computers in Agriculture and Nature Resources, ASABE 4th World Congress Conference.2006:270-275.
[63]Mitsuji Monta, Kazuhiko Namba, Naoshi Kondo. Three dimensional sensing system using laser scanner. Ottawa, Ontario, Canada:ASAE/CSAE Annual International Meeting,2004. ASAE Paper NO: 041158.
[64]张凯良,杨丽,张铁中.草莓采摘位置机器视觉与激光辅助定位方法.农业机械学报,2010,41(4)：151-156.
[65]K Tanigaki, T Fujiura, A Akase, et al. Cherry-harvesting robot. Computers and Electronics in Agriculture,2008,63(1):65-72.
[66]刘兆祥,刘刚,乔军.苹果采摘机器人三维视觉传感器设计.农业机械学报,2010,41(2)：171-175.
[67]S Han, M A Dickson, B Ni, et al. A robust procedure to obtain a guidance directrix for vision-based vehicle guidance systems. Chicago, Illinois, USA:Proceedings of the international conference on automation technology for off-road equipment, ATOE,2002:317-326.
[68]J B Gerrish, B W Fehr, G R Van Ee, et al. Self-steering tractor guided by computer-vision. Applied Engineering in Agriculture,1997,13(5):559-563.
[69]T Bakker, H Wouters, K Van Asselt, et al. A vision Based row detection system for sugar beet. Computers and Electronics in Agriculture,2008,60(1):87-95.
[70]任永新,谭豫之,杨会华,等.基于模糊控制的黄瓜采摘机器人视觉导航.江苏大学学报：自然科学版,2009,30(4)：343-346.
[71]任永新,李伟,陈晓,等.非结构环境下基于机器视觉的机器人路径跟踪方法.北京工业大学学报,2008,34(10)：1021-1025.
[72]燕学智,王树勋,马中胜,等.基于超声红外定位导航研制自动引导车辆系统.吉林大学学报：工学版,2006,36(2)：242-246.
[73]Naoshi Kondo, Kazunori Ninomiya, Shigehiko ·Hayashi, et al. A new challenge of robot for harvesting strawberry grown on table top culture. Tampa, Florida:ASAE Annual International Meeting, 2005. ASAE Paper NO:053138.
[74]K H Lee, R Ehsani, J K Schueller. Forward movement synchronization of two vehicles in parallel using a laser scanner. Applied engineering in agriculture,2007,23(6):827-836.
[75]Vijay Subramanian, Thomas F Burks. Autonomous path navigation in citrus groves using machine vision and laser radar. Tampa, Florida:ASAE Annual International Meeting,2005. ASAE Paper NO: 051142.
[76]Peter P Ling, Reza Ehsani, K C Ting, et al. Sensing and end-effector for a robotic tomato harvester. Ottawa, Ontario, Canada:ASAE/CSAE Annual International Meeting,2004. ASAE Paper NO:043088.
[77]Naoshi Kondo, Shigemune Taniwaki, Koichi Tanihara, et al. An end-effector and manipulator control for tomato cluster harvesting robot. Minneapolis, Minnesota:ASABE Annual International Meeting,2007. ASABE Paper NO:073114.
[78]刘继展,李萍萍,李智国.番茄采摘机器人末端执行器的硬件设计.农业机械学报,2008,39(3)：109-112.
[79]J Liu, P Li, Z Li. A multi-sensory end-effector for spherical fruit harvesting robot. Jinan, China: Automation and Logistics,2007 IEEE International Conference on. IEEE,2007:258-262.
[80]B S Lee, U A Rosa. Development of a canopy volume reduction technique for easy assessment and harvesting of Valencia citrus fruits. Transactions of the ASABE,2006,49(6):1695-1703.
[81]Brian Siu-him Lee, Uriel A. Rosa, Kabilesh Cheetancheri. End-effector for automated citrus harvesting. Portland, Oregon:ASABE Annual International Meeting,2006. ASABE Paper NO:061143.
[82]Duke M Bulanon, Takashi Kataoka, Hiroshi Okamoto, et al. Feedback control of manipulator using machine vision for robotic apple harvesting. Tampa, Florida:ASAE Annual International Meeting,2005. ASAE Paper NO:053114.
