开放环境中番茄的双目立体视觉识别与定位
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摘要
果蔬识别和定位的准确性与实时性将直接影响果蔬采摘机器人的采摘成功率和采摘效率。开放环境中果蔬的识别和定位受非结构环境因素的影响很大,因此一直以来是采摘机器人研究领域的研究重点之一
本研究以番茄为研究对象,利用双目立体视觉技术,详细探讨了开放环境中番茄图像分割算法,被枝叶遮挡番茄识别算法,成簇番茄识别算法及番茄3D定位方法,部分解决了当前番茄采摘机器人视觉系统研究中存在的问题,所开发的番茄识别和定位算法将为番茄采摘机器人视觉系统的改善奠定一定理论和方法基础。主要研究内容及研究结论如下：
(1)比较了基于色差、归一化色差、颜色分量比这三类颜色特征的番茄图像分割算法的分割性能。基于归一化色差、颜色分量比的番茄图像分割算法可实现不同光照强度下的番茄图像分割；光照较弱时.归一化色差法的分割效果较好；顺光条件下,光照较强时,双颜色分量比法的分割效果较好。
(2)提出了融合番茄光斑识别及分段阈值的番茄图像分割算法。该算法基于R分量与光照度的相关性,融合了基于归一化色差及颜色分量比这两类番茄图像分割算法的优点,进一步结合番茄光斑区域识别方法实现了不同光照强度下的番茄图像分割。对184幅图像共计750个含光斑的番茄区域的试验表明：基于该方法的图像分割正确率为93.6%,明显优于基于归一化色差法的36.3%；平均执行时间为71ms；该算法对对顺光条件下的番茄图像具有较好的整体分割性能。
(3)对比研究了基于圆回归及圆Hough变换的被遮挡番茄识别方法。首先两类方法均进行边缘曲率分析,去除曲率异常边缘,然后对曲率正常边缘分别通过圆回归及圆Hough变换实现被遮挡番茄的识别。对220幅存在遮挡的番茄图像的试验结果表明：轻微遮挡时,两类方法的识别正确率分别为90.8%,89.1%；中度遮挡时,两类方法的识别正确率分别为50.4%,74.8%；严重遮挡时,两类方法的识别性能均较差；可见存在遮挡时,圆Hough变换法的整体识别性能优于圆回归法；两类方法的平均执行时间均约为100ms。
(4)对比研究了基于数学形态学、圆回归、圆Hough变换及双目立体视觉的四类成簇番茄识别方法。数学形态学法通过条件腐蚀和循环膨胀实现成簇番茄的识别；圆回归与圆Hough变换法与被遮挡番茄识别所用方法相似；双目立体视觉法根据成簇区域内前后番茄区域深度差,基于迭代Otsu法将成簇番茄分为两类：重叠番茄和粘连番茄,然后使用圆回归法实现粘连番茄的识别,对经深度图像边缘分割后的彩色图像边缘应用圆回归法实现重叠番茄识别。经138对存在轻微遮挡的成簇番茄立体图像的试验结果显示：存在轻微遮挡时,四类方法的识别正确率分别为60.9%,69.0%,71.1%,82.5%；存在较严重遮挡时,四类方法的识别性能均不理想；四类方法的平均执行时间分别为3、85、138、500ms。试验结果表明：双目立体视觉法对粘连及重叠番茄的识别性能均较好,其余三类方法对粘连番茄的识别性能较好,而对重叠番茄的识别性能均较差；双目立体视觉法的整体识别性能要优于其余三类方法,特别是对重叠番茄。
(5)提出了番茄3D测量误差校正方法。研究了基于形心特征匹配,区域匹配及组合匹配的三类立体匹配方法,分析了基于三类立体匹配的3D定位误差,分析了造成深度测量误差的要因,提出了相应的校正方法,讨论了遮挡对3D定位精度的影响,测试分析了三类立体匹配方法的实时性。组合匹配将粗匹配阶段通过形心匹配获得的番茄粗略视差作为精匹配阶段区域匹配的视差范围中心,从而获得了一个中心随图像采集距离变化而变化的动态视差范围,在确保获得与区域匹配相近匹配精度的同时,减少了立体匹配计算量,图像采集距离在300～１000mm范围内时,所需匹配时间为区域匹配的三分之一。1349对立体图像的试验表明：1)基于三类立体匹配得到的x坐标测量误差相对较小,分别为：[0,12.8],[1.3,13.8],[1.3,13.8]mm。y坐标及深度测量误差相对较大,且与图像采集距离间分别呈现近似线性递减和递增的关系。2)经二元分段线性回归y坐标预测模型校正后,基于三类立体匹配的y坐标测量误差范围分别为：[-7,-0.8],[-6.1,1.4],[-6.1,1.8]mm。3)番茄大小是影响深度测量误差的要因。4)经基于深度测量值及番茄大小的二元线性回归深度预测模型校正后,基于三类立体匹配的深度测量误差范围分别为：[-10.4,28.7],[-6,12.8],[-5.6,5.3]mm。5)x、y坐标测量结果比深度测量值更易受遮挡影响。6)三类匹配方法的执行时间分别为：[0.4,4],[24,288.8],[9.6,98.8]ms。
(6)编制了基于C语言的番茄识别和定位软件。完成了番茄识别和定位算法应用流程,动态链接库及测试软件的设计,应用于番茄采摘机器人的视觉系统后,进行了实验室及温室的采摘试验。23次实验室采摘试验,识别成功18次,采摘成功11次；10次温室采摘试验,识别成功9次,采摘成功8次；采摘效率约为30s/个。
上述研究成果将为进一步提高番茄采摘机器人视觉系统对开放环境的适应能力奠定一定的方法和技术基础。
The accuracy and real time performance of recognition and localization methods for fruits and vegetables is the key to the success rate and efficiency of fruits and vegetables harvesting robots. Recognition and localization of fruits and vegetables is much influenced by the environment factors under open environments, so it is always a difficult task in the research field of harvesting robots.
Tomatoes are used as samples in this study. Image segmentation methods for tomatoes, recognition methods for occluded tomatoes, recognition methods for clustered tomatoes and localization methods for tomatoes were detailed studied by using binocular stereo vision technology. Some problems in the recognition and localization of tomatoes were solved in this study. The developed recognition and localization algorithms offered help for improving the vision system of tomato harvesting robots. Main contents and results were listed as follows:
(1) Segmentation performance was compared among three image segmentation methods for tomatoes based on color difference, normalized color difference and color component ratio. The method based on normalized color difference and the method based on color component ratio could realize the image segmentation for tomato images captured under different lighting conditions; the method based on normalized color difference was better under darker lighting conditions; the method based on double ratios of color components was better under front lighting conditions.
(2) A piecewise thresholding segmentation method for tomatoes fusing a recognition method for light spots was presented. Based on the dependency between R component values of pixels in the region of a tomato and the illuminance on the surface of the tomato, this method used both advantages of two methods:the method based on normalized color difference and the method based on color component ratio. Furthermore, the recognition method for light spots was also used. Employing this method, tomato image segmentation could be realized under different lighting conditions. Test results from184images in which there were750tomatoes with light spots on their surface showed that the success rate of image segmentation was93.6%. which was much better than the one of the method based on normalized color difference, which was onlv36.3%. The average running time was71ms. This method had good segmentation performance to tomato images which were captured under front lighting conditions.
(3) Two recognition methods for occluded tomatoes were studied:the method based on circle regression and the method based on circle Hough transformation. Firstly, the two methods were both based on curvature analysis, and then edge points with abnormal curvature values were discarded, after that, occluded tomatoes were recognized through the circle regression method and the circle Hough transformation method for the remaining edges, respectively. Test results from220images in which there were tomaoes occluded by leaves or branches showed that for tomatoes which were occluded by branches or leaves slightly, the recognition success rates of these two methods were90.8%and89.1%, respectively. For tomatoes occluded moderately, the recognition success rates of these two methods were90.8%and89.1%, respectively. For tomatoes occluded seriously, two methods both had bad recognition performance. Overall, for occluded tomatoes, the performance of the method based on circle Hough transformation was much better than that of the other. The running times for two methods were both about100ms.
(4) Four recognition methods for clustered tomatoes were presented:the method based on mathematic morphology, the method based on circle regression, the method based on Hough transformation and the method based on binocular stereo vision. The method based on mathematic morphology was realized through the operation of conditional corrosion and circular expansion. The method based on circle regression and the method based on Hough transformation was similar to those used in the recognition of occluded tomatoes. In the method based on binocular stereo vision, clustered tomatoes were classified into two types:overlapping tomatoes and adhering tomatoes, based on the depth difference between the front tomato and the back one in a same clustered region employing an iterative Otsu method. Then, adhering tomatoes were recognized using the circle regression method. On the other hand, overlapping tomatoes were recognized using the circle regression method for the edges of the clustered region in color image which was segmented into several segments by the edges in depth map. Furthermore, the number of edge segments was same to the number of tomatoes in this clustered region. Test results from138pairs of stereo images of tomatoes occluded slightly showed that the recognition success rates of these four methods were60.9%,69.0%,71.1%,82.5%, respectively. However, for tomatoes occluded seriously, the recognition performance of these four methods were all not good enough. The running times of these four methods were3,85.138, and500ms. respectively. Test results showed that the recognition performance of the method based on binocular stereo vision was good to both two types of clustered tomatoes. Otherwise, the recognition performance of other three methods was good to adhering tomatoes, but was not good to overlapping tomatoes. Overall, the performance of the method based on binocular stereo vision was better than other three methods, especially for overlapping tomatoes.
(5) Correction models for3D measurement error were presented. Three stereo matching methods were studied:the centroid-based stereo matching method, the area-based stereo matching method and the combination stereo matching method.3D localization errors were analyzed for the results produced based on these three stereo matching methods, the main factor to depth measurement errors was analyzed, and correction models were also presented. Furthermore, the influence to the accuracy of3D localization for tomatoes caused by occlusion was also discussed. Finally, the real time performance of these three stereo matching methods was tested, too. Combination stereo matching included two stages:rough stereo matching and precise stereo matching. Disparity acquired through centroid-based stereo matching at the rough matching stage was used as the center of the disparity range which was used in the area-based stereo matching at the precise matching stage. Then a dynamic disparity range was acquired of which the center moved with the shoot distance. By this way. not only the accuracy of the stereo matching was promised to be similar to that of the area-based stereo matching method, but also the time costs of stereo matching were also reduced. The time costs reduced to one third of that of area-based stereo matching method when the shoot distances were ranged from300to1000mm. Test results from1349pairs of stereo images showed that:1) for three stereo matching methods,x coordinate measurement errors were all smaller:the range of x coordinate measurement errors based on three stereo matching methods were [0,12.8],[1.3,13.8].[1.3.13.8] mm, respectively; but y coordinate and depth measurement errors were larger; y coordinate errors increased and depth errors decreased as the distances got larger.2) After the correction using binary piece wise linear regression models for y coordinate prediction, the ranges of y coordinate measurement errors based on three stereo matching methods were [-7,-0.8].[-6.1.1.4],[-6.1,1.8] mm. respectively3) Tomato size was the main factor to the depth measurement errors.4) After correction using binary linear regression models for depth prediction, the ranges of depth measurement errors based on three stereo matching methods were [-10.4,28.7],[-6,12.8],[-5.6,5.3] mm, respectively.5) x and y coordinate measurement results were more easily influenced by occlusion than depth measurement results.6) The ranges of running times of three stereo matching methods were [0.4,4],[24,288.8],[9.6,98.8] ms, respectively.
