温室番茄收获机器人选择性收获作业信息获取与路径规划研究
|
摘要
果蔬收获机器人关键技术的研发,是我国设施农业规模化、工业化进程中的必要环节。本研究针对番茄收获机器人作业信息获取技术开展研究,对于提高我国智能化设施农业装备的研发水平具有重要的理论意义和实际价值。
本研究以温室典型瓜果作物——番茄为研究对象,对番茄收获机器人选择性采收中作业信息快速获取问题进行了研究,为了获取各种成熟果实的等级信息,以便对果实实时分类采摘和存放,将计算机视觉图像、近红外反射光谱等信息通过多传感信息融合进行检测,并对多目标采摘的机械手路径规划开展研究。本研究主要完成了以下工作：
(1)研究果实图像、光谱和内在品质常规检测等试验信息采集。在对番茄试验样本的成熟期筛选的基础上,讨论了番茄可见光图像、多光谱图像、温室垄间道路图像的信息获取,介绍了利用光谱仪采集番茄近红外光谱信息的试验方案。研究了番茄内部理化成分的常规检测方法,并获取了番茄样本常规理化成分检测的数据。
(2)利用机器视觉技术对田间生长状态的番茄成熟度进行识别判定研究。选取绿熟期、催熟期、半熟期、成熟期和完熟期等五种程度的番茄果实为研究对象,在生长状态下采集可见光图像和近红外图像,提取了12个图像颜色特征变量进行分析,利用神经网络、判定分析等模式识别方法进行了果实识别分类。试验表明,①随着成熟度的变化,番茄果实图像色调H、G分量均值有递减趋势,半熟期果实图像H和G分量色差的均值标准差均为最大。②在所有成熟度番茄样本中,半熟期番茄近红外图像强度均值最小。③色调H分量均值可作为番茄成熟度判定指标,当H分量均值取43时,可以将绿熟期和催熟期以上成熟度番茄划分开。④基于H分量统计模型的番茄成熟度模型总体判定正确率为93%,判定误差主要由半熟期番茄识别造成。
(3)开展基于近红外光谱技术的果实内在品质快速检测研究。针对番茄收获机器人选择性收获时对番茄内部品质等级信息实时检测问题,利用可见-近红外光谱技术对番茄的胡萝卜素、固形物、总糖和总酸含量进行了非破坏性快速检测研究。①以镇江地产番茄品种共70个样品为研究对象,比较了3中不同的光谱预处理方法对回归模型精度的影响,采用样本数据集归一化处理,在全波段范围内建立了4个理化成分的支持向量机回归模型,模型的交互验证的相关系数均在G.94以上。②选取糖分作为番茄内在品质评价依据,利用iPLS法从全波段中提取出216个波长点,再利用遗传算法结合偏最小二乘法(GA-PLS)筛选出6个特征波长,分别为552nm、557nm、1215nm、1251nm、1279nm、1284nm。利用提取的6个特征波长,建立了糖分等级的支持向量机分类模型,分类准确率为82.35%,可以用于番茄品质指标的快速检测。
(4)针对温室环境下严重遮挡果实的快速识别和形状特征提取开展研究。①提出利用H分量颜色特征分割果实敏感区域,通过形态学开运算方法去除叶、茎秆和温室附属物等背景噪音,采用区域空洞填充算法消除阳光直射形成的亮斑空洞,应用顺序法对区域标记处理并实现多果实区域边界跟踪,识别出果实区域。②针对多种生长状态下尤其是严重重叠的多果实番茄图像的形状特征提取,提出基于边界弦平分线结合聚类算法,确定出每个果实的中心点和外接圆半径等形状特征参数。结果表明,针对分辨率640x480图像的处理时间为0.45s,对自然状态下的200幅番茄图像的正确识别率为95.5%。
(5)开展结果期温室内吊蔓绳障碍物识别研究。首先基于区域特征采用Otsu法对吊蔓绳图像进行阈值分割,并通过试验分析确定了适用于多种天气条件下的阈值修正量。针对温室内多种复杂背景噪声干扰,提出基于面积阈值法去除小面积噪声区域,采用区域外接矩长宽比阈值法去除大面积背景噪声区域,从而获得吊蔓绳区域。最后利用最小二乘拟合得到吊蔓绳障碍物位置信息。通过试验确定了外接矩长宽比阈值为6.0952,该识别方法对温室光照不均、背景噪声复杂等干扰因素有良好的适应性,对100幅植株障碍物样本图像的正确识别率达93%,平均耗时为0.8s。
(6)开展番茄收获机器人多源作业信息获取系统集成研究。探讨了番茄机器人实时分类采收过程中多传感器融合层次的选择和信息融合组合结构,研究了基于多传感器信息的番茄选择性采收决策和等级判定的实现。进行了温室番茄采收机器人多源信息获取系统硬件集成设计,构建了双目视觉识别系统,选用FieldSpecPro光谱仪采集果实内部糖分信息,利用MOTOMAN机械手结合自制的末端执行器组成番茄选择性采摘机械手,并和移动平台构成一个8自由度的番茄机器人。通过标定试验,分析确定双目摄像机的合理安装间距为250mm,定位误差可控制在5mm以内。采用基于Visual C++和MATLAB混合编程的方式,设计开发了番茄收获机器人采收决策和目标信息检测软件,并对选定样本进行了试验检测。
(7)研究了实时分类收获过程中机械手多果实位置点到多个果箱的复杂路径规划。提出构建机械手全局最优路径规划决策树,按照全局路径最短距离的优化原则遍历搜索决策树,建立路径优化模型,求解全局最优路径。结合番茄果实空间位置和吊蔓绳障碍物位置信息,构建MOTOMAN SV3X机械手运动学模型,开展局部避障运动轨迹规划研究。
(8)研究了温室非结构作业环境和复杂背景下垄间道路识别方法。提出了依据I分量直方图采用最大类间方差法进行图像阈值分割,利用间隔扫描区域边缘提取算法获取加热管边缘离散点簇,经最小二乘法拟合后得到两条加热管边缘线,进而推算出道路中心基准线。在光照不均和作物遮挡等多种情况下的道路检测试验表明,该方法提取的道路中心基准线与人工拟合道路基准线平均相对偏差为0.77%,当加热管被遮盖率在[10%90%]区间时,道路基准线提取算法准确率达91.4%,平均相对偏差0.72%,较Hough变换直线检测算法平均耗时降低了76.4%,表明该算法简单快捷,具有良好的鲁棒性。
Studies on the key techniques of the fruit-picking robot are the necessary circular to large-scale facility agriculture and industrialized process. Studies are made for the operation information acquization of tomato-picking robot, and it is of great theoretical significance and practical values to improve the research and development level of intelligentized facility agriculture in China.