[83]杨庆华,金寅德,钱少明,等.基于气动柔性驱动器的苹果采摘末端执行器研究.农业机械学报,2010,41(9)：154-158.
[84]钱少明,杨庆华,王志恒,等.黄瓜抓持特性与末端采摘执行器研究.农业工程学报,2010,26(7)：107-112.
[85]刘长林,张铁中,杨丽.茄子采摘机器人末端执行器设计.农机化研究,2008(12)：62-64.
[86]E J Van Henten, B A J Van Tuijl, G J Hoogakker, et al. An autonomous robot for de-leafing cucumber plants grown in a high-wire cultivation system. Biosystems engineering,2006,94(3): 317-323.
[87]E J Van Henten, B A J Van Tuijl, J Hemming, et al. Field test of an autonomous cucumber picking robot. Biosystems Engineering,2003,86(3):305-313.
[88]Xiuying Tang, Tiezhong Zhang, Ling Liu, et al. A new robot system for harvesting cucumber, reno, nevada:asabe annual international meeting,2009. ASABE Paper NO:096463.
[89]Seiichi Arima, Naoshi Kondo, Mitsuji Monta. Strawberry harvesting robot on table-top culture. Ottawa, Ontario, Canada:ASAE/CSAE Annual International Meeting,2004. ASAE Paper NO:043089.
[90]S Hayashi, K Shigematsu, S Yamamoto, et al. Evaluation of a strawberry-harvesting robot in a field test. Biosystems Engineering,2010,105(2):160-171.
[91]N Kondo, Y Nishitsuji, P P Ling, et al. Visual feedback guided robotic cherry tomato harvesting. Transactions of the ASAE,1996,39(6):2331-2338.
[92]刘长林,张铁中,杨丽.果蔬采摘机器人进展.安徽农业科学,2008,36(13)：5394-5397.
[93]R Noble, J N Reed, S Miles, et al. Influence of mushroom strains and population density on the performance of a robotic harvester. Journal of agricultural engineering research,1997,68(3):215-222.
[94]Satoru Sakai, Koichi Osuka, Mikio Umeda. USE of a heavy material handling agricultural robot for harvesting watermelons. Kyoto, Japan:Proceedings of the international conference on automation technology for off-road equipment, ATOE,2004:321-331.
[95]陆怀民.林木球果采集机器人设计与试验,农业机械学报,2001,32(6)：52-54.
[96]赵根,沈毅,陈丽萍,等.草莓的立体栽培模式.农业科技通讯,2012,(12)：220-,222.
[97]N Otsu. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics,1979, SMC-9(1):62-66.
[98]刘波,方俊永,刘学,等.基于成像光谱技术的作物杂草识别研究.光谱学与光谱分析,2010,30(7)：1830-1833.
[99]H H Muhammed. Hyperspectral crop reflectance data for characterising and estimating fungal disease severity in Wheat. Biosystems Engineering,2005,91(1):9-20.
[100]朱大洲,王坤,周光华,等.单粒大豆的近红外光谱特征及品种鉴别研究.光谱学与光谱分析,2010,30(12)：3217-3221.
[101]张学兵,刘裕岭,夏义华,等.西瓜立体栽培技术.上海农业科技,2002,(4)：63.
[102]李文信.西瓜立体栽培优势及技术要点.中国西瓜甜瓜,2001,(3)：19.
[103]李民赞.光谱分析技术及其应用.北京：科学出版社,2006,176-180.
[104]李实英,杨高波.特征提取与图像处理(第二版).北京：电子工业出版社,2010,153-154.
[105]贾云得.机器视觉.北京：科学出版社,2000,282-287.
[106]张广军.机器视觉.北京：科学出版社,2005,108-112
[107]Z Y Zhang. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334.
[108]J J More. The levenberg-marquardt algorithm:implementation and theory. Lecture Notes in Mathematics 630, New York:Springer-Verlag,1977:105-116.