(6) Finally, softwares of the recognition and localization methods for tomatoes under open environments were designed based on the C program language. The application flow chart of the recognition and localization methods for tomatoes and its dynamic link library produced from the C program of the recognition and localization methods was designed. Moreover, a VB program interface which was used to test the DLL was also designed. Harvesting experiments were executed both in laboratory and greenhouse after the DLL was employed by the vision system of a tomato harvesting robot. Among23times harvesting attempts in laboratory, the number of successful recognition was18, and the number of successful harvesting was11. Moreover, the numbers of successful recognition and successful harvesting were9and8in10times of harvesting attempts in greenhouse. The harvesting efficiency was about30s per tomato.
The above work provided method and technology foundation for improving the adaption performance of the vision system of tomato harvesting robots under open environments.
本研究以番茄为研究对象,利用双目立体视觉技术,详细探讨了开放环境中番茄图像分割算法,被枝叶遮挡番茄识别算法,成簇番茄识别算法及番茄3D定位方法,部分解决了当前番茄采摘机器人视觉系统研究中存在的问题,所开发的番茄识别和定位算法将为番茄采摘机器人视觉系统的改善奠定一定理论和方法基础。主要研究内容及研究结论如下：
(1)比较了基于色差、归一化色差、颜色分量比这三类颜色特征的番茄图像分割算法的分割性能。基于归一化色差、颜色分量比的番茄图像分割算法可实现不同光照强度下的番茄图像分割；光照较弱时.归一化色差法的分割效果较好；顺光条件下,光照较强时,双颜色分量比法的分割效果较好。
(2)提出了融合番茄光斑识别及分段阈值的番茄图像分割算法。该算法基于R分量与光照度的相关性,融合了基于归一化色差及颜色分量比这两类番茄图像分割算法的优点,进一步结合番茄光斑区域识别方法实现了不同光照强度下的番茄图像分割。对184幅图像共计750个含光斑的番茄区域的试验表明：基于该方法的图像分割正确率为93.6%,明显优于基于归一化色差法的36.3%；平均执行时间为71ms；该算法对对顺光条件下的番茄图像具有较好的整体分割性能。
(3)对比研究了基于圆回归及圆Hough变换的被遮挡番茄识别方法。首先两类方法均进行边缘曲率分析,去除曲率异常边缘,然后对曲率正常边缘分别通过圆回归及圆Hough变换实现被遮挡番茄的识别。对220幅存在遮挡的番茄图像的试验结果表明：轻微遮挡时,两类方法的识别正确率分别为90.8%,89.1%；中度遮挡时,两类方法的识别正确率分别为50.4%,74.8%；严重遮挡时,两类方法的识别性能均较差；可见存在遮挡时,圆Hough变换法的整体识别性能优于圆回归法；两类方法的平均执行时间均约为100ms。
(4)对比研究了基于数学形态学、圆回归、圆Hough变换及双目立体视觉的四类成簇番茄识别方法。数学形态学法通过条件腐蚀和循环膨胀实现成簇番茄的识别；圆回归与圆Hough变换法与被遮挡番茄识别所用方法相似；双目立体视觉法根据成簇区域内前后番茄区域深度差,基于迭代Otsu法将成簇番茄分为两类：重叠番茄和粘连番茄,然后使用圆回归法实现粘连番茄的识别,对经深度图像边缘分割后的彩色图像边缘应用圆回归法实现重叠番茄识别。经138对存在轻微遮挡的成簇番茄立体图像的试验结果显示：存在轻微遮挡时,四类方法的识别正确率分别为60.9%,69.0%,71.1%,82.5%；存在较严重遮挡时,四类方法的识别性能均不理想；四类方法的平均执行时间分别为3、85、138、500ms。试验结果表明：双目立体视觉法对粘连及重叠番茄的识别性能均较好,其余三类方法对粘连番茄的识别性能较好,而对重叠番茄的识别性能均较差；双目立体视觉法的整体识别性能要优于其余三类方法,特别是对重叠番茄。
(5)提出了番茄3D测量误差校正方法。研究了基于形心特征匹配,区域匹配及组合匹配的三类立体匹配方法,分析了基于三类立体匹配的3D定位误差,分析了造成深度测量误差的要因,提出了相应的校正方法,讨论了遮挡对3D定位精度的影响,测试分析了三类立体匹配方法的实时性。组合匹配将粗匹配阶段通过形心匹配获得的番茄粗略视差作为精匹配阶段区域匹配的视差范围中心,从而获得了一个中心随图像采集距离变化而变化的动态视差范围,在确保获得与区域匹配相近匹配精度的同时,减少了立体匹配计算量,图像采集距离在300～１000mm范围内时,所需匹配时间为区域匹配的三分之一。1349对立体图像的试验表明：1)基于三类立体匹配得到的x坐标测量误差相对较小,分别为：[0,12.8],[1.3,13.8],[1.3,13.8]mm。y坐标及深度测量误差相对较大,且与图像采集距离间分别呈现近似线性递减和递增的关系。2)经二元分段线性回归y坐标预测模型校正后,基于三类立体匹配的y坐标测量误差范围分别为：[-7,-0.8],[-6.1,1.4],[-6.1,1.8]mm。3)番茄大小是影响深度测量误差的要因。4)经基于深度测量值及番茄大小的二元线性回归深度预测模型校正后,基于三类立体匹配的深度测量误差范围分别为：[-10.4,28.7],[-6,12.8],[-5.6,5.3]mm。5)x、y坐标测量结果比深度测量值更易受遮挡影响。6)三类匹配方法的执行时间分别为：[0.4,4],[24,288.8],[9.6,98.8]ms。
(6)编制了基于C语言的番茄识别和定位软件。完成了番茄识别和定位算法应用流程,动态链接库及测试软件的设计,应用于番茄采摘机器人的视觉系统后,进行了实验室及温室的采摘试验。23次实验室采摘试验,识别成功18次,采摘成功11次；10次温室采摘试验,识别成功9次,采摘成功8次；采摘效率约为30s/个。
上述研究成果将为进一步提高番茄采摘机器人视觉系统对开放环境的适应能力奠定一定的方法和技术基础。
The accuracy and real time performance of recognition and localization methods for fruits and vegetables is the key to the success rate and efficiency of fruits and vegetables harvesting robots. Recognition and localization of fruits and vegetables is much influenced by the environment factors under open environments, so it is always a difficult task in the research field of harvesting robots.
Tomatoes are used as samples in this study. Image segmentation methods for tomatoes, recognition methods for occluded tomatoes, recognition methods for clustered tomatoes and localization methods for tomatoes were detailed studied by using binocular stereo vision technology. Some problems in the recognition and localization of tomatoes were solved in this study. The developed recognition and localization algorithms offered help for improving the vision system of tomato harvesting robots. Main contents and results were listed as follows:
(1) Segmentation performance was compared among three image segmentation methods for tomatoes based on color difference, normalized color difference and color component ratio. The method based on normalized color difference and the method based on color component ratio could realize the image segmentation for tomato images captured under different lighting conditions; the method based on normalized color difference was better under darker lighting conditions; the method based on double ratios of color components was better under front lighting conditions.
(2) A piecewise thresholding segmentation method for tomatoes fusing a recognition method for light spots was presented. Based on the dependency between R component values of pixels in the region of a tomato and the illuminance on the surface of the tomato, this method used both advantages of two methods:the method based on normalized color difference and the method based on color component ratio. Furthermore, the recognition method for light spots was also used. Employing this method, tomato image segmentation could be realized under different lighting conditions. Test results from184images in which there were750tomatoes with light spots on their surface showed that the success rate of image segmentation was93.6%. which was much better than the one of the method based on normalized color difference, which was onlv36.3%. The average running time was71ms. This method had good segmentation performance to tomato images which were captured under front lighting conditions.
(3) Two recognition methods for occluded tomatoes were studied:the method based on circle regression and the method based on circle Hough transformation. Firstly, the two methods were both based on curvature analysis, and then edge points with abnormal curvature values were discarded, after that, occluded tomatoes were recognized through the circle regression method and the circle Hough transformation method for the remaining edges, respectively. Test results from220images in which there were tomaoes occluded by leaves or branches showed that for tomatoes which were occluded by branches or leaves slightly, the recognition success rates of these two methods were90.8%and89.1%, respectively. For tomatoes occluded moderately, the recognition success rates of these two methods were90.8%and89.1%, respectively. For tomatoes occluded seriously, two methods both had bad recognition performance. Overall, for occluded tomatoes, the performance of the method based on circle Hough transformation was much better than that of the other. The running times for two methods were both about100ms.
(4) Four recognition methods for clustered tomatoes were presented:the method based on mathematic morphology, the method based on circle regression, the method based on Hough transformation and the method based on binocular stereo vision. The method based on mathematic morphology was realized through the operation of conditional corrosion and circular expansion. The method based on circle regression and the method based on Hough transformation was similar to those used in the recognition of occluded tomatoes. In the method based on binocular stereo vision, clustered tomatoes were classified into two types:overlapping tomatoes and adhering tomatoes, based on the depth difference between the front tomato and the back one in a same clustered region employing an iterative Otsu method. Then, adhering tomatoes were recognized using the circle regression method. On the other hand, overlapping tomatoes were recognized using the circle regression method for the edges of the clustered region in color image which was segmented into several segments by the edges in depth map. Furthermore, the number of edge segments was same to the number of tomatoes in this clustered region. Test results from138pairs of stereo images of tomatoes occluded slightly showed that the recognition success rates of these four methods were60.9%,69.0%,71.1%,82.5%, respectively. However, for tomatoes occluded seriously, the recognition performance of these four methods were all not good enough. The running times of these four methods were3,85.138, and500ms. respectively. Test results showed that the recognition performance of the method based on binocular stereo vision was good to both two types of clustered tomatoes. Otherwise, the recognition performance of other three methods was good to adhering tomatoes, but was not good to overlapping tomatoes. Overall, the performance of the method based on binocular stereo vision was better than other three methods, especially for overlapping tomatoes.
(5) Correction models for3D measurement error were presented. Three stereo matching methods were studied:the centroid-based stereo matching method, the area-based stereo matching method and the combination stereo matching method.3D localization errors were analyzed for the results produced based on these three stereo matching methods, the main factor to depth measurement errors was analyzed, and correction models were also presented. Furthermore, the influence to the accuracy of3D localization for tomatoes caused by occlusion was also discussed. Finally, the real time performance of these three stereo matching methods was tested, too. Combination stereo matching included two stages:rough stereo matching and precise stereo matching. Disparity acquired through centroid-based stereo matching at the rough matching stage was used as the center of the disparity range which was used in the area-based stereo matching at the precise matching stage. Then a dynamic disparity range was acquired of which the center moved with the shoot distance. By this way. not only the accuracy of the stereo matching was promised to be similar to that of the area-based stereo matching method, but also the time costs of stereo matching were also reduced. The time costs reduced to one third of that of area-based stereo matching method when the shoot distances were ranged from300to1000mm. Test results from1349pairs of stereo images showed that:1) for three stereo matching methods,x coordinate measurement errors were all smaller:the range of x coordinate measurement errors based on three stereo matching methods were [0,12.8],[1.3,13.8].[1.3.13.8] mm, respectively; but y coordinate and depth measurement errors were larger; y coordinate errors increased and depth errors decreased as the distances got larger.2) After the correction using binary piece wise linear regression models for y coordinate prediction, the ranges of y coordinate measurement errors based on three stereo matching methods were [-7,-0.8].[-6.1.1.4],[-6.1,1.8] mm. respectively3) Tomato size was the main factor to the depth measurement errors.4) After correction using binary linear regression models for depth prediction, the ranges of depth measurement errors based on three stereo matching methods were [-10.4,28.7],[-6,12.8],[-5.6,5.3] mm, respectively.5) x and y coordinate measurement results were more easily influenced by occlusion than depth measurement results.6) The ranges of running times of three stereo matching methods were [0.4,4],[24,288.8],[9.6,98.8] ms, respectively.