As for guiding the tomato harvest robots harvesting selectively, issues that acquire information quickly and accurately have been studied. In order to obtain the information of ripen fruits grades for picking and storing tomatoes can be classified with real-time, thus the detection has been conducted with multi-sensor infusion of computer vision images and reflected near-infrared spectra. And the path for manipulator picking multi-objects has been planned. In this paper major research works are as follows:
1. A method of test information collection has been studied for image, spectroscopy of tomato and normal detection of tomato internal composition. Based on the filtered in the maturity of the tomato testing samples, the information acquisitions are been discussed for visible image, multispectral images of tomato and road image in greenhouse. The testing scheme about acquisition of tomato near-infrared spectroscopy was introduced with the spectrometer. The generalized detection indexes of tomato internal physical and chemical composition are been conducted by normal detections and process.
2. By using machine vision technology, the tomatoes' maturity has been identified under natural growth conditions of the field. The tomato samples chosen as research objects are divided into five different stages, such as breakers, turning, pink, light-red, and red stages. The visible light and near-infrared images of tomatoes have been acquired. And12color eigenvalues of images were extracted and analyzed. Tomatoes are classified with using pattern recognition methods, such as neural networks and decision analysis. The results indicate that:(1) with the changes of maturity, the Hue-means and Green-mean of images decreased gradually, and the standard deviations of the images'Hue-mean and Green-component mean are the largest values for tomatoes in the pink stage.(2) The intensity mean of tomato samples'near-infrared images in the pink stage is the lowest value.(3) Hue-mean can be used as a criterion for judging tomatoes'maturity. When Hue-mean taking43, tomatoes can be divided into maturity of breakers and turning above (including turning).(4) Based on the statistical model of Hue component, the accuracy of determination for tomatoes'maturity model is93%, and the judgment errors are caused by identifying tomatoes at the pink stage.
3. Rapid detection has been studied for the internal quality of tomatoes based the techniques of visible-near-infrared spectroscopy. In order to detect the internal quality grades of tomatoes during harvesting selectively, the non-destructive rapid detections are taken by using the techniques of visible-near-infrared spectroscopy against carotenoids, solids, total sugar and total acid content of tomatoes.(1) A total of70tomato samples produced in Zhenjiang are chosen as the research objects. Three different spectral pretreatment methods are compared on the accuracies of the regression mode. Four physical and chemical compositions of support vector machine regress models have been established in the range of full wavelength with normalization of sampling data set. The cross validation correlation coefficient of the model is over0.94.(2) Sugar is selected to evaluate the intrinsic quality of tomatoes. By using iPLS method,216wavelength points are extracted from the full-waveband range, and further6characteristic wavelength extracted by using genetic algorithm in combination with partial least squares (GA-PLS) are522nm,557nm,1215nm,1251nm,1279nm, and1284nm, respectively. Based on the extracted6characteristic wavelengths, support vector machine classification models have been established for judging tomatoes'sugar grades. The accuracy of classification is82.35percent. And sugar can be used as an assessment indicator for rapid detecting tomatoes'quality.
4. Studies are made for rapidly identifying and extracting the tomatoes sheltered under the condition of a greenhouse. The Hue component of the color characteristics is proposed to segment the sensitive area of the tomato. For recognizing the area of tomatoes, the background noises of the leaf, stem and greenhouses'appendages are eliminated by the morphological opening operation method, and bright spots caused by direct sunlight are eliminated by the method of filling regional hole, and the marked regions are dealt with the method of sequence for tracking the boundary of multi-tomatoes areas. The shape characteristics of multi-tomatoes seriously overlapped are extracted in a variety of natural growth conditions. Characteristics parameters of shape in each tomato, such as the center point and circumradius, are determined based on the cluster algorithm of the boundary string bisector. The results show that the processing time of the image with640X480is0.45second, and correct recognition rate for200images of tomatoes under the natural state is95.5%.
5. In the period of harvesting, the identification of the ropes fixing the tomatoes' stems are studied. At first, the Otsu method is used to determine the threshold segmentation of the rope images, and a modified amount of threshold suitable for variety weather is determined by experimental analysis. Considering a variety of complex noise interference of background conditions in a greenhouse, a threshold-based method is used to remove the noise region of the small area. And aspect ratio threshold method of external rectangular for the region is used to remove the area with large background noise for recognizing the rope area. Finally, the location of the ropes is determined using the method of least squares. Aspect ratio's threshold value of circumscribed rectangular is6.0952determined by the experiments. The identification method has adaptability to interfere factors, such as the uneven light and complex background noise in the greenhouse. The correct identification rate of100sample images with obstacles is93%, and the average elapsed time is0.8second.
6. System integration of multi-information acquisition has been studied for tomato harvesting robots. In the process of real-time classification harvesting, the tomato picking robot is explored to treat the selection of fusion hierarchy and information fusion composite structures with multi-sensors, thus making the achievement of selective harvesting decisions and grade determination of tomatoes based on the multi-sensors information. A system of multi-information acquisition has been studied for guiding tomato harvesting robots in greenhouses. The binocular visual recognition system has been established. The information of fruits'sugar grades is acquired by the FieldSpec Pro spectrometer. The tomato picking manipulator is composed of a MOTOMAN manipulator accompanied by a self-developed end-effector. And the tomato picking manipulator and a movable platform constitutes an8-DOF tomato harvesting robot. It has been known through calibration experiments that the reasonable mounting pitch of the binocular cameras is analyzed to determine it as250mm, and positioning tolerance can be controlled within5mm. Based on the mixed programming of Visual C++and MATLAB, modular design has been used to develop the software of the harvesting decision-making and target information detector, and then the testing detection is made for the selected samples.
7. In the process of real-time classification harvesting, the complex path planning has been studied for the manipulator going from muti-fruit location points to multiple boxes of fruits. A method that builds a manipulator global optimal path planning decision tree is proposed, thus searching the decision tree comprehensively according to the optimal principle of the global shortest distance so as to establish path optimal model and to solve the global optimal path. Combining the spatial location of tomato fruits with the location information of hanging vine rope obstacles, the kinematics model has been constructed for MOTOMAN SV3X manipulator, and studies are made for local obstacle-avoiding movement trajectory planning.