[2]M W Hannan, T F Burks, D M Bulanon. A real-time machine vision algorithm for robotic citrus harvesting. Minneapolis, Minnesota:AS ABE Annual International Meeting,2007. AS ABE Paper NO: 073125.
[3]Radnaabazar Chinchuluun, Won Suk Lee. Citrus yield mapping system in natural outdoor scenes using the watershed transform. Portland, Oregon:ASABE Annual International Meeting,2006. ASABE Paper NO:063010.
[4]Palaniappan Annamalai, Won Suk Lee. Citrus yield mapping system using machine vision. Las Vegas, Nevada, USA:ASAE Annual International Meeting,2003. ASAE Paper NO:031002.
[5]D M Bulanon, T Kataoka, S Zhang, et al. Optimal thresholding for the automatic recognition of apple fruits. Sacramento, California, USA:ASAE Annual International Meeting,2001. ASAE Paper NO: 01-3133.
[6]司永胜,乔军,刘刚,等.苹果采摘机器人果实识别与定位方法.农业机械学报,2010,41(9)：148-153.
[7]赵金英,张铁中,杨丽,等.西红柿采摘机器人视觉系统的目标提取.农业机械学报,2006,37(10)：200-203.
[8]Yoshinori Gejima, Masateru Nagata, Hiyoshi Kenji. Judging of tomato maturity by utilizing a low-resolution RGB color system. Ottawa, Ontario, Canada:ASAE Annual International Meeting,2004.
ASAE Paper NO:046122.
[9]谢志勇,张铁中.基于RGB彩色模型的草莓图像色调分割算法.中国农业大学学报,2006,11(1)：84-86.
[10]Huanyu Jiang, Yongshi Peng, Chuan Shen, et al. Study of area-based stereovision method for locating tomato in greenhouse. Providence, Rhode Island:ASABE Annual International Meeting.2008, ASABE Paper NO:084883.
[11]赵晓霞,张铁中,陈兵旗.自然环境下桃子图像分割算法.农机化研究.2009(2)：107-111.
[12]Bin Li, Maohua Wang, Ning Wang. Development of a real-time fruit recognition system for pineapple harvesting robots. Pittsburgh, Pennsylvania:ASABE Annual International Meeting.2010, ASABE Paper NO:1009510.
[13]刘长林,张铁中,杨丽,等.茄子收获机器人视觉系统图像识别方法.农业机械学报,2008,39(11)：216-219.
[14]王勇,沈明霞,姬长英.基于色差信息的田间成熟棉花识别.浙江农业学报,2007,19(5)：385-388.
[15]Liyong Qi, Qinghua Yang, Guanjun Bao, et al. A dynamic threshold segmentation algorithm for cucumber identification in greenhouse. Tianjin, China:Image and Signal Processing,2009. IEEE Paper NO:1-4.
[16]丁亚兰,耿楠,周全程.基于图像的猕猴桃果实目标提取研究.微计算机信息,2009,25(6-3)：294-295.
[17]张建华.基于计算机视觉的硬皮甜瓜外部品质自动化分级研究：[硕士学位论文].兰州：甘肃农业大学,2009.
[18]H Zhang, B Chen, L Zhang. Detection algorithm for crop multi-centerlines based on machine vision. Transactions of the ASABE,2008,51(3):1089-1097.
[19]于扬,崔天时,董桂菊.基于颜色特征与直方图阈值相结合的田间青椒图像分割算法.微型机与应用,2010,(4)：51-53.
[20]Kevin E Kane, Won Suk Lee. Multispectral imaging for in-field green citrus identification. Minneapolis, Minnesota:ASABE Annual International Meeting,2007. ASABE Paper NO:073025.
[21]Kevin E Kane, Won Suk Lee. Spectral sensing of different citrus varieties for precision agriculture. Portland, Oregon:ASABE Annual International Meeting,2006. ASABE Paper NO:061065.
[22]蔡健荣,王建黑,陈全胜,等.光谱图像技术结合SAM算法识别自然场景下的成熟柑橘.光子学报,2009,38(12)：3171-3175.
[23]Seiichi Arima, Naoshi Kondo, Yasunori Shibano. Studies on cucumber harvesting robot (Part 3). Japan Journal of Agrieultural Maehinery,1995,57(1):51-58.