(6) Finally, softwares of the recognition and localization methods for tomatoes under open environments were designed based on the C program language. The application flow chart of the recognition and localization methods for tomatoes and its dynamic link library produced from the C program of the recognition and localization methods was designed. Moreover, a VB program interface which was used to test the DLL was also designed. Harvesting experiments were executed both in laboratory and greenhouse after the DLL was employed by the vision system of a tomato harvesting robot. Among23times harvesting attempts in laboratory, the number of successful recognition was18, and the number of successful harvesting was11. Moreover, the numbers of successful recognition and successful harvesting were9and8in10times of harvesting attempts in greenhouse. The harvesting efficiency was about30s per tomato.
The above work provided method and technology foundation for improving the adaption performance of the vision system of tomato harvesting robots under open environments.
引文
[1]中华人民共和国国家统计局.2012中国统计年鉴,北京,中国统计出版社,2012.
[2]汤修映,张铁中.果蔬收获机器人研究综述.机器人,2005,27(1)：90-96.
[3]Jimenez A.R... Ceres R., Pons J.L. A survey of computer vision methods for locating fruit on trees. Transaction of the ASAE,2000,43(6):1911-1920.
[4]Li. P.L., Lee, S.H., Hsu, H.Y. Review on fruit harvesting method for potential use of automatic fruit harvesting systems. Procedia Engineering,2011,23:351-366.
[5]Torregrosa, A., Orti. E.. Martin, B., et al. Mechanical harvesting of oranges and mandarins in Spain. Biosystems Engineering.2009,104:18-24.
[6]陈度,杜小强,王书茂,等.振动式果品收获技术机理分析及研究进展.农业工程学报,2011,27(8)：195-200.
[7]Du, X.Q.. Chen. D., Zhang. Q.. et al. Dynamic responses of sweet cherry trees under vibrator)'excitations. Biosystems Engineering,2012,111:305-314.
[8]赵匀,武传字,胡旭东,等.农业机器人的研究进展及存在的问题.农业工程学报,2003,19(1)：20-24.
[9]徐丽明.张铁中.果蔬果实收获机器人的研究现状及关键问题和对策.农业二程学报,2004,20(5)：38-42.
[10]张立彬.计时鸣,胥芳,等.农业机器人的主要应用领域和关键技术.浙江工业大学学报,2002.30(1)：36-41.
[]1]宋健,张铁中,徐丽明,等.果蔬采摘机器人研究进展与展望.农业机械学报,2006,37(5)：158-162.
[12]Van Henten, E.J., Hemming, J., Van Tuijl, B.A.J., et al. An autonomous robot for harvesting cucumbers in greenhouses. Autonomous Robots.2002,13:241-258.
[13]Van Henten, E.J., Van Tuijl, B.A.J., Hemming, J., et al. Field test of an autonomous cucumber picking robot. Biosystems Engineering,2003,86(3):305-313.
[14]Van Henten, E.J.. Hemming, J., Van Tuijl, B.A.J., et al. Collision-free Motion Planning for a Cucumber Picking Robot. Biosystems Engineering.2003,86 (2):135-144.
[15]Van Henten, E.J.. Van't Slot. D.A., Hol, C.W.J., et al. Optimal Design of a Cucumber Harvesting Robot. Proceedings of AgEng 2006, Bonn, Germany,2006, Paper nr.802.
[16]Van Henten, E.J., Van Willigenburg, L.G., Van't Slot, D. Optimal design of a cucumber harvesting robot. Computers and Electronics in Agriculture,2009.65:247-257.
[17]纪超,冯青春,袁挺,等.温室黄瓜采摘机器人系统研制及性能分析.机器人,2011.33(6)：726-730.
[18]Hayashi, S., Ganno. K.. Ishii, Y., et al. Robotic harvesting system for eggplants. JARQ,2002, 36:163-168.
[19]Reed, J.N., Miles, S.J., Butler, J., et al. Automatic mushroom harvester development. J. agric. Engng Res.,2001,78(1):15-23.
[20]周云山,李强,李红英,等.计算机视觉在蘑菇采摘机器人上的应用.农业工程学报,1995,16(4)：27-32.
[21]Plebe, A., Grasso, G. Localization of spherical fruits for robotic harvesting. Machine Vision and Applications,2001,13(2):70-79.
[22]Zhao, D.A., Lu, J.D., Ji, W., et al. Design and control of an apple harvesting robot. Biosystems Engineering,2011,110:112-122.
[23]顾宝兴,姬长英,王海青,等.智能移动水果采摘机器人设计与试验.农业机械学报,2012,43(6)：153-160.
[24]Kondo, N., Ninomiya, K., Hayashi, S., et al. A new challenge of robot for harvesting strawberry grown on table top culture. In 2005 ASAE Annual International Meeting, Paper No.053138. Tampa, Florida, USA:ASAE.
[25]Hayashi S., Shigematsu, K.., Yamamoto, S., et al. Evaluation of a strawberry-harvesting robot in a field test. Biosystems Engineering,2009:1-12.
[26]Guo, F., Cao, Q.X., Cui, YJ, et al. Fruit location and stem detection method for strawberry harvesting robot. Transactions of the CSAE,2008,24(10):89-94.
[27]张凯良,杨丽,王粮局,等.高架草莓采摘机器人设计与试验.农业机械学报,2012,43(9)：165-172.
[28]Tanigaki, K., Fujiura, T., Akase, A., et al. Cherry-harvesting robot. Computers and Electronics in Agriculture,2008,63(1):65-72.
[29]Edan Y., Rogozin D., Flash T., et al. Robotic melon harvesting. IEEE Transactions on Robotics and Automation,2000,16(6):831-834.
[30]李斌,Ning, W.,汪懋华,等.基于单目视觉的田间菠萝果实识别.农业工程学报,2010,26(10)：345-349.
[31]Erdogan, D., Guner, M., Dursun, E., et al. Mechanical harvesting of apricots. Biosystems Engineering, 2003,85(1):19-28.
[32]Scarfe, A.J., Flemmer, R.C., Bakker, H.H., et al. Development ofan autonomous kiwifruit picking robot. Proceedings of the 4th International Conference on Autonomous Robots and Agents, Wellington, New Zealand,2009,380-384.
[33]Aljanobi, A. A., Al-hamed, S.A., Al-Suhaibani, S. A. A setup of mobile robotic unit for fruit harvesting. 19th International Workshop on Robotics in Alpe-Adria-Danube Region-RAAD 2010, Budapest, Hungary,
2010,105-108.
[34]Kohan, A., Borghaee, A.M., Yazdi, M., et al. Robotic harvesting of rosa damascena using stereoscopic machine vision. World Applied Sciences Journal,2011,12 (2):231-237.
[35]Cho, S. I., Chang, S. J., Kim, Y. Y., et al. Development of a three-degrees-of-freedom robot for harvesting lettuce using machine vision and fuzzy logic control. Biosystems Engineering,2002,82 (2): 143-149.
[36]Rath, T., Kawollek, M. Robotic harvesting of Gerbera Jamesonii based on detection and three-dimensional modeling of cut flower pedicels. Computers and Electronics in Agriculture,2009, 66:85-92.
[37]Dong. F., Heinemann. W., Kasper, R. Development of a row guidance system for an autonomous robot for white asparagus harvesting. Computers and Electronics in Agriculture.2011.79:216-225.
[38]Chatzimichali, A. P.. Georgilas. I.P.. Tourassis, V.D. Design of an advanced prototype robot for white asparagus harvesting.2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore.2009.887-892.
[39]Kitamura, S., Oka, K., Ikutomo, K., et al. A distinction method for fruit of sweet pepper using reflection of led light. International Conference on Instrumentation Control and Information Technology, Chofu, Tokyo, Japan,2008:491-494.
[40]Foglia. M.M., Reina, G. Agricultural robot for radicchio harvesting. Journal of Field Robotics,2006, 23(6/7):363-377.
[41]Polat, R., Gezer, I., Guner, M., et al.Mechanical harvesting of pistachio nuts. Journal of Food Engineering,2007,79:1131-1135.
[42]陆怀民.林木球果采摘机器人设计与试验.农业机械学报,2001.32(6)：52-58.
[43]Ji, W., Zhao, D.A., Cheng, F.Y., et al. Automatic recognition vision system guided for apple harvesting robot. Computers and Electrical Engineering,2012,38:1186-1195.
[44]Gotou, K., Fujiura, T., Nishiura. Y., et al.3-D vision system of tomato production robot. Proceedings of the 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics,2003,2:1210-1215.
[45]Arima, S., Kondo. N., Monta. M. Strawberry Harvesting Robot on Table-top Culture. In 2004 ASAE/CSAE Annual International Meeting, Paper No.043089. Ottawa, Canada, USA:ASAE.
[46]Kondo, N., Ting. K.C. Robotics for plant production. Artificial Intelligence Review,1998, 12(1-3):227-243.
[47]Jimenez. A.R., Jain, A.K., Ceres, R. Automatic fruit recognition:A survey and new results using Range/Attenuation images. Pattern Recognition,1999.32(10):1719-1736.
[48]Jimenez. A.R., Ceres, R., Pons, J.L. A vision system based on a laser range-finder applied to robotic fruit harvesting. Machine Vision and Applications,2000,11:321-329.
[49]Stajnko. D., Lakota, M., Hocevar, M. Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging. Computers and Electronics in Agriculture,2004,42:31-42.
[50]徐惠荣,应义斌.红外热成像在树上柑桔识别中的应用研究.红外与毫米波学报,2004,23(5)：353-356.
[51]Bulanona, D.M., Burksa, T.F., Alchanatis, V. Study on temporal variation in citrus canopy using thermal imaging for citrus fruit detection. Biosystems engineering,2008,101:161-171.
[52]毛罕平,李明喜,张艳诚.基于多光谱图像融合和形态重构的图像分割方法.农业工程学报,2008,24(6)：174-178.
[53]Bulanon. D.M., Burks, T.F., Alchanatis, V. Image fusion of visible and thermal images for fruit detection. Biosystems Engineering,2009,103:12-22.
[54]俞晓磊,赵志敏.光谱导航技术在果树果实定位中的应用.光谱学与光谱分析,2009. 30(3)：770-773.
[55]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.
[56]Bulanon, D.M., Burks, T.F., Alchanatis, V. A multispectral imaging analysis for enhancing cirtrus detection. Environ. Control Biol.,2010,48(2):81-91.
[57]Okamoto, H., Lee, W.S. Green citrus detection using hyperspectral imaging. Computers and Electronics in Agriculture,2009,66:201-208.
[58]Yang, L., Dickinson, J., Wu, Q.M.J., et al. A fruit recognition method for automatic harvesting.14th International Conference on Mechatronics and Machine Vision in Practice, Xiamen, China,2007,152-157.
[59]Kurtulmus, F., Lee, W. S., Vardar, A. Green citrus detection using'eigenfruit', color and circular Gabor texture features under natural outdoor conditions. Computers and Electronics in Agriculture,2011, 78:140-149.
[60]Rajendra, P., Mitsutaka, K., Kazunori, N., et al. Shading compensation methods for robots to harvest strawberries in tabletop culture.2011 International Symposium on System Integration,2011,172-177.
[61]Arefi, A., Motlagh, A. M., Teimourlou, R. F. A segmentation algorithm for the automatic recognition of tomato at harvest. Journal of Food Agriculture & Environment,2010,8(3-4):815-819.