8. A method has been studied that identifies the central path of greenhouse operating environment with the non-structural and complex background. A method has been proposed that segments the image adaptive threshold with maximum interclass variance according to the I component histogram, and obtains heat pipe edge discrete points cluster by using the edge extraction algorithm of the target area after the segmentation of binary image, and then obtains the two heating tube edge line after its bridging the method of least squares. Based on this, a baseline extraction algorithm has been derived for the center of the road, and this road detection algorithm is verified for uneven illumination and crop occlusion factors. The results show that the average deviation between baseline of the center of the road used with this method and the manual fitting method is0.77%, when the heating pipe shadow rate is between10%and90%, with91.4%of the accuracy of road baseline extraction algorithm, and with0.72%of the average relative deviation. Especially, this average time is76.4%lower than the Hough transform for line detection algorithm, which shows that this algorithm is simple and quick with satisfactory robustness.
本研究以温室典型瓜果作物——番茄为研究对象,对番茄收获机器人选择性采收中作业信息快速获取问题进行了研究,为了获取各种成熟果实的等级信息,以便对果实实时分类采摘和存放,将计算机视觉图像、近红外反射光谱等信息通过多传感信息融合进行检测,并对多目标采摘的机械手路径规划开展研究。本研究主要完成了以下工作：
(1)研究果实图像、光谱和内在品质常规检测等试验信息采集。在对番茄试验样本的成熟期筛选的基础上,讨论了番茄可见光图像、多光谱图像、温室垄间道路图像的信息获取,介绍了利用光谱仪采集番茄近红外光谱信息的试验方案。研究了番茄内部理化成分的常规检测方法,并获取了番茄样本常规理化成分检测的数据。
(2)利用机器视觉技术对田间生长状态的番茄成熟度进行识别判定研究。选取绿熟期、催熟期、半熟期、成熟期和完熟期等五种程度的番茄果实为研究对象,在生长状态下采集可见光图像和近红外图像,提取了12个图像颜色特征变量进行分析,利用神经网络、判定分析等模式识别方法进行了果实识别分类。试验表明,①随着成熟度的变化,番茄果实图像色调H、G分量均值有递减趋势,半熟期果实图像H和G分量色差的均值标准差均为最大。②在所有成熟度番茄样本中,半熟期番茄近红外图像强度均值最小。③色调H分量均值可作为番茄成熟度判定指标,当H分量均值取43时,可以将绿熟期和催熟期以上成熟度番茄划分开。④基于H分量统计模型的番茄成熟度模型总体判定正确率为93%,判定误差主要由半熟期番茄识别造成。
(3)开展基于近红外光谱技术的果实内在品质快速检测研究。针对番茄收获机器人选择性收获时对番茄内部品质等级信息实时检测问题,利用可见-近红外光谱技术对番茄的胡萝卜素、固形物、总糖和总酸含量进行了非破坏性快速检测研究。①以镇江地产番茄品种共70个样品为研究对象,比较了3中不同的光谱预处理方法对回归模型精度的影响,采用样本数据集归一化处理,在全波段范围内建立了4个理化成分的支持向量机回归模型,模型的交互验证的相关系数均在G.94以上。②选取糖分作为番茄内在品质评价依据,利用iPLS法从全波段中提取出216个波长点,再利用遗传算法结合偏最小二乘法(GA-PLS)筛选出6个特征波长,分别为552nm、557nm、1215nm、1251nm、1279nm、1284nm。利用提取的6个特征波长,建立了糖分等级的支持向量机分类模型,分类准确率为82.35%,可以用于番茄品质指标的快速检测。
(4)针对温室环境下严重遮挡果实的快速识别和形状特征提取开展研究。①提出利用H分量颜色特征分割果实敏感区域,通过形态学开运算方法去除叶、茎秆和温室附属物等背景噪音,采用区域空洞填充算法消除阳光直射形成的亮斑空洞,应用顺序法对区域标记处理并实现多果实区域边界跟踪,识别出果实区域。②针对多种生长状态下尤其是严重重叠的多果实番茄图像的形状特征提取,提出基于边界弦平分线结合聚类算法,确定出每个果实的中心点和外接圆半径等形状特征参数。结果表明,针对分辨率640x480图像的处理时间为0.45s,对自然状态下的200幅番茄图像的正确识别率为95.5%。
(5)开展结果期温室内吊蔓绳障碍物识别研究。首先基于区域特征采用Otsu法对吊蔓绳图像进行阈值分割,并通过试验分析确定了适用于多种天气条件下的阈值修正量。针对温室内多种复杂背景噪声干扰,提出基于面积阈值法去除小面积噪声区域,采用区域外接矩长宽比阈值法去除大面积背景噪声区域,从而获得吊蔓绳区域。最后利用最小二乘拟合得到吊蔓绳障碍物位置信息。通过试验确定了外接矩长宽比阈值为6.0952,该识别方法对温室光照不均、背景噪声复杂等干扰因素有良好的适应性,对100幅植株障碍物样本图像的正确识别率达93%,平均耗时为0.8s。
(6)开展番茄收获机器人多源作业信息获取系统集成研究。探讨了番茄机器人实时分类采收过程中多传感器融合层次的选择和信息融合组合结构,研究了基于多传感器信息的番茄选择性采收决策和等级判定的实现。进行了温室番茄采收机器人多源信息获取系统硬件集成设计,构建了双目视觉识别系统,选用FieldSpecPro光谱仪采集果实内部糖分信息,利用MOTOMAN机械手结合自制的末端执行器组成番茄选择性采摘机械手,并和移动平台构成一个8自由度的番茄机器人。通过标定试验,分析确定双目摄像机的合理安装间距为250mm,定位误差可控制在5mm以内。采用基于Visual C++和MATLAB混合编程的方式,设计开发了番茄收获机器人采收决策和目标信息检测软件,并对选定样本进行了试验检测。
(7)研究了实时分类收获过程中机械手多果实位置点到多个果箱的复杂路径规划。提出构建机械手全局最优路径规划决策树,按照全局路径最短距离的优化原则遍历搜索决策树,建立路径优化模型,求解全局最优路径。结合番茄果实空间位置和吊蔓绳障碍物位置信息,构建MOTOMAN SV3X机械手运动学模型,开展局部避障运动轨迹规划研究。
(8)研究了温室非结构作业环境和复杂背景下垄间道路识别方法。提出了依据I分量直方图采用最大类间方差法进行图像阈值分割,利用间隔扫描区域边缘提取算法获取加热管边缘离散点簇,经最小二乘法拟合后得到两条加热管边缘线,进而推算出道路中心基准线。在光照不均和作物遮挡等多种情况下的道路检测试验表明,该方法提取的道路中心基准线与人工拟合道路基准线平均相对偏差为0.77%,当加热管被遮盖率在[10%90%]区间时,道路基准线提取算法准确率达91.4%,平均相对偏差0.72%,较Hough变换直线检测算法平均耗时降低了76.4%,表明该算法简单快捷,具有良好的鲁棒性。
Studies on the key techniques of the fruit-picking robot are the necessary circular to large-scale facility agriculture and industrialized process. Studies are made for the operation information acquization of tomato-picking robot, and it is of great theoretical significance and practical values to improve the research and development level of intelligentized facility agriculture in China.