[24]E J van Henten, J Hemming, B A J Van Tuijl, et al. An autonomous robot for harvesting cucumbers in greenhouses. Autonomous Robots,2002,13(3):241-258,
[25]袁挺,许晨光,任永新,等.基于近红外图像的温室环境下黄瓜果实信息获取.光谱学与光谱分析,2009,29(8)：2054-2058.
[26]H Okamoto, T Murata, T Kataoka, et al. Weed detection using hyperspectral imaging. Kyoto, Japan: ASAE Annual International Meeting,2004. ASAE Paper NO:701P1004.
[27]E S Staab, D C Slaughter, Y Zhang, et al. Hyperspectral imaging system for precision weed control in processing tomato. Reno, Nevada:ASABE Annual International Meeting,2009. ASABE Paper NO: 096635.
[28]O Safren, V Alchanatis, V Ostrovsky, et al. Detection of green apples in hyperspectral images of apple-tree foliage using machine vision. Transactions of the ASABE,2007,50(6):2303-2313.
[29]Diwan P Ariana, Renfu Lu. Wavebands selection for a hyperspectral reflectance and transmittance imaging system for quality evaluation of pickling cucumbers. Reno, Nevada:ASABE Annual International Meeting,2009. ASABE Paper NO:096872.
[30]V K Chong, N Kondo, K,Ninomiya, et al. Comparison on eggplant fruit grading between nir-color camera and color camera. Kyoto, Japan:Proceedings of the international conference on automation technology for off-road equipment, ATOE,2004:387-393.
[31]Hoyoung Lee, Soo Hyun Park, Hak Sung Kim, et al. Development of band-selective 3CCD camera to detect surface defect on fruit. Pittsburgh, Pennsylvania:2010 ASABE Annual International Meeting, 2010. ASABE Paper NO:1009519.
[32]Chari V K Kandala, Jaya Sundaram, G Konda Naganathan, et al. Non destructive determination of peanut moisture content using near infrared reflectance spectroscopy. Pittsburgh, PA:ASABE Annual International Meeting,2010. ASABE Paper NO:1008520.
[33]Fen Dai, Tiansheng Hong, Kun Zhang, et al. Comparison of modeling methods in rapid estimation of sugar content, of apple based on near infrared spectrum. Pittsburgh, Pennsylvania:2010 ASABE Annual International Meeting,2010. ASABE Paper NO:1008620.
[34]Yankun Peng, Hui Huang, Wei Wang, et al. Prediction of chlorophyll content in wheat leaves using hyperspectral images. Pittsburgh, Pennsylvania:2010 ASABE Annual International Meeting,2010. ASABE Paper NO:1009492.
[35]张亚静,李民赞,乔军,等.一种基于图像特征和神经网络的苹果图像分割算法.光学学报,2009,28(11)：2104-2108.
[36]王津京,赵德安,姬伟,等.采摘机器人基于支持向量机苹果识别方法.农业机械学报,2009,40(1)：148-151.
[37]王津京,赵德安,姬伟,等.基于BP神经网络的苹果图像分割算法.农机化研究,2008(11)：19-21.
[38]王津京.基于支持向量机苹果采摘机器人视觉系统的研究：[硕士学位论文].镇江：江苏大学,2009.
[39]Tomowo Shiigi, Naoshi Kondo, Mitsutaka Kurita, et al. Strawberry harvesting robot for fruits grown on table top culture. Providence, Rhode Island:ASABE Annual International Meeting,2008. ASABE Paper NO:084046.
[40]张铁中,周天娟.草莓采摘机器人的研究：Ⅰ.基于BP神经网络的草莓图像分割.中国农业大学学报,2004,9(4)：65-68.
[41]谢志勇,张铁中,赵金英.基于Hough变换的成熟草莓识别技术.农业机械学报,2007,38(3)：106-109.
[42]C F Chien, T T Lin. Leaf area measurement of selected vegetable seedlings using elliptical Hough transform. Transactions of the ASAE,2002,45(5):1669-1677.