[62]蔡健荣,周小军,李玉良,等.基于机器视觉自然场景下成熟柑橘识别.农业工程学报,2008,24(1)：175-178.
[63]Li, P.L., Lee, S.H., Hsu, H.Y. Study on citrus fruit image data separability by segmentation. Procedia Engineering,2011,23:408-416.
[64]Mao, W., Jia, B., Zhang, X., et al. Detection and position method of apple tree image. In International Federation for Information Processing, Beijing, China,2009:1039-1048.
[65]Yin, J.J., Mao, H.P., Zhong. S.Y. Segmentation methods of fruit image based on color difference. Journal of Communication and Computer,2009,6(7):40-45.
[66]Bulanon, D.M., Kataoka, T., Okamoto, H., et al. Determining the 3-D location of the apple fruit during harvest. Automation Technology for Off-Road Equipment Proceedings, Hyatt Regency Chicago, Chicago, Illinois, USA,2004:91-97.
[67]Gu, H., Lu, Y.Y., Lou, J.L., et al. Recognition and location of fruit objects based on machine vision. In 16th International Conference on Artificial Reality and Telexistence, Hangzhou, China,2006:785-795.
[68]李寒,王库,曹倩,等.基于机器视觉的番茄多目标提取与匹配.农业工程学报,2012,28(5)：168-172.
[69]荀一,陈晓,李伟,等.基于轮廓曲率的树上苹果自动识别.江苏大学学报：自然科学版,2007,(286)：461-464.
[70]Tarrio, Paula., Bernardos, A.M., Casar, J.R., et al. A harvesting robot for small fruit in bunches based on 3-d stereoscopic vision.4th World Congress on Computers in Agriculture and Natural Resources, Orlando, Florida, USA,2006:270-275.
[71]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.
[72]Yin, H.P., Chai, Y., Yang, S.X., et al. Ripe tomato detection for robotic vision harvesting systems in greenhouses. Transactions of the ASABE,2011,54(4):1539-1546.
[73]Bulanon, D.M., Kataoka, T., Ota, Y., et al. A segmentation algorithm for the automatic recognition of Fuji apples at harvest. Biosystems Engineering,2002,83(4):405-412.
[74]Bulanon, D.M., Kataoka, T., Ota, Y., et al. A machine vision system for the apple harvesting robot. Agricultural Engineering International:the CIGR Journal of Scientific Research and Development, Manuscript PM 01006, Vol. Ill,2001.
[75]Bulanon, D.M., Kataoka, T., Okamoto, H., et al. Development of a real-time machine vision system for the apple harvesting robot. SICE Annual Conference in Sapporo, Hokkaido Institute of Tecnology, Japan. 2004,1:595-598.
[76]杨旭强,冯勇,刘洪臣.一种基于HSI颜色模型的目标提取方法.光学技术,2006,32(2)：290-292.
[77]赵金英,张铁中,杨丽.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.
[78]韦皆顶,费树岷,汪木兰.基于HSV彩色模型的自然场景下棉花图像分割策略研究.棉花学报,2008,20(1)：34-38.
[79]熊俊涛,邹湘军,陈丽娟,等.采摘机械手对扰动荔枝的视觉定位.农业工程学报,2012,28(14)：36-41.
[80]郭艾侠,邹湘军,朱梦思,等.基于探索性分析的的荔枝果及结果母枝颜色特征分析与识别[J].农业工程学报,2013,29(4)：191-198.
[81]郑小东,赵杰文,刘木华.基于双目立体视觉的番茄识别与定位技术.计算机工程,2004,30(22)：171-173.
[82]蔡健荣,赵杰文.自然场景下成熟水果的计算机视觉识别.农业机械学报,2005,36(2)：61-64.
[83]张铁中,陈利兵,宋健.草莓采摘机器人的研究Ⅱ：基于图像的草莓重心位置和采摘点的确定.中国农业大学学报,2005,10(1)：48-51.
[84]徐惠荣,叶尊忠,应义斌.基于彩色信息的树上柑橘识别研究.农业工程学报,2005,21(5)：98-101.
[85]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,2006,1(1)：84-86.
[86]尹建军,王新忠.毛罕平.RGB与HSI颜色空间下番茄图像分割的对比研究.农机化研究,2006,11：171-174.
[87]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.
[88]宋健.自动阈值法在生长环境茄子图像分割中的应用.潍坊学院学报,2007,7(6)：6-9.
[89]申川.基于双目立体视觉系统的设施农业作物位置信息获取[硕士学位论文].杭州,浙江大学,2006.
[90]蒋焕煜,彭永石,由川,等.基于双目立体视觉技术的成熟番茄识别与定位.农业工程学报,2008,24(8)：279-283.
[91]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.
[92]Jiang H.Y., Peng, Y.S.,Ying Y.B. Measurement of 3-D locations of ripe tomato by binocular stereo vision for tomato harvesting. In 2008 ASABE Annual International Meeting, Rhode Island, USA,2008,11: 6616-6621.
[93]彭永石.基于双目立体视觉技术的果蔬采摘机器人视觉系统的研究[硕士学位论文].杭州,浙江大学,2008.
[94]Mao, W.H., Ji, B.P., Zhan, J.C., et al. Apple location method for the apple harvesting robot.2nd International Congress on Image and Signal Processing, Tianjin, China,2009,1-5.
[95]宋健.基于神经网络的茄子采摘机器人视觉识别方法.潍坊学院学报,2011,11(6)：90-93.
[96]Arefi, A., Motlagh, A. M., Mollazade, K., et al. Recognition and localization of ripen tomato based on machine vision. Australian Journal of Crop Science,2011,5(10):1144-1149.
[97]吕继东,赵德安,姬伟等.采摘机器人振荡果实动态识别,农业机械学报,2012,43(5)：173-196.
[98]钱建平,杨信廷,吴晓明,等.自然场景下基于混合颜色空间的成熟期苹果识别方法[J].农业工程学报,2012,28(17)：137-142.
[99]Qi, L.Y., Yang, Q.H., Bao, G.J., et al. A dynamic threshold segmentation algorithm for cucumber identification in greenhouse.2nd International Congress on Image and Signal Processing, Tianjin, China, 2009,1-4.
[100]李二超,李战明,刘徽容.基于颜色和纹理特征的黄瓜果实图像分割.光学技术,2009,35(4)：529-531.
[101]Xiang, R., Ying, Y. B., Jiang, H. Y. Research on image segmentation methods of tomato in nature conditions. In 2011 4th International Congress on Image and Signal Processing, Shanghai, China, 2011:1268-1272.
[102]Patel, H.N., Jain, R.K., Joshi, M.V. Fruit detection using improved multiple features based algorithm. International Journal of Computer Applications,2011,13(2):1-5.
[103]Xu, Y.W., Imou, K., Kaizu, Y., et al. Two-stage approach for detecting slightly overlapping strawberries using HOG descriptor. Biosystems Engineering,2013,115:144-153.
[104]Philipp, I., Rath, T. Improving plant discrimination in image processing by use of different colour space transformations. Computers and Electronics in Agriculture,2002,35:1-15.
[105]Wang, X.Z., Mao, H.P., Han, X., et al. Vision-based judgment of tomato maturity under growth conditions. African Journal of Biotechnology,2011,10(18):3616-3623.
[106]李听,李立君,高自成,等.基于偏好人工免疫网络多特征融合的油茶果图像识别.农业工程学报,2012,28(14)：133-137.
[107]Pastrana, J. C., Rath, T. Novel image processing approach for solving the overlapping problem in agriculture. Biosystems Engineering,2013,115:106-115.
[108]Song, J. Image segmentation of eggplants in growing environment based on improved BP neural network. Advances in Science and Engineering,2011, (40-41):599-603.
[109]杨国彬,赵杰文,向忠平.利用计算机视觉对自然背景下西红柿进行判别.农机化研究,2003, 1(1)：60-62.
[110]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.
[111]Zheng, L.Y.. Zhang, J.T., Wang, Q.Y. Mean-shift-based color segmentation of images containing green vegetation. Computers and Electronics in Agriculture,2009,65:93-98.
[112]张瑞合,姬长英.计算机视觉技术在番茄收获中的应用.农业机械学报,2001,32(5)：50-52.
[113]Teixido, M., Font, D., Palleja, T., et al. Definition of linear color models in the rgb vector color space to detect red peaches in orchard images taken under natural illumination, Sensors,2012.12:7701-7718.
[114]宋怀波,张传栋,潘景朋,等.基于凸壳的重叠苹果目标分割与重建算法.农业工程学报,2013,
29(3)：163-168.[115]孙俊,武小红,张晓东,等.基于MFICSC算法的生菜图像目标聚类分割.农业工程学报,2012,28(13)：149-153.
[116]Linker R., Cohen, O., Naor, Amos. Determination of the number of green apples in RGB images recorded in orchards. Computers and Electronics in Agriculture,2012,81:45-57.
[117]Yang, Q.H., Qi, L.Y., Bao, G.J.. et al. Cucumber image segmentation algorithm based on rough set theory. New Zealand Journal of Agriclutural Research,2007,50:989-996.
[118]毛亮,薛月菊,孔德运,等.基于稀疏场水平集的荔枝图像分割算法.农业工程学报,2011,27(4)：345-349.
[119]陈科尹,邹湘军,熊俊涛,等.基于视觉显著性改进的水果图像模糊聚类分割算法.农业工程学报,2013,29(6)：157-165.
[120]Tian, L.F., Slaughter, D.C. Environmentally adaptive segmentation algorithm for outdoor image segmentation. Computers and Electronics in Agriculture,1998,21:153-168.
[121]张铁中,林宝龙,高锐.水果采摘机器人视觉系统的目标提取.中国农业大学学报,2004,9(2)：68-72.
[122]Ruiz-Ruiz, G., Gomez-Gil, J., Navas-Gracia, L.M. Testing Different Color Spaces Based on Hue for the Environmentally Adaptive Segmentation Algorithm (EASA). Computers and Electronics in Agriculture,2009, 68:88-96.
[123]周志宇,刘迎春,张建新.基于自适应Canny算子的柑橘边缘检测.农业工程学报,2008,24(3)：21-24.
[124]赵峻颖,潘革生,郭微.边缘检测局部算子在草莓轮廓提取上的应用.农机化研究,2008,6(6)：184-186.
[125]冯青春,袁挺,纪超,等.黄瓜采摘机器人远近景组合闭环定位方法.农业机械学报,2011,42(2)：154-157.
[126]任守纲,马超,谢忠红,等.基于分水岭和梯度的蝴蝶兰图像分割方法.农业工程学报,2012,28(9)：125-129.
[127]赵杰文,刘木华,杨国彬.基于HIS颜色特征的田间成熟番茄识别技术.农业机械学报.2004,35(5)：122-135.
[128]周天娟,张铁中,杨丽.草莓采摘机器人的研究Ⅲ：扫描线填充算法在草莓图像孔洞填充中的应用.中国农业大学学报,2007,12(2)：67-71.
[129]Wang, J.J., Zhao, D.A., Ji, W., et al. Application of Support Vector Machine to Apple Recognition using in Apple Harvesting Robot. Proceedings of the 2009 IEEE International Conference on Information and Automation, Zhuhai/Macau, China,2009,1110-1115.
[130]Arivazhagan, S., Shebiah, R.N., Nidhyanandhan, S.S., et al. Fruit Recognition using Color and Texture Features. Journal of Emerging Trends in Computing and Information Sciences,2010, 1(2):90-94.
[131]熊俊涛,邹湘军,陈丽娟,等.基于机器视觉的自然环境中成熟荔枝识别.农业机械学报,2011,42(9)：162-166.