As for guiding the tomato harvest robots harvesting selectively, issues that acquire information quickly and accurately have been studied. In order to obtain the information of ripen fruits grades for picking and storing tomatoes can be classified with real-time, thus the detection has been conducted with multi-sensor infusion of computer vision images and reflected near-infrared spectra. And the path for manipulator picking multi-objects has been planned. In this paper major research works are as follows:
1. A method of test information collection has been studied for image, spectroscopy of tomato and normal detection of tomato internal composition. Based on the filtered in the maturity of the tomato testing samples, the information acquisitions are been discussed for visible image, multispectral images of tomato and road image in greenhouse. The testing scheme about acquisition of tomato near-infrared spectroscopy was introduced with the spectrometer. The generalized detection indexes of tomato internal physical and chemical composition are been conducted by normal detections and process.
2. By using machine vision technology, the tomatoes' maturity has been identified under natural growth conditions of the field. The tomato samples chosen as research objects are divided into five different stages, such as breakers, turning, pink, light-red, and red stages. The visible light and near-infrared images of tomatoes have been acquired. And12color eigenvalues of images were extracted and analyzed. Tomatoes are classified with using pattern recognition methods, such as neural networks and decision analysis. The results indicate that:(1) with the changes of maturity, the Hue-means and Green-mean of images decreased gradually, and the standard deviations of the images'Hue-mean and Green-component mean are the largest values for tomatoes in the pink stage.(2) The intensity mean of tomato samples'near-infrared images in the pink stage is the lowest value.(3) Hue-mean can be used as a criterion for judging tomatoes'maturity. When Hue-mean taking43, tomatoes can be divided into maturity of breakers and turning above (including turning).(4) Based on the statistical model of Hue component, the accuracy of determination for tomatoes'maturity model is93%, and the judgment errors are caused by identifying tomatoes at the pink stage.
3. Rapid detection has been studied for the internal quality of tomatoes based the techniques of visible-near-infrared spectroscopy. In order to detect the internal quality grades of tomatoes during harvesting selectively, the non-destructive rapid detections are taken by using the techniques of visible-near-infrared spectroscopy against carotenoids, solids, total sugar and total acid content of tomatoes.(1) A total of70tomato samples produced in Zhenjiang are chosen as the research objects. Three different spectral pretreatment methods are compared on the accuracies of the regression mode. Four physical and chemical compositions of support vector machine regress models have been established in the range of full wavelength with normalization of sampling data set. The cross validation correlation coefficient of the model is over0.94.(2) Sugar is selected to evaluate the intrinsic quality of tomatoes. By using iPLS method,216wavelength points are extracted from the full-waveband range, and further6characteristic wavelength extracted by using genetic algorithm in combination with partial least squares (GA-PLS) are522nm,557nm,1215nm,1251nm,1279nm, and1284nm, respectively. Based on the extracted6characteristic wavelengths, support vector machine classification models have been established for judging tomatoes'sugar grades. The accuracy of classification is82.35percent. And sugar can be used as an assessment indicator for rapid detecting tomatoes'quality.
4. Studies are made for rapidly identifying and extracting the tomatoes sheltered under the condition of a greenhouse. The Hue component of the color characteristics is proposed to segment the sensitive area of the tomato. For recognizing the area of tomatoes, the background noises of the leaf, stem and greenhouses'appendages are eliminated by the morphological opening operation method, and bright spots caused by direct sunlight are eliminated by the method of filling regional hole, and the marked regions are dealt with the method of sequence for tracking the boundary of multi-tomatoes areas. The shape characteristics of multi-tomatoes seriously overlapped are extracted in a variety of natural growth conditions. Characteristics parameters of shape in each tomato, such as the center point and circumradius, are determined based on the cluster algorithm of the boundary string bisector. The results show that the processing time of the image with640X480is0.45second, and correct recognition rate for200images of tomatoes under the natural state is95.5%.
5. In the period of harvesting, the identification of the ropes fixing the tomatoes' stems are studied. At first, the Otsu method is used to determine the threshold segmentation of the rope images, and a modified amount of threshold suitable for variety weather is determined by experimental analysis. Considering a variety of complex noise interference of background conditions in a greenhouse, a threshold-based method is used to remove the noise region of the small area. And aspect ratio threshold method of external rectangular for the region is used to remove the area with large background noise for recognizing the rope area. Finally, the location of the ropes is determined using the method of least squares. Aspect ratio's threshold value of circumscribed rectangular is6.0952determined by the experiments. The identification method has adaptability to interfere factors, such as the uneven light and complex background noise in the greenhouse. The correct identification rate of100sample images with obstacles is93%, and the average elapsed time is0.8second.
6. System integration of multi-information acquisition has been studied for tomato harvesting robots. In the process of real-time classification harvesting, the tomato picking robot is explored to treat the selection of fusion hierarchy and information fusion composite structures with multi-sensors, thus making the achievement of selective harvesting decisions and grade determination of tomatoes based on the multi-sensors information. A system of multi-information acquisition has been studied for guiding tomato harvesting robots in greenhouses. The binocular visual recognition system has been established. The information of fruits'sugar grades is acquired by the FieldSpec Pro spectrometer. The tomato picking manipulator is composed of a MOTOMAN manipulator accompanied by a self-developed end-effector. And the tomato picking manipulator and a movable platform constitutes an8-DOF tomato harvesting robot. It has been known through calibration experiments that the reasonable mounting pitch of the binocular cameras is analyzed to determine it as250mm, and positioning tolerance can be controlled within5mm. Based on the mixed programming of Visual C++and MATLAB, modular design has been used to develop the software of the harvesting decision-making and target information detector, and then the testing detection is made for the selected samples.
7. In the process of real-time classification harvesting, the complex path planning has been studied for the manipulator going from muti-fruit location points to multiple boxes of fruits. A method that builds a manipulator global optimal path planning decision tree is proposed, thus searching the decision tree comprehensively according to the optimal principle of the global shortest distance so as to establish path optimal model and to solve the global optimal path. Combining the spatial location of tomato fruits with the location information of hanging vine rope obstacles, the kinematics model has been constructed for MOTOMAN SV3X manipulator, and studies are made for local obstacle-avoiding movement trajectory planning.