[43]C F Chien, T T Lin. Non-destructive growth measurement of selected vegetable seedlings using orthogonal images. Transactions of the ASAE,2005,48(5):1953-1961.
[44]Y Kim, P P Ling. Machine vision guided sensor positioning system for leaf temperature assessment. Transactions of the ASAE. American Society of Agricultural Engineers,2000,44(6):1941-1947.
[45]K Nishiwaki, T Togashi, K Amaha, et al. Estimate crop position using template matching in rice production. Sacramento, California, USA:ASAE Annual International Meeting,2001. ASAE Paper NO: 01-3103.
[46]郭峰,曹其新,谢国俊,等.基于OHTA颜色空间的瓜果轮廓提取方法.农业机械学报,2006,36(11)：113-116.
[47]Naoshi Kondo, Kazunori Ninomiya, Rajendra Peter, et al. Development of multi-product grading system. Tampa, Florida:ASAE Annual International Meeting,2005. ASAE Paper NO: 056094.
[48]Jun Qiao, Akira Sasao, Sakae Shibusawa, et al. Mobile fruit grading robot -concept and prototype. Ottawa, Ontario, Canada ASAE/CSAE Annual International Meeting,2004. ASAE/CSAE Paper NO: 043086.
[49]N Kondo, J Qiao, S Shibusawa, et al. Mobile type sweet pepper grading robot. Kyoto, Japan: Proceedings of the international conference on automation technology for off-road equipment, ATOE, 2004:480-486.
[50]Murali Regunathan, Won Suk Lee, et al. Citrus fruit identification and size determination using machine vision and ultrasonic sensors. Tampa, Florida:ASAE Annual International Meeting,2005. ASAE Paper NO:053017.
[51]Jiangbo Li, Xiuqin Rao, Yibin Ying, et al. Study on detection method for cut roses based on machine vision. Pittsburgh, Pennsylvania:AS ABE Annual International Meeting,2010. AS ABE Paper NO:1008777.
[52]Y Ying, H Jing, Y Tao, et al. Detecting stem and shape of pears using Fourier transformation and an artificial neural network. Transactions of the ASAE,2003,46(1):157-162.
[53]俞高红,骆健民,赵匀.基于序贯扫描算法的区域标记技术与蘑菇图像分割方法.农业工程学报,2006,22(4)：139-142.
[54]Huanyu Jiang, Yongshi Peng, Yibin Ying. Measurement of 3-D locations of ripe tomato by binocular stereo vision for tomato harvesting. Providence, Rhode Island:ASABE Annual International Meeting,2008. ASABE Paper NO:084880.
[55]蒋焕煜,彭永石,应义斌.双目立体视觉技术在果蔬采摘机器人中的应用.江苏大学学报：自然科学版,2008,29(5)：377-380.
[56]L Yang, J Dickinson, Q M J Wu, et al. A fruit recognition method for automatic harvesting. Mechatronics and Machine Vision in Practice,2007. M2VIP 2007.14th International Conference on. IEEE,2007:152-157.
[57]孙学岩.采摘机器人单摄像机测距系统设计与实验.传感器与微系统,2010(3)：80-81.
[58]Francisco Rovira-Mas, Qin Zhang, John F Reid. Stereo 3D crop maps from aerial images. Las Vegas, Nevada, USA:ASAE Annual International Meeting,2003. ASAE Paper NO:031003.
[59]F Rovira-Mds, J F Reid.3D density and density maps for stereo-based navigation. Kyoto, Japan: Proceedings of the international conference on automation technology for off-road equipment, ATOE, 2004:24-35.
[60]D M Bulanon, T Kataoka, H Okamoto, et al. Determining the 3-D location of the apple fruit during harvest. Kyoto, Japan:Proceedings of the international conference on automation technology for off-road equipment, ATOE,2004:91-97.
[61]Teruo Takahashi, Shuhuai Zhang, Hiroshi Fukuchi. Measurement of 3-D locations of fruit by binocular stereo vision for apple harvesting in an orchard. Chicago, Illinois, USA:Annual International Meeting/CIGRXVth World Congress,2002. ASAE Paper NO:021102.