[132]Rakun, J., Stajnko, D., Zazula, D. Detecting fruits in natural scenes by using spatial-frequency based texture analysis and multiview geometry. Computers and Electronics in Agriculture,2011,76:80-88.
[133]Nuske, S., Achar, S., Bates, T., Narasimhan, S., Singh, S. Yield estimation in vineyards by visual grape detection.2011 IEEE/RSJ International Conference on Intelligent Robots and Systems,2011,2352-2358.
[134]Zhao, P., Cao, J. Weed recognition using image blur information. Biosystems Engineering,2011, 110:198-205.
[135]Wang, Y., Zhu, X.R., Jia, Y.X., et al. Object Recognition on Cotton Harvesting Robot Using Human Visual System.2012 IFIP International Federation for Information Processing, Beijing, China,2012, 368:65-71.
[136]尹建军,毛罕平,王新忠,等.不同生长状态下多目标番茄图像的自动分割方法.农业工程学报,2006,22(10)：149-153.
[137]周天娟,张铁中,杨丽,等.基于数学形态学的相接触草莓果实的分割方法及比较研究.农业工程学报,2007,23(9)：164-168.
[138]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.
[139]Yin, H.P., Chai, Y., Yang, S.X., et al. Ripe tomato extraction for a harvesting robotic system. Proceedings of the 2009 IEEE International Conference on Systems, San Antonio, TX, USA,2009, 2984-2989.
[140]Hannan, M.W., Burks, T.F., Bulanon, D.M. A machine vision algorithm combining adaptive segmentation and shape analysis for orange fruit detection. Agricultural Engineering International:the CIGR Ejournal,2009, Manuscript 1281.6:1-17.
[141]Chen, X.M., Yang, S.X. A practical solution for ripe tomato recognition and localization. J Real-Time Image Proc,2011, Special Issue.
[142]项荣,应义斌,蒋焕煜,等.基于边缘曲率分析的重叠番茄识别.农业机械学报,2012,43(3)：157-162.
[143]姚立健,丁为民,赵三琴,等.广义Hough变换在遮挡图像识别中的应用.农业工程学报,2008,24(12)：97-101.
[144]姚立健,丁为民,张培培,等.基于改进型广义Hough变换的茄子果实位姿识别方法.农业工程 学报.2009.25(12)：128-132.
[145]崔永杰,苏帅,吕志海,等,基于Hough变换的猕猴桃毗邻果实的分离方法.农机化研究,2012.34(12)：166-169.
[146]Feng, J., Wang. S.W.. Liu. Gang., et al. A Separating Method of Adjacent Apples Based on Machine Vision and Chain Code Information.2012 IFIP International Federation for Information Processing.2012, 368:258-267.
[147]Xiang. R.. Jiang, H.Y., Peng. Y.S., et al. Research on recognition method for overlapping tomatoes based on depth map. In 2011 ASABE Annual International Meeting, Louisville. Kentucky, USA, Paper No:1111156.2011,8:319-327.
[148]Reis. M.J.C.S., Morais. R.. Pereira, C., et al. A low-cost system to detect bunches of grapes in natural environment from color Images.13th International Conference on Advances Concepts for Intelligent Vision Systems, Ghent. Belgium.2011.69:92-102.
[149]Reis, M.C.. Morais, R., Pereira. C. et al. Automatic detection of white grapes in natural environment using image processing.6th International Conference on Soft Computing Models in Industrial and Environmental Applications,2011,87:19-26.
[150]Pla, F., Juste. F.. Ferri, F. Feature extraction of spherical objects in image analysis:an application to robotic citrus harvesting. Computers and Electronics in Agriculture,1993,8(1):57-72.
[151]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.2009,,41(9)：148-153.
[152]刘坤,费树岷.汪木兰.等.基于改进随机Hough变换的棉桃识别技术.农业机械学报,2010.41(8)：160-165.
[153]谢忠红,姬长英,郭小清,等.基于改进Hough变换的类圆果实目标检测.农业工程学报,2010,26(7)：157-162.
[154]李听,李立君,高自成,等.改进类圆随机Hough变换及其在油茶果实遮挡识别中的应用.农业工程学报,2013,29(1)：164-]70.
[155]司永胜.乔军,刘刚,等.基于机器视觉的苹果识别和形状特征提.农业机械学报.2009,,40(8)：161-165.
[156]陈玉,赵德安.基于LBM的苹果采摘机器人视觉图像自动修复算法.农业机械学报,2010,41(11)：153-162.
[157]Van Henten, E.J., Van Tuijl, B.A.J., Hoogakker, G.J., et al. An autonomous robot for de-leafing cucumber plants grown in a high-wire cultivation system. Biosystems Engineering,2006,94(3):317-323.
[158]Ota, T.. Bontsema, J., Hayashi, S., et al. Development of a cucumber leaf picking device for greenhouse production. Biosystems Engineering.2007,98:381-390.
[159]Regunathan. M., Lee, W.S. Citrus fruit identification and size determination using machine vision and ultrasonic sensors. In 2005 ASAE Annual International Meeting, Paper No.053017. Tampa, Florida, USA:ASAE.
[160]Yin, H. P., Chai, Y., Yang, S.X., et al. Ripe tomato recognition and localization for a tomato harvesting robotic system.2009 International Conference of Soft Computing and Pattern Recognition,2009:557-562.
[161]Lee. K.H.. Ehsani. R. Comparison of two 2D laser scanners for sensing object distances, shapes, and surface patterns. Computers and Electronics in Agriculture,2008,60:250-262.
[162]刘兆祥,刘刚,乔军.应用于苹果采摘机器人的激光距离传感器.江苏大学学报,2010,31(4)：373-377.
[163]He, B., Liu, G. Apple maturity discrimination and position. Computer and Computing Technologies in Agriculture,2, International Federation for Information Processing,2008,259:969-976.
[164]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.
[165]Irie, N., Taguchi, N., Horie, T., et al. Asparagus harvesting robot coordinated with 3-D vision sensor. IEEE International Conference on Industrial Technology, Gippsland, Australia,2009:1-6.
[166]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.
[167]Busch, A., Palk, P. An Image Processing Approach to Distance Estimation for Automated Strawberry Harvesting.8th International Conference on Image Analysis and Recognition, Burnaby, BC, Canada,2011, 6754:389-396.
[168]Song, J. Target positioning of picking robot fusing laser ranging and vision.2012 Proceedings of the International Conference on Green Communications and Networks,2012,113:985-993.
[169]Zhang, L., Grift, T.E. A monocular vision-based diameter sensor for Miscanthus giganteus. Biosystem Engineering,2012,111:298-304.
[170]蔡健荣,周小军,王锋,等.柑橘采摘机器人障碍物识别技术.农业机械学报,2009,40(11)：171-175.
[171]袁挺,李伟,谭豫之,等.温室环境下黄瓜采摘机器人信息获取.农业机械学报,2009,40(10)：151-155.
[172]项荣,应义斌,蒋焕煜,等.基于双目立体视觉的番茄定位.农业工程学报,2012,28(5)：161-167.
[173]Takahash. T, Zhang, S.H., Fukuchi, H. Measurement of 3-D locations of fruit by binocular stereo vision for apple harvesting in an orchard. In 2002 ASAE Annual International Meeting/CIGR XVth World Congress,2002:1-10.
[174]Kitamura, S., Oka, K. Recognition and cutting system of sweet pepper for picking robot in greenhouse horticulture. Proceeding of the IEEE International Conference on Mechatronics & Automation, Niagara Falls, 2005,1802-1812.
[175]Kitamura, S., Oka, K. Improvement of the ability to recognize sweet pepper for picking robot in greenhouse horticulture. S1CE-ICASE International Joint Conference,2006,353-356.
[176]Wang, H.J., Zou, X.J., Liu, C., et al. Study on a location method for bio-objects in virtual environment based on neural network and fuzzy reasoning. Intelligent Robotics and Applications, Lecture Notes in Computer Science, Singapore,2009,5928:1004-1012.
[177]Li Bin, Wang Haifeng, Huang Wenqian, et al. Construction and in-field experiment of low-cost binocular vision platform for pineapple harvesting robot. Transactions of the Chinese Society of Agricultural Engineering,2012,28(Supp.2):188-192.
[178]Zou, X.J., Zou, H.X., Lu, J. Virtual manipulator-based binocular stereo vision positioning system and errors modeling. Machine Vision and Applications,2012,23:43-63.
[179]Moallem, P., Faez, K. Search space reduction in the edge based stereo matching by context of disparity gradient limit. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis, Pula, Yugoslavia,2001:164-169.
[180]Kweon, J.J., Kang, D.K., Kim, S.D. A stereo matching algorithm using line segment features. Fourth IEEE Region 10 International Conference, Bomboy,1989:589-592.
[181]Goulermas, J.Y., Liatsis, P. Hybrid symbiotic genetic optimisation for robust edge-based stereo correspondence. Pattern Recognition,2001,34:2477-2496.
[182]Wu, M.S., Leou, J.J. A bipartite matching approach to feature correspondence in stereo vision. Pattern Recognition Letters,1995,16:23-31.
[183]Jawahar, C.V., Narayanan, P.J. An adaptive multi-feature correspondence algorithm for stereo using dynamic programming. Pattern Recognition Letters,2002,23:549-556.
[184]Wei Y.C., Quan L. Region-based progressive stereo matching. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2004,1:1106-1113.
[185]Takahashi T., Zhang S.H., Fukuchi, H. Acquisition of 3-D Information by binocular stereo vision for vehicle navigation through an orchard. Proceedings in Automation Technology for Off-Road Equipment, Chicago. Illinois, USA,2002:337-346.
[186]毛罕平,李明喜.基于多源机器视觉信息融合的番茄目标匹配.农业工程学报,2009,25(10)：142-147.
[187]周俊.刘锐,张高阳.基于立体视觉的水果采摘机器人系统设计.农业机械学报,2010,41(6)：158-162.
[188]Stefano, L.D., Marchionni, M., Mattoccia. S. A fast area-based stereo matching algorithm. Image and Vision Computing,2004,22:983-1005.
[189]Wang, K. Adaptive stereo matching algorithm based on edge detection.2004 International Conference on image Processing,2004,2:1345-1348.
[190]Veksler, O. Stereo Matching by compact windows via minimum cycle. Eighth IEEE International Conference on Computer Vision, Vancouver, Canda,2001,1:540-547.
[191]Kanade, T., Okutornu, M. A stereo matching algorithm with an adaptive window:theory and experiment. IEEE Trans. PAMI,1994(9):920-932.
[192]Cheng, W., Liu, J., Zhang, J.X. A binocular computer vision system for aerial image pairs.3rd International Conference on Signal Processing, Beijing, China,1996,2:954-957.
[193]Tang, L., Wu, C.K., Chen, Z.Z. Image dense matching based on region growth with adaptive window. Pattern Recognition Letters,2002,23:1169-1178.
[194]云挺,顾磊,吴慧中.双目立体视觉中的区域匹配新方法.工程图学学报,2008,2：116-124.
[195]Xiang, R., Ying, Y.B., Jiang H.Y., et al.3-D location of tomato based on binocular stereo vision for tomato harvesting robot. In 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies, Qingdao, China,2010:76582Z1-76582Z7.
[196]李理,殷国富,刘柯岐.田问果蔬采摘机器人视觉传感器设计与试验.农业机械学报,2010,41(5)：152-157.
[197]Bias, M.R., Blanke, M. Stereo Vision with Texture Learning for Fault-Tolerant Automatic Baling. Computers and Electronics in Agriculture,2011,75:159-168.
[198]Xu, Y.J., Du, L.M., Hou, Z.Q., et al. A grouped scale-adaptive phase-based stereo matching method.4th International Conference on Signal Processing, Beijing, China,1998,2:893-896.