8. A method has been studied that identifies the central path of greenhouse operating environment with the non-structural and complex background. A method has been proposed that segments the image adaptive threshold with maximum interclass variance according to the I component histogram, and obtains heat pipe edge discrete points cluster by using the edge extraction algorithm of the target area after the segmentation of binary image, and then obtains the two heating tube edge line after its bridging the method of least squares. Based on this, a baseline extraction algorithm has been derived for the center of the road, and this road detection algorithm is verified for uneven illumination and crop occlusion factors. The results show that the average deviation between baseline of the center of the road used with this method and the manual fitting method is0.77%, when the heating pipe shadow rate is between10%and90%, with91.4%of the accuracy of road baseline extraction algorithm, and with0.72%of the average relative deviation. Especially, this average time is76.4%lower than the Hough transform for line detection algorithm, which shows that this algorithm is simple and quick with satisfactory robustness.
引文
[1]中华人民共和国农业部主管.中国农业机械化年鉴.2011[M].北京市：中国农业科学技术出版社,2011.12.
[2]Hayashi S, Shigematsu K, Yamamoto S, et al. Evaluation of a strawberry-harvesting robot in a field test[J]. Biosystems Engineering,2010,105(2):160-171.
[3]Sanders K F. Orange harvesting systems review[J]. Biosystems Engineering, 2005,90(2):115-125.
[4]神内新疆食品研究开发中心,中国农业科学院蔬菜花卉研究所,石河子大学,NY/T1517-2007加工用番茄[S].行业标准-农业,2007.
[5]中国农产品市场协会,农业部蔬菜品质监督检验测试中心(北京).NY/T 940-2006番茄等级规格[S].行业标准-农业(CSIC-NY),2006.
[6]Feng J, Wang S W, Liu G, et al. A Separating Method of Adjacent Apples Based on Machine Vision and Chain Code Information[M]//Computer and Computing Technologies In Agriculture V, PT I.2012:258-267.
[7]Ji W, Zhao D, Cheng F Y, et al. Automatic recognition vision system guided for apple harvesting robot[J]. Computers & Electrical Engineering,2012,38(5SI):1186-1195.
[8]Arefi A, Motlagh A M, Mollazade K, et al. Recognition and localization of ripen tomato based on machine vision[J]. Australian Journal of Crop Science,2011,5(10):1144-1149.
[9]Blanes C, Mellado M, Ortiz C, et al. Technologies for robot grippers in pick and place operations for fresh fruits and vegetables[J]. Spanish Journal of Agricultural Research, 2011,9(4):1130-1141.
[10]Dong F H, Heinemann W, Kasper R. Nonlinear Control Design for Row Guidance System of an Automated Asparagus Harvesting Robot[M]//2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).2011:1087-1092.
[11]He B, Liu G, Ji Y, et al. Auto Recognition of Navigation Path for Harvest Robot Based on Machine Vision[M]//Computer and Computing Technologies in Agriculture IV, PT 1. 2011:138-148.
[12]Li P L, Lee S H, Hsu H Y. Study on citrus fruit image data separability by segmentation methods[M]//PEEA 2011.2011.
[13]Reis M, Morais R, Pereira C, et al. A Low-Cost System to Detect Bunches of Grapes in Natural Environment from Color Images[M]//Advanced Concepts for Intelligent Vision Systems.2011:92-102.
[14]Wang X Z, Mao H P, Han X, et al. Vision-based judgment of tomato maturity under growth conditions[J]. African Journal of Biotechnology,2011,10(18):3616-3623.
[15]Yang H Q, Kuang B Y, Mouazen A M. In Situ Determination of Growing Stages and Harvest Time of Tomato (Lycopersicon esculentum) Fruits Using Fiber-Optic Visible-Near-Infrared (Vis-NIR) Spectroscopy[J]. Applied Spectroscopy,2011,65(8): 931-938.
[16]Zhao D A, Lv J D, Ji W, et al. Design and control of an apple harvesting robot[J]. Biosystems Engineering,2011,110(2):112-122.
[17]Hayashi S, Shigematsu K, Yamamoto S, et al. Evaluation of a strawberry-harvesting robot in a field test[J]. Biosystems Engineering,2010,105(2):160-171.
[18]Lu Q A, Lu H Z, Cai J R, et al. Feature extraction of near-spherical fruit with partial occlusion for robotic harvesting[J]. Maejo International Journal of Science and Technology, 2010,4(3):435-445.
[19]Van Henten E J, Schenk E J, van Willigenburg L G, et al. Collision-free inverse kinematics of the redundant seven-link manipulator used in a cucumber picking robot[J]. Biosystems Engineering,2010,106(2):112-124.
[20]蔡健荣,孙海波,李永平,等.基于双目立体视觉的果树三维信息获取与重构[J].农业机械学报,2012(03)：152-156.
[21]陈军,王虎,蒋浩然,等.猕猴桃采摘机器人末端执行器设计[J].农业机械学报,2012(10)：151-154.
[22]陈燕,蔡伟亮,向和平,等.面向机器人采摘的荔枝果梗力学特性[J].农业工程学报,2012(21)：53-58.
[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[J].农业机械学报,2012(06)：153-160.
[24]李斌,王海峰,黄文倩,等.菠萝采收机械低成本双目视觉平台搭建与田间试验(英文)[J].农业工程学报,2012(S2)：188-192.
[25]李寒,王库,曹倩,等.基于机器视觉的番茄多目标提取与匹配[J].农业工程学报,2012(05)：168-172.
[26]李立君,李昕,高自成,等.基于偏好免疫网络的油茶果采摘机器人图像识别算法[J].农业机械学报,2012(11)：209-213.
[27]李昕,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[J].农业工程学报,2012(14)：133-137.
[28]吕继东,赵德安,姬伟,等.苹果采摘机器人对振荡果实的快速定位采摘方法[J].农业工程学报,2012(13)：48-53.
[29]吕继东,赵德安,姬伟,等.采摘机器人振荡果实动态识别[J].农业机械学报,2012(05)：173-178.
[30]王海峰,李斌,刘广玉,等.菠萝采摘机械手的设计与试验(英文)[J].农业工程学报,2012(S2)：42-46.
[31]项荣,应义斌,蒋焕煜,等.基于双目立体视觉的番茄定位[J].农业工程学报,2012(05)：161-167.
[32]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[J].农业工程学报, 2012(14)：36-41.
[33]张凯良,杨丽,王粮局,等.高架草莓采摘机器人设计与试验[J].农业机械学报,2012(09)：165-172.
[34]崔鹏,陈志,张小超.苹果采摘机器人仿生机械手静力学分析与仿真[J].农业机械学报,2011(02)：149-153.