[62]P Tarrio, A M Bernardos, J R Casar, et al. A harvesting robot for small fruit in bunches based on 3-D stereoscopic vision. Orlando, Florida USA:Computers in Agriculture and Nature Resources, ASABE 4th World Congress Conference.2006:270-275.
[63]Mitsuji Monta, Kazuhiko Namba, Naoshi Kondo. Three dimensional sensing system using laser scanner. Ottawa, Ontario, Canada:ASAE/CSAE Annual International Meeting,2004. ASAE Paper NO: 041158.
[64]张凯良,杨丽,张铁中.草莓采摘位置机器视觉与激光辅助定位方法.农业机械学报,2010,41(4)：151-156.
[65]K Tanigaki, T Fujiura, A Akase, et al. Cherry-harvesting robot. Computers and Electronics in Agriculture,2008,63(1):65-72.
[66]刘兆祥,刘刚,乔军.苹果采摘机器人三维视觉传感器设计.农业机械学报,2010,41(2)：171-175.
[67]S Han, M A Dickson, B Ni, et al. A robust procedure to obtain a guidance directrix for vision-based vehicle guidance systems. Chicago, Illinois, USA:Proceedings of the international conference on automation technology for off-road equipment, ATOE,2002:317-326.
[68]J B Gerrish, B W Fehr, G R Van Ee, et al. Self-steering tractor guided by computer-vision. Applied Engineering in Agriculture,1997,13(5):559-563.
[69]T Bakker, H Wouters, K Van Asselt, et al. A vision Based row detection system for sugar beet. Computers and Electronics in Agriculture,2008,60(1):87-95.
[70]任永新,谭豫之,杨会华,等.基于模糊控制的黄瓜采摘机器人视觉导航.江苏大学学报：自然科学版,2009,30(4)：343-346.
[71]任永新,李伟,陈晓,等.非结构环境下基于机器视觉的机器人路径跟踪方法.北京工业大学学报,2008,34(10)：1021-1025.
[72]燕学智,王树勋,马中胜,等.基于超声红外定位导航研制自动引导车辆系统.吉林大学学报：工学版,2006,36(2)：242-246.
[73]Naoshi Kondo, Kazunori Ninomiya, Shigehiko ·Hayashi, et al. A new challenge of robot for harvesting strawberry grown on table top culture. Tampa, Florida:ASAE Annual International Meeting, 2005. ASAE Paper NO:053138.
[74]K H Lee, R Ehsani, J K Schueller. Forward movement synchronization of two vehicles in parallel using a laser scanner. Applied engineering in agriculture,2007,23(6):827-836.
[75]Vijay Subramanian, Thomas F Burks. Autonomous path navigation in citrus groves using machine vision and laser radar. Tampa, Florida:ASAE Annual International Meeting,2005. ASAE Paper NO: 051142.
[76]Peter P Ling, Reza Ehsani, K C Ting, et al. Sensing and end-effector for a robotic tomato harvester. Ottawa, Ontario, Canada:ASAE/CSAE Annual International Meeting,2004. ASAE Paper NO:043088.
[77]Naoshi Kondo, Shigemune Taniwaki, Koichi Tanihara, et al. An end-effector and manipulator control for tomato cluster harvesting robot. Minneapolis, Minnesota:ASABE Annual International Meeting,2007. ASABE Paper NO:073114.
[78]刘继展,李萍萍,李智国.番茄采摘机器人末端执行器的硬件设计.农业机械学报,2008,39(3)：109-112.
[79]J Liu, P Li, Z Li. A multi-sensory end-effector for spherical fruit harvesting robot. Jinan, China: Automation and Logistics,2007 IEEE International Conference on. IEEE,2007:258-262.
[80]B S Lee, U A Rosa. Development of a canopy volume reduction technique for easy assessment and harvesting of Valencia citrus fruits. Transactions of the ASABE,2006,49(6):1695-1703.
[81]Brian Siu-him Lee, Uriel A. Rosa, Kabilesh Cheetancheri. End-effector for automated citrus harvesting. Portland, Oregon:ASABE Annual International Meeting,2006. ASABE Paper NO:061143.