[199]Ulusoy, I., Hancock, E.R. A statistical approach to sparse multi-scale phase-based stereo. Pattern Recognition,2006,1-17.
[200]饶秀勤.基于机器视觉的水果品质实时检测与分级生产线的关键技术研究[博士论文],杭州,浙江大学,2007,65-66.
[201]Point Grey Research, Procuct Catalog-Stereo, Canada, Point Grey Research, Inc,2009,2-2.
[202]吴遵高.测量系统分析.北京,中国标准出版社,2004,51-51.
[2]汤修映,张铁中.果蔬收获机器人研究综述.机器人,2005,27(1)：90-96.
[3]Jimenez A.R... Ceres R., Pons J.L. A survey of computer vision methods for locating fruit on trees. Transaction of the ASAE,2000,43(6):1911-1920.
[4]Li. P.L., Lee, S.H., Hsu, H.Y. Review on fruit harvesting method for potential use of automatic fruit harvesting systems. Procedia Engineering,2011,23:351-366.
[5]Torregrosa, A., Orti. E.. Martin, B., et al. Mechanical harvesting of oranges and mandarins in Spain. Biosystems Engineering.2009,104:18-24.
[6]陈度,杜小强,王书茂,等.振动式果品收获技术机理分析及研究进展.农业工程学报,2011,27(8)：195-200.
[7]Du, X.Q.. Chen. D., Zhang. Q.. et al. Dynamic responses of sweet cherry trees under vibrator)'excitations. Biosystems Engineering,2012,111:305-314.
[8]赵匀,武传字,胡旭东,等.农业机器人的研究进展及存在的问题.农业工程学报,2003,19(1)：20-24.
[9]徐丽明.张铁中.果蔬果实收获机器人的研究现状及关键问题和对策.农业二程学报,2004,20(5)：38-42.
[10]张立彬.计时鸣,胥芳,等.农业机器人的主要应用领域和关键技术.浙江工业大学学报,2002.30(1)：36-41.
[]1]宋健,张铁中,徐丽明,等.果蔬采摘机器人研究进展与展望.农业机械学报,2006,37(5)：158-162.
[12]Van Henten, E.J., Hemming, J., Van Tuijl, B.A.J., et al. An autonomous robot for harvesting cucumbers in greenhouses. Autonomous Robots.2002,13:241-258.
[13]Van Henten, E.J., Van Tuijl, B.A.J., Hemming, J., et al. Field test of an autonomous cucumber picking robot. Biosystems Engineering,2003,86(3):305-313.
[14]Van Henten, E.J.. Hemming, J., Van Tuijl, B.A.J., et al. Collision-free Motion Planning for a Cucumber Picking Robot. Biosystems Engineering.2003,86 (2):135-144.
[15]Van Henten, E.J.. Van't Slot. D.A., Hol, C.W.J., et al. Optimal Design of a Cucumber Harvesting Robot. Proceedings of AgEng 2006, Bonn, Germany,2006, Paper nr.802.
[16]Van Henten, E.J., Van Willigenburg, L.G., Van't Slot, D. Optimal design of a cucumber harvesting robot. Computers and Electronics in Agriculture,2009.65:247-257.
[17]纪超,冯青春,袁挺,等.温室黄瓜采摘机器人系统研制及性能分析.机器人,2011.33(6)：726-730.
[18]Hayashi, S., Ganno. K.. Ishii, Y., et al. Robotic harvesting system for eggplants. JARQ,2002, 36:163-168.
[19]Reed, J.N., Miles, S.J., Butler, J., et al. Automatic mushroom harvester development. J. agric. Engng Res.,2001,78(1):15-23.
[20]周云山,李强,李红英,等.计算机视觉在蘑菇采摘机器人上的应用.农业工程学报,1995,16(4)：27-32.
[21]Plebe, A., Grasso, G. Localization of spherical fruits for robotic harvesting. Machine Vision and Applications,2001,13(2):70-79.
[22]Zhao, D.A., Lu, J.D., Ji, W., et al. Design and control of an apple harvesting robot. Biosystems Engineering,2011,110:112-122.
[23]顾宝兴,姬长英,王海青,等.智能移动水果采摘机器人设计与试验.农业机械学报,2012,43(6)：153-160.
[24]Kondo, N., Ninomiya, K., Hayashi, S., et al. A new challenge of robot for harvesting strawberry grown on table top culture. In 2005 ASAE Annual International Meeting, Paper No.053138. Tampa, Florida, USA:ASAE.
[25]Hayashi S., Shigematsu, K.., Yamamoto, S., et al. Evaluation of a strawberry-harvesting robot in a field test. Biosystems Engineering,2009:1-12.
[26]Guo, F., Cao, Q.X., Cui, YJ, et al. Fruit location and stem detection method for strawberry harvesting robot. Transactions of the CSAE,2008,24(10):89-94.
[27]张凯良,杨丽,王粮局,等.高架草莓采摘机器人设计与试验.农业机械学报,2012,43(9)：165-172.
[28]Tanigaki, K., Fujiura, T., Akase, A., et al. Cherry-harvesting robot. Computers and Electronics in Agriculture,2008,63(1):65-72.
[29]Edan Y., Rogozin D., Flash T., et al. Robotic melon harvesting. IEEE Transactions on Robotics and Automation,2000,16(6):831-834.
[30]李斌,Ning, W.,汪懋华,等.基于单目视觉的田间菠萝果实识别.农业工程学报,2010,26(10)：345-349.
[31]Erdogan, D., Guner, M., Dursun, E., et al. Mechanical harvesting of apricots. Biosystems Engineering, 2003,85(1):19-28.
[32]Scarfe, A.J., Flemmer, R.C., Bakker, H.H., et al. Development ofan autonomous kiwifruit picking robot. Proceedings of the 4th International Conference on Autonomous Robots and Agents, Wellington, New Zealand,2009,380-384.
[33]Aljanobi, A. A., Al-hamed, S.A., Al-Suhaibani, S. A. A setup of mobile robotic unit for fruit harvesting. 19th International Workshop on Robotics in Alpe-Adria-Danube Region-RAAD 2010, Budapest, Hungary,
2010,105-108.
[34]Kohan, A., Borghaee, A.M., Yazdi, M., et al. Robotic harvesting of rosa damascena using stereoscopic machine vision. World Applied Sciences Journal,2011,12 (2):231-237.
[35]Cho, S. I., Chang, S. J., Kim, Y. Y., et al. Development of a three-degrees-of-freedom robot for harvesting lettuce using machine vision and fuzzy logic control. Biosystems Engineering,2002,82 (2): 143-149.
[36]Rath, T., Kawollek, M. Robotic harvesting of Gerbera Jamesonii based on detection and three-dimensional modeling of cut flower pedicels. Computers and Electronics in Agriculture,2009, 66:85-92.
[37]Dong. F., Heinemann. W., Kasper, R. Development of a row guidance system for an autonomous robot for white asparagus harvesting. Computers and Electronics in Agriculture.2011.79:216-225.
[38]Chatzimichali, A. P.. Georgilas. I.P.. Tourassis, V.D. Design of an advanced prototype robot for white asparagus harvesting.2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore.2009.887-892.
[39]Kitamura, S., Oka, K., Ikutomo, K., et al. A distinction method for fruit of sweet pepper using reflection of led light. International Conference on Instrumentation Control and Information Technology, Chofu, Tokyo, Japan,2008:491-494.
[40]Foglia. M.M., Reina, G. Agricultural robot for radicchio harvesting. Journal of Field Robotics,2006, 23(6/7):363-377.
[41]Polat, R., Gezer, I., Guner, M., et al.Mechanical harvesting of pistachio nuts. Journal of Food Engineering,2007,79:1131-1135.
[42]陆怀民.林木球果采摘机器人设计与试验.农业机械学报,2001.32(6)：52-58.
[43]Ji, W., Zhao, D.A., Cheng, F.Y., et al. Automatic recognition vision system guided for apple harvesting robot. Computers and Electrical Engineering,2012,38:1186-1195.
[44]Gotou, K., Fujiura, T., Nishiura. Y., et al.3-D vision system of tomato production robot. Proceedings of the 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics,2003,2:1210-1215.
[45]Arima, S., Kondo. N., Monta. M. Strawberry Harvesting Robot on Table-top Culture. In 2004 ASAE/CSAE Annual International Meeting, Paper No.043089. Ottawa, Canada, USA:ASAE.
[46]Kondo, N., Ting. K.C. Robotics for plant production. Artificial Intelligence Review,1998, 12(1-3):227-243.
[47]Jimenez. A.R., Jain, A.K., Ceres, R. Automatic fruit recognition:A survey and new results using Range/Attenuation images. Pattern Recognition,1999.32(10):1719-1736.
[48]Jimenez. A.R., Ceres, R., Pons, J.L. A vision system based on a laser range-finder applied to robotic fruit harvesting. Machine Vision and Applications,2000,11:321-329.
[49]Stajnko. D., Lakota, M., Hocevar, M. Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging. Computers and Electronics in Agriculture,2004,42:31-42.
[50]徐惠荣,应义斌.红外热成像在树上柑桔识别中的应用研究.红外与毫米波学报,2004,23(5)：353-356.
[51]Bulanona, D.M., Burksa, T.F., Alchanatis, V. Study on temporal variation in citrus canopy using thermal imaging for citrus fruit detection. Biosystems engineering,2008,101:161-171.
[52]毛罕平,李明喜,张艳诚.基于多光谱图像融合和形态重构的图像分割方法.农业工程学报,2008,24(6)：174-178.
[53]Bulanon. D.M., Burks, T.F., Alchanatis, V. Image fusion of visible and thermal images for fruit detection. Biosystems Engineering,2009,103:12-22.
[54]俞晓磊,赵志敏.光谱导航技术在果树果实定位中的应用.光谱学与光谱分析,2009. 30(3)：770-773.
[55]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.
[56]Bulanon, D.M., Burks, T.F., Alchanatis, V. A multispectral imaging analysis for enhancing cirtrus detection. Environ. Control Biol.,2010,48(2):81-91.
[57]Okamoto, H., Lee, W.S. Green citrus detection using hyperspectral imaging. Computers and Electronics in Agriculture,2009,66:201-208.
[58]Yang, L., Dickinson, J., Wu, Q.M.J., et al. A fruit recognition method for automatic harvesting.14th International Conference on Mechatronics and Machine Vision in Practice, Xiamen, China,2007,152-157.
[59]Kurtulmus, F., Lee, W. S., Vardar, A. Green citrus detection using'eigenfruit', color and circular Gabor texture features under natural outdoor conditions. Computers and Electronics in Agriculture,2011, 78:140-149.
[60]Rajendra, P., Mitsutaka, K., Kazunori, N., et al. Shading compensation methods for robots to harvest strawberries in tabletop culture.2011 International Symposium on System Integration,2011,172-177.
[61]Arefi, A., Motlagh, A. M., Teimourlou, R. F. A segmentation algorithm for the automatic recognition of tomato at harvest. Journal of Food Agriculture & Environment,2010,8(3-4):815-819.
[62]蔡健荣,周小军,李玉良,等.基于机器视觉自然场景下成熟柑橘识别.农业工程学报,2008,24(1)：175-178.
[63]Li, P.L., Lee, S.H., Hsu, H.Y. Study on citrus fruit image data separability by segmentation. Procedia Engineering,2011,23:408-416.
[64]Mao, W., Jia, B., Zhang, X., et al. Detection and position method of apple tree image. In International Federation for Information Processing, Beijing, China,2009:1039-1048.
[65]Yin, J.J., Mao, H.P., Zhong. S.Y. Segmentation methods of fruit image based on color difference. Journal of Communication and Computer,2009,6(7):40-45.