[35]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[J].农业机械学报,2011(02)：154-157.
[36]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[J].机器人,2011(06)：726-730.
[37]卢伟,宋爱国,蔡健荣,等.柑橘采摘机器人结构设计及运动学算法[J].东南大学学报(自然科学版),2011(01)：95-100.
[38]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[J].农业工程学报,2011(04)：345-349.
[39]叶敏,邹湘军,蔡沛锋,等.水果采摘机器人通用夹持机构设计[J].农业机械学报,2011(S1)：177-180.
[40]张凯良,杨丽,张铁中.草莓收获机器人采摘执行机构设计与试验[J].农业机械学报,2011(09)：155-161.
[41]周俊,张高阳,刘锐,等.基于粒子滤波的苹果采摘机器人目标姿态估算[J].农业机械学报,2011(03)：161-165.
[42]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[J].农业机械学报,2010(11)：153-157.
[43]冯青春,纪超,张俊雄,等.黄瓜采摘机械臂结构优化与运动分析[J].农业机械学报,2010(S1)：244-248.
[44]居洪玲,姬长英.一种多用途采摘机器人末端执行器的设计[J].浙江农业学报,2010,22(3)：370-373.
[45]李斌,汪懋华,李莉.基于单目视觉的田间菠萝果实识别[J].农业工程学报,2010(10)：345-349.
[46]李理,殷国富,刘柯岐.田间果蔬采摘机器人视觉传感器设计与试验[J].农业机械学报,2010(5)：152-157.
[47]李智国,刘继展,李萍萍.机器人采摘中番茄力学特性与机械损伤的关系[J].农业工程学报,2010(5)：112-116.
[48]刘继展,李萍萍,倪齐,等.番茄采摘机器人真空吸盘装置设计与试验[J].农业机械学报,2010(10)：170-173.
[49]刘兆祥,刘刚,乔军.苹果采摘机器人三维视觉传感器设计[J].农业机械学报,2010(2)：171-175.
[50]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[J].农业工程学报, 2010(7)：107-112.
[51]司永胜,乔军,刘刚,等.苹果采摘机器人果实识别与定位方法[J].农业机械学报,2010(9)：148-153.
[52]屠珺,刘成良,李彦明,等.基于光照无关图的苹果图像识别方法[J].农业工程学报,2010(S2)：26-31.
[53]吴刚,谭或,郑永军,等.基于改进Hough变换的收获机器人行走目标直线检测[J].农业机械学报,2010(2)：176-179.
[54]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[J].农业机械学报,2010(4)：151-156.
[55]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[J].农业机械学报,2010(6)：158-162.
[56]Van Henten E J, Hemming J, Van Tuijl B, et al. Collision-free motion planning for a cucumber picking robot[J]. Biosystems Engineering,2003,86(2):135-144.
[57]van Henten E J, Hemming J, van Tuijl B, et al. An autonomous robot for harvesting cucumbers in greenhouses[J]. Autonomous Robots,2002,13(3):241-258.
[58]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[C].
[59]Yin H, Chai Y, Yang S X, et al. Ripe tomato detection for robotic vision harvesting systems in greenhouses[J]. Transactions of the ASABE,2011,54(4):1539-1546.
[60]蔡健荣,赵杰文,Rath Thomas,等.水果收获机器人避障路径规划[J].农业机械学报,2007,38(3)：102-105,135.
[61]梁喜凤,王永维,苗香雯.番茄收获机械手避障运动规划[J].农业机械学报,2008,39(11)：89-93.
[62]魏新华,丁为民,陈玉仑,等.茄子收获机器人机械臂避障路径规划[J].农业机械学报,2008,39(11)：94-98.
[63]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[J].农业工程学报,2006,22(10)：149-153.
[64]Slaughter D C, Harrell R C. Color vision in robotic fruit harvesting[J]. Transactions of the ASAE,1987,30(4):1144-1148.
[65]Schertz C E, Brown G K. Basic considerations in mechanizing citrus harvest[J]. Transactions of the ASAE,1968,11(2):343-346.
[66]Slaughter D C, Harrell R C. Discriminating fruit for robotic harvest using color in natural outdoor scenes[J]. Transactions of the ASAE,1989,32(2):757-763.
[67]Harrell R C, Slaughter D C, Adsit P D. Fruit-tracking system for robotic harvesting[J]. Machine Vision and Applications,1989,2(2):69-80.
[68]徐惠荣,应义斌.红外热成像在树上柑桔识别中的应用研究[J].红外与毫米波学报, 2004,23(5)：353-356.
[69]徐惠荣,叶尊忠,应义斌.基于彩色信息的树上柑橘识别研究[J].农业工程学报,2005,21(5)：98-101.
[70]Kurtulmus F, Lee W S, Vardar A. Green citrus detection using'eigenfruit', color and circular Gabor texture features under natural outdoor conditions[J]. Computers and Electronics in Agriculture,2011,78(2):140-149.
[71]Parrish Jr. E A, Goksel A K. Pictorial pattern recognition applied to fruit harvesting. [J]. Transactions of the ASAE,1977,20(5):822-827.
[73]D'Esnon A G, Rabatel G, Pellenc R, et al. Magali: A self-propelled robot to pick apples[J]. American Society of Agricultural Engineers,1987.
[74]Rabatel G. A vision system for Magali, the fruit picking robot: Int. Conf. Agricultural Engineering, Paris,1988[C].
[76]Rakun J, Stajnko D, Zazula D. Detecting fruits in natural scenes by using spatial-frequency based texture analysis and multiview geometry[J]. Computers and Electronics in Agriculture, 2011,76(1):80-88.
[77]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取[J].农业机械学报,2009,40(8)：161-165,73.
[78]司永胜,刘刚,高瑞.基于K-均值聚类的绿色苹果识别技术[J].农业机械学报,2009,40(z1)：100-104.
[79]司永胜,乔军,刘刚,等.苹果采摘机器人果实识别与定位方法[J].农业机械学报,2010,41(9)：148-153.
[80]Sites P W, Delwiche M J. Computer vision to locate fruit on a tree.[J]. Transactions of the ASAE,1988,31(1):257-263,272.
[81]Liu Y, Chen B, Qiao J. Development of a Machine Vision Algorithm for Recognition of Peach Fruit in a Natural Scene[J]. Transactions of the ASABE,2011,54(2):695-702.
[82]Kapach K, Barnea E, Mairon R, et al. Computer vision for fruit harvesting robots-state of the art and challenges ahead[J]. International Journal of Computational Vision and Robotics, 2012,3(1):4-34.