[82]Duke M Bulanon, Takashi Kataoka, Hiroshi Okamoto, et al. Feedback control of manipulator using machine vision for robotic apple harvesting. Tampa, Florida:ASAE Annual International Meeting,2005. ASAE Paper NO:053114.
[83]杨庆华,金寅德,钱少明,等.基于气动柔性驱动器的苹果采摘末端执行器研究.农业机械学报,2010,41(9)：154-158.
[84]钱少明,杨庆华,王志恒,等.黄瓜抓持特性与末端采摘执行器研究.农业工程学报,2010,26(7)：107-112.
[85]刘长林,张铁中,杨丽.茄子采摘机器人末端执行器设计.农机化研究,2008(12)：62-64.
[86]E J Van Henten, B A J Van Tuijl, G J Hoogakker, et al. An autonomous robot for de-leafing cucumber plants grown in a high-wire cultivation system. Biosystems engineering,2006,94(3): 317-323.
[87]E J Van Henten, B A J Van Tuijl, J Hemming, et al. Field test of an autonomous cucumber picking robot. Biosystems Engineering,2003,86(3):305-313.
[88]Xiuying Tang, Tiezhong Zhang, Ling Liu, et al. A new robot system for harvesting cucumber, reno, nevada:asabe annual international meeting,2009. ASABE Paper NO:096463.
[89]Seiichi Arima, Naoshi Kondo, Mitsuji Monta. Strawberry harvesting robot on table-top culture. Ottawa, Ontario, Canada:ASAE/CSAE Annual International Meeting,2004. ASAE Paper NO:043089.
[90]S Hayashi, K Shigematsu, S Yamamoto, et al. Evaluation of a strawberry-harvesting robot in a field test. Biosystems Engineering,2010,105(2):160-171.
[91]N Kondo, Y Nishitsuji, P P Ling, et al. Visual feedback guided robotic cherry tomato harvesting. Transactions of the ASAE,1996,39(6):2331-2338.
[92]刘长林,张铁中,杨丽.果蔬采摘机器人进展.安徽农业科学,2008,36(13)：5394-5397.
[93]R Noble, J N Reed, S Miles, et al. Influence of mushroom strains and population density on the performance of a robotic harvester. Journal of agricultural engineering research,1997,68(3):215-222.
[94]Satoru Sakai, Koichi Osuka, Mikio Umeda. USE of a heavy material handling agricultural robot for harvesting watermelons. Kyoto, Japan:Proceedings of the international conference on automation technology for off-road equipment, ATOE,2004:321-331.
[95]陆怀民.林木球果采集机器人设计与试验,农业机械学报,2001,32(6)：52-54.
[96]赵根,沈毅,陈丽萍,等.草莓的立体栽培模式.农业科技通讯,2012,(12)：220-,222.
[97]N Otsu. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics,1979, SMC-9(1):62-66.
[98]刘波,方俊永,刘学,等.基于成像光谱技术的作物杂草识别研究.光谱学与光谱分析,2010,30(7)：1830-1833.
[99]H H Muhammed. Hyperspectral crop reflectance data for characterising and estimating fungal disease severity in Wheat. Biosystems Engineering,2005,91(1):9-20.
[100]朱大洲,王坤,周光华,等.单粒大豆的近红外光谱特征及品种鉴别研究.光谱学与光谱分析,2010,30(12)：3217-3221.
[101]张学兵,刘裕岭,夏义华,等.西瓜立体栽培技术.上海农业科技,2002,(4)：63.
[102]李文信.西瓜立体栽培优势及技术要点.中国西瓜甜瓜,2001,(3)：19.
[103]李民赞.光谱分析技术及其应用.北京：科学出版社,2006,176-180.
[104]李实英,杨高波.特征提取与图像处理(第二版).北京：电子工业出版社,2010,153-154.
[105]贾云得.机器视觉.北京：科学出版社,2000,282-287.
[106]张广军.机器视觉.北京：科学出版社,2005,108-112
[107]Z Y Zhang. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334.
[108]J J More. The levenberg-marquardt algorithm:implementation and theory. Lecture Notes in Mathematics 630, New York:Springer-Verlag,1977:105-116.