[66]Bulanon, D.M., Kataoka, T., Okamoto, H., et al. Determining the 3-D location of the apple fruit during harvest. Automation Technology for Off-Road Equipment Proceedings, Hyatt Regency Chicago, Chicago, Illinois, USA,2004:91-97.
[67]Gu, H., Lu, Y.Y., Lou, J.L., et al. Recognition and location of fruit objects based on machine vision. In 16th International Conference on Artificial Reality and Telexistence, Hangzhou, China,2006:785-795.
[68]李寒,王库,曹倩,等.基于机器视觉的番茄多目标提取与匹配.农业工程学报,2012,28(5)：168-172.
[69]荀一,陈晓,李伟,等.基于轮廓曲率的树上苹果自动识别.江苏大学学报：自然科学版,2007,(286)：461-464.
[70]Tarrio, Paula., Bernardos, A.M., Casar, J.R., et al. A harvesting robot for small fruit in bunches based on 3-d stereoscopic vision.4th World Congress on Computers in Agriculture and Natural Resources, Orlando, Florida, USA,2006:270-275.
[71]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.
[72]Yin, H.P., Chai, Y., Yang, S.X., et al. Ripe tomato detection for robotic vision harvesting systems in greenhouses. Transactions of the ASABE,2011,54(4):1539-1546.
[73]Bulanon, D.M., Kataoka, T., Ota, Y., et al. A segmentation algorithm for the automatic recognition of Fuji apples at harvest. Biosystems Engineering,2002,83(4):405-412.
[74]Bulanon, D.M., Kataoka, T., Ota, Y., et al. A machine vision system for the apple harvesting robot. Agricultural Engineering International:the CIGR Journal of Scientific Research and Development, Manuscript PM 01006, Vol. Ill,2001.
[75]Bulanon, D.M., Kataoka, T., Okamoto, H., et al. Development of a real-time machine vision system for the apple harvesting robot. SICE Annual Conference in Sapporo, Hokkaido Institute of Tecnology, Japan. 2004,1:595-598.
[76]杨旭强,冯勇,刘洪臣.一种基于HSI颜色模型的目标提取方法.光学技术,2006,32(2)：290-292.
[77]赵金英,张铁中,杨丽.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.
[78]韦皆顶,费树岷,汪木兰.基于HSV彩色模型的自然场景下棉花图像分割策略研究.棉花学报,2008,20(1)：34-38.
[79]熊俊涛,邹湘军,陈丽娟,等.采摘机械手对扰动荔枝的视觉定位.农业工程学报,2012,28(14)：36-41.
[80]郭艾侠,邹湘军,朱梦思,等.基于探索性分析的的荔枝果及结果母枝颜色特征分析与识别[J].农业工程学报,2013,29(4)：191-198.
[81]郑小东,赵杰文,刘木华.基于双目立体视觉的番茄识别与定位技术.计算机工程,2004,30(22)：171-173.
[82]蔡健荣,赵杰文.自然场景下成熟水果的计算机视觉识别.农业机械学报,2005,36(2)：61-64.
[83]张铁中,陈利兵,宋健.草莓采摘机器人的研究Ⅱ：基于图像的草莓重心位置和采摘点的确定.中国农业大学学报,2005,10(1)：48-51.
[84]徐惠荣,叶尊忠,应义斌.基于彩色信息的树上柑橘识别研究.农业工程学报,2005,21(5)：98-101.
[85]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,2006,1(1)：84-86.
[86]尹建军,王新忠.毛罕平.RGB与HSI颜色空间下番茄图像分割的对比研究.农机化研究,2006,11：171-174.
[87]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.
[88]宋健.自动阈值法在生长环境茄子图像分割中的应用.潍坊学院学报,2007,7(6)：6-9.
[89]申川.基于双目立体视觉系统的设施农业作物位置信息获取[硕士学位论文].杭州,浙江大学,2006.
[90]蒋焕煜,彭永石,由川,等.基于双目立体视觉技术的成熟番茄识别与定位.农业工程学报,2008,24(8)：279-283.
[91]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.
[92]Jiang H.Y., Peng, Y.S.,Ying Y.B. Measurement of 3-D locations of ripe tomato by binocular stereo vision for tomato harvesting. In 2008 ASABE Annual International Meeting, Rhode Island, USA,2008,11: 6616-6621.
[93]彭永石.基于双目立体视觉技术的果蔬采摘机器人视觉系统的研究[硕士学位论文].杭州,浙江大学,2008.
[94]Mao, W.H., Ji, B.P., Zhan, J.C., et al. Apple location method for the apple harvesting robot.2nd International Congress on Image and Signal Processing, Tianjin, China,2009,1-5.
[95]宋健.基于神经网络的茄子采摘机器人视觉识别方法.潍坊学院学报,2011,11(6)：90-93.
[96]Arefi, A., Motlagh, A. M., Mollazade, K., et al. Recognition and localization of ripen tomato based on machine vision. Australian Journal of Crop Science,2011,5(10):1144-1149.
[97]吕继东,赵德安,姬伟等.采摘机器人振荡果实动态识别,农业机械学报,2012,43(5)：173-196.
[98]钱建平,杨信廷,吴晓明,等.自然场景下基于混合颜色空间的成熟期苹果识别方法[J].农业工程学报,2012,28(17)：137-142.
[99]Qi, L.Y., Yang, Q.H., Bao, G.J., et al. A dynamic threshold segmentation algorithm for cucumber identification in greenhouse.2nd International Congress on Image and Signal Processing, Tianjin, China, 2009,1-4.
[100]李二超,李战明,刘徽容.基于颜色和纹理特征的黄瓜果实图像分割.光学技术,2009,35(4)：529-531.
[101]Xiang, R., Ying, Y. B., Jiang, H. Y. Research on image segmentation methods of tomato in nature conditions. In 2011 4th International Congress on Image and Signal Processing, Shanghai, China, 2011:1268-1272.
[102]Patel, H.N., Jain, R.K., Joshi, M.V. Fruit detection using improved multiple features based algorithm. International Journal of Computer Applications,2011,13(2):1-5.
[103]Xu, Y.W., Imou, K., Kaizu, Y., et al. Two-stage approach for detecting slightly overlapping strawberries using HOG descriptor. Biosystems Engineering,2013,115:144-153.
[104]Philipp, I., Rath, T. Improving plant discrimination in image processing by use of different colour space transformations. Computers and Electronics in Agriculture,2002,35:1-15.
[105]Wang, X.Z., Mao, H.P., Han, X., et al. Vision-based judgment of tomato maturity under growth conditions. African Journal of Biotechnology,2011,10(18):3616-3623.
[106]李听,李立君,高自成,等.基于偏好人工免疫网络多特征融合的油茶果图像识别.农业工程学报,2012,28(14)：133-137.
[107]Pastrana, J. C., Rath, T. Novel image processing approach for solving the overlapping problem in agriculture. Biosystems Engineering,2013,115:106-115.
[108]Song, J. Image segmentation of eggplants in growing environment based on improved BP neural network. Advances in Science and Engineering,2011, (40-41):599-603.
[109]杨国彬,赵杰文,向忠平.利用计算机视觉对自然背景下西红柿进行判别.农机化研究,2003, 1(1)：60-62.
[110]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.
[111]Zheng, L.Y.. Zhang, J.T., Wang, Q.Y. Mean-shift-based color segmentation of images containing green vegetation. Computers and Electronics in Agriculture,2009,65:93-98.
[112]张瑞合,姬长英.计算机视觉技术在番茄收获中的应用.农业机械学报,2001,32(5)：50-52.
[113]Teixido, M., Font, D., Palleja, T., et al. Definition of linear color models in the rgb vector color space to detect red peaches in orchard images taken under natural illumination, Sensors,2012.12:7701-7718.
[114]宋怀波,张传栋,潘景朋,等.基于凸壳的重叠苹果目标分割与重建算法.农业工程学报,2013,
29(3)：163-168.[115]孙俊,武小红,张晓东,等.基于MFICSC算法的生菜图像目标聚类分割.农业工程学报,2012,28(13)：149-153.
[116]Linker R., Cohen, O., Naor, Amos. Determination of the number of green apples in RGB images recorded in orchards. Computers and Electronics in Agriculture,2012,81:45-57.
[117]Yang, Q.H., Qi, L.Y., Bao, G.J.. et al. Cucumber image segmentation algorithm based on rough set theory. New Zealand Journal of Agriclutural Research,2007,50:989-996.
[118]毛亮,薛月菊,孔德运,等.基于稀疏场水平集的荔枝图像分割算法.农业工程学报,2011,27(4)：345-349.
[119]陈科尹,邹湘军,熊俊涛,等.基于视觉显著性改进的水果图像模糊聚类分割算法.农业工程学报,2013,29(6)：157-165.
[120]Tian, L.F., Slaughter, D.C. Environmentally adaptive segmentation algorithm for outdoor image segmentation. Computers and Electronics in Agriculture,1998,21:153-168.
[121]张铁中,林宝龙,高锐.水果采摘机器人视觉系统的目标提取.中国农业大学学报,2004,9(2)：68-72.
[122]Ruiz-Ruiz, G., Gomez-Gil, J., Navas-Gracia, L.M. Testing Different Color Spaces Based on Hue for the Environmentally Adaptive Segmentation Algorithm (EASA). Computers and Electronics in Agriculture,2009, 68:88-96.
[123]周志宇,刘迎春,张建新.基于自适应Canny算子的柑橘边缘检测.农业工程学报,2008,24(3)：21-24.
[124]赵峻颖,潘革生,郭微.边缘检测局部算子在草莓轮廓提取上的应用.农机化研究,2008,6(6)：184-186.
[125]冯青春,袁挺,纪超,等.黄瓜采摘机器人远近景组合闭环定位方法.农业机械学报,2011,42(2)：154-157.
[126]任守纲,马超,谢忠红,等.基于分水岭和梯度的蝴蝶兰图像分割方法.农业工程学报,2012,28(9)：125-129.
[127]赵杰文,刘木华,杨国彬.基于HIS颜色特征的田间成熟番茄识别技术.农业机械学报.2004,35(5)：122-135.
[128]周天娟,张铁中,杨丽.草莓采摘机器人的研究Ⅲ：扫描线填充算法在草莓图像孔洞填充中的应用.中国农业大学学报,2007,12(2)：67-71.
[129]Wang, J.J., Zhao, D.A., Ji, W., et al. Application of Support Vector Machine to Apple Recognition using in Apple Harvesting Robot. Proceedings of the 2009 IEEE International Conference on Information and Automation, Zhuhai/Macau, China,2009,1110-1115.
[130]Arivazhagan, S., Shebiah, R.N., Nidhyanandhan, S.S., et al. Fruit Recognition using Color and Texture Features. Journal of Emerging Trends in Computing and Information Sciences,2010, 1(2):90-94.
[131]熊俊涛,邹湘军,陈丽娟,等.基于机器视觉的自然环境中成熟荔枝识别.农业机械学报,2011,42(9)：162-166.
[132]Rakun, J., Stajnko, D., Zazula, D. Detecting fruits in natural scenes by using spatial-frequency based texture analysis and multiview geometry. Computers and Electronics in Agriculture,2011,76:80-88.
[133]Nuske, S., Achar, S., Bates, T., Narasimhan, S., Singh, S. Yield estimation in vineyards by visual grape detection.2011 IEEE/RSJ International Conference on Intelligent Robots and Systems,2011,2352-2358.
[134]Zhao, P., Cao, J. Weed recognition using image blur information. Biosystems Engineering,2011, 110:198-205.