[83]Van Henten E J, Van Tuijl B A J, Hemming J, et al. Field Test of an Autonomous Cucumber Picking Robot[J]. Biosystems Engineering,2003,86(3):305-313.
[84]张铁中,林宝龙,高锐.水果采摘机器人视觉系统的目标提取[J].中国农业大学学报,2004,9(2)：68-72.
[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[J].中国农业大学学报,2006,11(1)：84-86.
[86]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[J].中国农业大学学报,2005,10(1)：48-51.
[87]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[J].中国农业大学学报,2004,9(4)：65-68.
[88]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[J].中国农业大学学报,2007,12(2)：67-71.
[89]Illingworth J, Kittler J. Survey of the Hough transform[J]. Computer Vision, Graphics, and Image Processing,1988,44(1):87-116.
[90]Whittaker A D, Miles G E, Mitchell O R, et al. Fruit location in a partially occluded image.[J]. Transactions of the ASAE,1987,30(3):591-596.
[91]Kondo N, Nishitsuji Y, Ling P P, et al. Visual feedback guided robotic cherry tomato harvesting[J]. Transactions Of The ASAE,1996,39(6):2331-2338.
[92]张瑞合,姬长英,沈明霞,等.计算机视觉技术在番茄收获中的应用[J].农业机械学报,2001,32(5)：50-52.
[93]蔡健荣,赵杰文.自然场景下成熟水果的计算机视觉识别[J].农业机械学报,2005,36(2)：61-64.
[94]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[J].农业工程学报,2006,22(10)：149-153.
[95]尹建军,王新忠,毛罕平,等.RGB与HSI颜色空间下番茄图像分割的对比研究[J].农机化研究,2006(11)：171-174.
[96]李明喜.基于多源图像融合的收获目标准确定位研究[D].江苏大学农业机械化工程,2008.
[97]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[J].农业工程学报,2008,24(6)：174-178.
[98]陈慧.基于FCM聚类算法的水果图像识别的研究[D].云南大学模式识别与智能系统,2008.
[99]董坦坦.成熟番茄的图像识别及其位姿的获取研究[D].南京农业大学农业电气化与自动化,2009.
[100]陈君,朱红.基于互信息最佳阈值迭代的图像分割方法研究[J].武汉理工大学学报(交通科学与工程版),2011,35(3)：641-644.
[101]郭文川,朱新华,郭康权.果品内在品质无损检测技术的研究进展[J].农业工程学报,2001,17(5)：1-5.
[102]Zhang L, McCarthy M J. Measurement and evaluation of tomato maturity using magnetic resonance imaging[J]. Postharvest Biology and Technology,2012,67(0):37-43.
[103]Van de Poel B, Bulens I, Hertog M L A T, et al. Model-based classification of tomato fruit development and ripening related to physiological maturity[J]. Postharvest Biology and Technology,2012,67(0):59-67.
[104]Wang X, Mao H, Han X, et al. Vision-based judgment of tomato maturity under growth conditions[J]. African Journal of Biotechnology,2011,10(18):3616-3623.
[105]王慧慧,孙永海,刘晶晶,等.基于二维离散小波的鲜玉米果穗成熟度的等级评定[J].吉林大学学报(工学版),2011,41(2)：574-578.
[106]王慧慧,孙永海,张贵林,等.基于压力和图像的鲜玉米果穗成熟度分级方法[J].农业工程学报,2010,26(7)：369-373.
[107]马培恰,吴文,王平,等.贡柑采收期果肉颜色变化与果实成熟度的关系研究[J].广东农业科学,2010,37(10)：63-64.
[108]刘鹏,屠康,苏子鹏,等.基于多传感器融合技术的苹果成熟度检测研究[J].江苏农业学报,2010,26(3)：670-672.
[109]尹建军,毛罕平,王新忠,等.自然条件下番茄成熟度机器人判别模型[J].农业机械学报,2009(10)：146-150.
[110]应义斌,饶秀勤,马俊福.柑橘成熟度机器视觉无损检测方法研究[J].农业工程学报,2004,20(2)：144-147.
[111]张长利,房俊龙,潘伟.用遗传算法训练的多层前馈神经网络对番茄成熟度进行自动检测的研究[J].农业工程学报,2001,17(3)：153-156.
[112]王乐妍,张冬仙,章海军,等.基于激光光致发光光谱的果实成熟度测试方法研究[J].光谱学与光谱分析,2008,28(12)：2772-2776.
[113]周轩明.板栗成熟度指标的筛选及近红外检测方法初探[D].中国农业大学中国农业大学果树,2007.
[114]Clment A, Dorais M, Vernon M. Multivariate approach to the measurement of tomato maturity and gustatory attributes and their rapid assessment by Vis-NIR spectroscopy.[J]. Journal of Agricultural and Food Chemistry,2008,56(5):1538-1544.
[115]张生伟.便携式西瓜成熟度测试装置的研制[D].扬州大学机械制造及其自动化,2010.
[116]贺昀轩.基于超声波的芒果成熟度检测研究[D].广西大学检测技术与自动化装置,2009.
[117]Lien C, Ay C, Ting C. Non-destructive impact test for assessment of tomato maturity[J]. Journal of Food Engineering,2009,91(3):402-407.
[118]王书茂,焦群英,籍俊杰.西瓜成熟度无损检验的冲击振动方法[J].农业工程学报,1999(03).
[119]陈志远,张继澍,刘亚龙,等.番茄成熟度与其电学参数关系的研究[J].西北植物学报,2008(4)：4826-4830.
[120]郭文川,朱新华,周闯鹏,等.西红柿成熟度与电特性关系的无损检测研究[J].农业现代化研究,2002,23(6)：458-460.
[121]Gomez A H, Hu G, Wang J, et al. Evaluation of tomato maturity by electronic nose[J]. Computers and Electronics in Agriculture,2006,54(1):44-52.
[122]周亦斌,王俊.基于电子鼻的番茄成熟度及贮藏时间评价的研究[J].农业工程学报,2005,21(4)：113-117.
[123]张云鹤,乔晓军,王成,等.基于机器视觉的作物茎秆直径变化测量仪研发[J].农机化研究,2011,33(12)：158-160,164.
[124]乔桂新,温维亮,郭新宇,等.植物茎秆可视化研究[J].计算机工程与应用,2011,47(35)：164-167.
[125]毛文华,王辉,赵博,等.基于株心颜色的玉米田间杂草识别方法[J].农业工程学报,2009.
[126]沈宝国,陈树人,尹建军,等.基于颜色特征的棉田绿色杂草图像识别方法[J].农业工程学报,2009(6)：163-167.