[135]Wang, Y., Zhu, X.R., Jia, Y.X., et al. Object Recognition on Cotton Harvesting Robot Using Human Visual System.2012 IFIP International Federation for Information Processing, Beijing, China,2012, 368:65-71.
[136]尹建军,毛罕平,王新忠,等.不同生长状态下多目标番茄图像的自动分割方法.农业工程学报,2006,22(10)：149-153.
[137]周天娟,张铁中,杨丽,等.基于数学形态学的相接触草莓果实的分割方法及比较研究.农业工程学报,2007,23(9)：164-168.
[138]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.
[139]Yin, H.P., Chai, Y., Yang, S.X., et al. Ripe tomato extraction for a harvesting robotic system. Proceedings of the 2009 IEEE International Conference on Systems, San Antonio, TX, USA,2009, 2984-2989.
[140]Hannan, M.W., Burks, T.F., Bulanon, D.M. A machine vision algorithm combining adaptive segmentation and shape analysis for orange fruit detection. Agricultural Engineering International:the CIGR Ejournal,2009, Manuscript 1281.6:1-17.
[141]Chen, X.M., Yang, S.X. A practical solution for ripe tomato recognition and localization. J Real-Time Image Proc,2011, Special Issue.
[142]项荣,应义斌,蒋焕煜,等.基于边缘曲率分析的重叠番茄识别.农业机械学报,2012,43(3)：157-162.
[143]姚立健,丁为民,赵三琴,等.广义Hough变换在遮挡图像识别中的应用.农业工程学报,2008,24(12)：97-101.
[144]姚立健,丁为民,张培培,等.基于改进型广义Hough变换的茄子果实位姿识别方法.农业工程 学报.2009.25(12)：128-132.
[145]崔永杰,苏帅,吕志海,等,基于Hough变换的猕猴桃毗邻果实的分离方法.农机化研究,2012.34(12)：166-169.
[146]Feng, J., Wang. S.W.. Liu. Gang., et al. A Separating Method of Adjacent Apples Based on Machine Vision and Chain Code Information.2012 IFIP International Federation for Information Processing.2012, 368:258-267.
[147]Xiang. R.. Jiang, H.Y., Peng. Y.S., et al. Research on recognition method for overlapping tomatoes based on depth map. In 2011 ASABE Annual International Meeting, Louisville. Kentucky, USA, Paper No:1111156.2011,8:319-327.
[148]Reis. M.J.C.S., Morais. R.. Pereira, C., et al. A low-cost system to detect bunches of grapes in natural environment from color Images.13th International Conference on Advances Concepts for Intelligent Vision Systems, Ghent. Belgium.2011.69:92-102.
[149]Reis, M.C.. Morais, R., Pereira. C. et al. Automatic detection of white grapes in natural environment using image processing.6th International Conference on Soft Computing Models in Industrial and Environmental Applications,2011,87:19-26.
[150]Pla, F., Juste. F.. Ferri, F. Feature extraction of spherical objects in image analysis:an application to robotic citrus harvesting. Computers and Electronics in Agriculture,1993,8(1):57-72.
[151]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.2009,,41(9)：148-153.
[152]刘坤,费树岷.汪木兰.等.基于改进随机Hough变换的棉桃识别技术.农业机械学报,2010.41(8)：160-165.
[153]谢忠红,姬长英,郭小清,等.基于改进Hough变换的类圆果实目标检测.农业工程学报,2010,26(7)：157-162.
[154]李听,李立君,高自成,等.改进类圆随机Hough变换及其在油茶果实遮挡识别中的应用.农业工程学报,2013,29(1)：164-]70.
[155]司永胜.乔军,刘刚,等.基于机器视觉的苹果识别和形状特征提.农业机械学报.2009,,40(8)：161-165.
[156]陈玉,赵德安.基于LBM的苹果采摘机器人视觉图像自动修复算法.农业机械学报,2010,41(11)：153-162.
[157]Van Henten, E.J., Van Tuijl, B.A.J., Hoogakker, G.J., et al. An autonomous robot for de-leafing cucumber plants grown in a high-wire cultivation system. Biosystems Engineering,2006,94(3):317-323.
[158]Ota, T.. Bontsema, J., Hayashi, S., et al. Development of a cucumber leaf picking device for greenhouse production. Biosystems Engineering.2007,98:381-390.
[159]Regunathan. M., Lee, W.S. Citrus fruit identification and size determination using machine vision and ultrasonic sensors. In 2005 ASAE Annual International Meeting, Paper No.053017. Tampa, Florida, USA:ASAE.
[160]Yin, H. P., Chai, Y., Yang, S.X., et al. Ripe tomato recognition and localization for a tomato harvesting robotic system.2009 International Conference of Soft Computing and Pattern Recognition,2009:557-562.
[161]Lee. K.H.. Ehsani. R. Comparison of two 2D laser scanners for sensing object distances, shapes, and surface patterns. Computers and Electronics in Agriculture,2008,60:250-262.
[162]刘兆祥,刘刚,乔军.应用于苹果采摘机器人的激光距离传感器.江苏大学学报,2010,31(4)：373-377.
[163]He, B., Liu, G. Apple maturity discrimination and position. Computer and Computing Technologies in Agriculture,2, International Federation for Information Processing,2008,259:969-976.
[164]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.
[165]Irie, N., Taguchi, N., Horie, T., et al. Asparagus harvesting robot coordinated with 3-D vision sensor. IEEE International Conference on Industrial Technology, Gippsland, Australia,2009:1-6.
[166]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.
[167]Busch, A., Palk, P. An Image Processing Approach to Distance Estimation for Automated Strawberry Harvesting.8th International Conference on Image Analysis and Recognition, Burnaby, BC, Canada,2011, 6754:389-396.
[168]Song, J. Target positioning of picking robot fusing laser ranging and vision.2012 Proceedings of the International Conference on Green Communications and Networks,2012,113:985-993.
[169]Zhang, L., Grift, T.E. A monocular vision-based diameter sensor for Miscanthus giganteus. Biosystem Engineering,2012,111:298-304.
[170]蔡健荣,周小军,王锋,等.柑橘采摘机器人障碍物识别技术.农业机械学报,2009,40(11)：171-175.
[171]袁挺,李伟,谭豫之,等.温室环境下黄瓜采摘机器人信息获取.农业机械学报,2009,40(10)：151-155.
[172]项荣,应义斌,蒋焕煜,等.基于双目立体视觉的番茄定位.农业工程学报,2012,28(5)：161-167.
[173]Takahash. T, Zhang, S.H., Fukuchi, H. Measurement of 3-D locations of fruit by binocular stereo vision for apple harvesting in an orchard. In 2002 ASAE Annual International Meeting/CIGR XVth World Congress,2002:1-10.
[174]Kitamura, S., Oka, K. Recognition and cutting system of sweet pepper for picking robot in greenhouse horticulture. Proceeding of the IEEE International Conference on Mechatronics & Automation, Niagara Falls, 2005,1802-1812.
[175]Kitamura, S., Oka, K. Improvement of the ability to recognize sweet pepper for picking robot in greenhouse horticulture. S1CE-ICASE International Joint Conference,2006,353-356.
[176]Wang, H.J., Zou, X.J., Liu, C., et al. Study on a location method for bio-objects in virtual environment based on neural network and fuzzy reasoning. Intelligent Robotics and Applications, Lecture Notes in Computer Science, Singapore,2009,5928:1004-1012.
[177]Li Bin, Wang Haifeng, Huang Wenqian, et al. Construction and in-field experiment of low-cost binocular vision platform for pineapple harvesting robot. Transactions of the Chinese Society of Agricultural Engineering,2012,28(Supp.2):188-192.
[178]Zou, X.J., Zou, H.X., Lu, J. Virtual manipulator-based binocular stereo vision positioning system and errors modeling. Machine Vision and Applications,2012,23:43-63.
[179]Moallem, P., Faez, K. Search space reduction in the edge based stereo matching by context of disparity gradient limit. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis, Pula, Yugoslavia,2001:164-169.
[180]Kweon, J.J., Kang, D.K., Kim, S.D. A stereo matching algorithm using line segment features. Fourth IEEE Region 10 International Conference, Bomboy,1989:589-592.
[181]Goulermas, J.Y., Liatsis, P. Hybrid symbiotic genetic optimisation for robust edge-based stereo correspondence. Pattern Recognition,2001,34:2477-2496.
[182]Wu, M.S., Leou, J.J. A bipartite matching approach to feature correspondence in stereo vision. Pattern Recognition Letters,1995,16:23-31.
[183]Jawahar, C.V., Narayanan, P.J. An adaptive multi-feature correspondence algorithm for stereo using dynamic programming. Pattern Recognition Letters,2002,23:549-556.
[184]Wei Y.C., Quan L. Region-based progressive stereo matching. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2004,1:1106-1113.
[185]Takahashi T., Zhang S.H., Fukuchi, H. Acquisition of 3-D Information by binocular stereo vision for vehicle navigation through an orchard. Proceedings in Automation Technology for Off-Road Equipment, Chicago. Illinois, USA,2002:337-346.
[186]毛罕平,李明喜.基于多源机器视觉信息融合的番茄目标匹配.农业工程学报,2009,25(10)：142-147.
[187]周俊.刘锐,张高阳.基于立体视觉的水果采摘机器人系统设计.农业机械学报,2010,41(6)：158-162.
[188]Stefano, L.D., Marchionni, M., Mattoccia. S. A fast area-based stereo matching algorithm. Image and Vision Computing,2004,22:983-1005.
[189]Wang, K. Adaptive stereo matching algorithm based on edge detection.2004 International Conference on image Processing,2004,2:1345-1348.
[190]Veksler, O. Stereo Matching by compact windows via minimum cycle. Eighth IEEE International Conference on Computer Vision, Vancouver, Canda,2001,1:540-547.
[191]Kanade, T., Okutornu, M. A stereo matching algorithm with an adaptive window:theory and experiment. IEEE Trans. PAMI,1994(9):920-932.
[192]Cheng, W., Liu, J., Zhang, J.X. A binocular computer vision system for aerial image pairs.3rd International Conference on Signal Processing, Beijing, China,1996,2:954-957.
[193]Tang, L., Wu, C.K., Chen, Z.Z. Image dense matching based on region growth with adaptive window. Pattern Recognition Letters,2002,23:1169-1178.
[194]云挺,顾磊,吴慧中.双目立体视觉中的区域匹配新方法.工程图学学报,2008,2：116-124.
[195]Xiang, R., Ying, Y.B., Jiang H.Y., et al.3-D location of tomato based on binocular stereo vision for tomato harvesting robot. In 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies, Qingdao, China,2010:76582Z1-76582Z7.
[196]李理,殷国富,刘柯岐.田问果蔬采摘机器人视觉传感器设计与试验.农业机械学报,2010,41(5)：152-157.
[197]Bias, M.R., Blanke, M. Stereo Vision with Texture Learning for Fault-Tolerant Automatic Baling. Computers and Electronics in Agriculture,2011,75:159-168.
[198]Xu, Y.J., Du, L.M., Hou, Z.Q., et al. A grouped scale-adaptive phase-based stereo matching method.4th International Conference on Signal Processing, Beijing, China,1998,2:893-896.
[199]Ulusoy, I., Hancock, E.R. A statistical approach to sparse multi-scale phase-based stereo. Pattern Recognition,2006,1-17.
[200]饶秀勤.基于机器视觉的水果品质实时检测与分级生产线的关键技术研究[博士论文],杭州,浙江大学,2007,65-66.
[201]Point Grey Research, Procuct Catalog-Stereo, Canada, Point Grey Research, Inc,2009,2-2.
[202]吴遵高.测量系统分析.北京,中国标准出版社,2004,51-51.