[127]毛文华,曹晶晶,姜红花,等.基于多特征的田间杂草识别方法[J].农业工程学报,2007(11).
[128]毛文华,王一鸣,张小超.基于机器视觉的苗期杂草实时分割算法[J].农业机械学报,2005(01).
[129]纪寿文,王荣本,陈佳娟,等.应用计算机图像处理技术识别玉米苗期田间杂草的研究[J].农业工程学报,2001,17(2)：154-156.
[130]吕俊伟,马成林,左春柽.用彩色图像识别田间洋葱和杂草的研究(英文)[J].农业工程学报,2001(05).
[131]Guyer D E, Miles G E, Schreiber M M, et al. Machine vision and image processing for plant identification[J]. Transactions of the ASAE,1986,29(6):1500-1507.
[132]Woebbecke D M, Meyer G E, Von Bargen K, et al. Shape features for identifying young weeds using image analysis[J]. Transactions of the ASAE,1995,38(1):271-281.
[133]Meyer G E, Mehta T, Kocher M F, et al. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying[J]. Transactions of the American Society of Agricultural Engineers,1998,41(4):1189-1197.
[134]吕俊伟,马成林,于永胜.采用多光谱图像融合提高作物和杂草灰度比值[J].农业工程学报,2005(11).
[135]毛文华,王月青,王一鸣,等.苗期作物和杂草的光谱分析与识别[J].光谱学与光谱分析,2005(06).
[136]肖健.果树对靶喷雾系统中图像识别技术[D].中国农业大学农业机械化工程,2005.
[137]赵茂程,郑加强,林小静,等.基于分形理论的树木图像分割方法[J].农业机械学报,2004,35(2)：72-75.
[138]赵茂程,郑加强,林小静,等.基于BP神经网络的树形识别系统研究[J].林业科学,2004,40(1)：154-157.
[139]Lee W S, Slaughter D C, Giles D K. Robotic weed control system for tomatoes using machine vision system and precision chemical application:Paper - American Society of Agricultural Engineers, Minneapolis, MN, USA,1997[C].
[140]周俊.农用轮式移动机器人视觉导航系统的研究[D].南京农业大学南京农业大学农业机械化工程,2003.
[141]罗锡文,臧英,周志艳.精细农业中农情信息采集技术的研究进展[J].农业工程学报,2006,22(1)：167-173.
[142]Tillett N D, Hague T, Miles S J. Inter-row vision guidance for mechanical weed control in sugar beet[J]. Computers and Electronics in Agriculture,2002,33(3):163-177.
[143]Subramanian V, Burks T F, Arroyo A A. Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation[J]. Computers and Electronics in Agriculture,2006,53(2):130-143.
[144]Kise M, Zhang Q, Rovira Mas F. A stereovision-based crop row detection method for tractor-automated guidance[J]. Biosystems Engineering,2005,90(4):357-367.
[145]Kise M, Zhang Q. Development of a stereovision sensing system for 3D crop row structure mapping and tractor guidance[J]. Biosystems Engineering,2008,101(2):191-198.
[146]Sgaard H T, Olsen H J. Determination of crop rows by image analysis without segmentation[J]. Computers and Electronics in Agriculture,2003,38(2):141-158.
[147]Neris L D O, Roda V O, Trindade Jr. O. A method for agricultural machine guidance on row crops based on the vanishing point: 2007 IEEE International Conference on Vehicular Electronics and Safety, ICVES, Beijing, China,2007[C].
[148]Han S, Zhang Q, Ni B, et al. A guidance directrix approach to vision-based vehicle guidance systems[J]. Computers and Electronics in Agriculture,2004,43(3):179-195.
[149]Gottschalk R, Burgos Artizzu X P, Ribeiro A, et al. Real-time image processing for the guidance of a small agricultural field inspection vehicle:15th International Conference on Mechatronics and Machine Vision in Practice, M2VIP'O8, Auckland, New zealand,2008[C].
[150]Astrand B, Baerveldt A J. A vision based row-following system for agricultural field machinery [J]. Mechatronics,2005,15(2):251-269.
[151]Rovira Mas F, Zhang Q, Reid J F, et al. Hough-transform-based vision algorithm for crop row detection of an automated agricultural vehicle[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering,2005,219(8):999-1010.
[152]Bakker T, Wouters H, van Asselt K, et al. A vision based row detection system for sugar beet[J]. Computers and Electronics in Agriculture,2008,60(1):87-95.
[153]Leemans V, Destain M F. Application of the Hough Transform for Seed Row Localisation using Machine Vision[J]. Biosystems Engineering,2006,94(3):325-336.
[154]Leemans V, Destain M F. Line cluster detection using a variant of the Hough transform for culture row localisation[J]. Image and Vision Computing,2006,24(5):541-550.
[155]Leemans V, Destain M F. A computer-vision based precision seed drill guidance assistance[J]. Computers and Electronics in Agriculture,2007,59(1-2):1-12.
[156]周俊,姬长英.基于知识的视觉导航农业机器人行走路径识别[J].农业工程学报, 2003,19(6)：101-105.
[157]周俊,姬长英.农业机器人视觉导航中多分辨率路径识别[J].农业机械学报,2003,34(6)：120-123.
[158]周俊,刘成良,姬长英.农用轮式移动机器人相对位姿的求解方法[J].中国图象图形学报,2005,10(3)：310-314.
[159]Zhang F, Ying Y, Shen C, et al. Stereovision-based 3D field recognition for automatic ghuidance system of off-road vehicle: Proceedings of SPIE - The International Society for Optical Engineering, Boston, MA, United states,2005[C].
[160]Zhang F, Yin Y, Jiang H, et al. Correlation Analysis-based Image Segmentation Approch for Automatic Agriculture Vehicle[J]. Jounral of Zhejiang University,2005,6A(10):1158-1162.
[161]张志斌,罗锡文,周学成,等.基于Hough变换和Fisher准则的垄线识别算法[J].中国图象图形学报,2007,12(12)：2164-2168.
[162]张志斌,罗锡文,李庆,等.基于良序集和垄行结构的农机视觉导航参数提取算法[J].农业工程学报,2007,23(7)：122-126.
[163]张志斌,罗锡文,王在满.基于良序子集的最近邻垄行图像识别算法[J].中国图象图形学报,2007,12(11)：2048-2051.
[164]王晓燕,陈媛,陈兵旗,等.免耕覆盖地科秆行茬导航路径的图像检测[J].农业机械学报,2009,40(6)：158-163.
[165]陈娇,姜国权,杜尚丰,等.基于垄线平行特征的视觉导航多垄线识别[J].农业工程学报,2009,25(12)：107-113.
[166]安秋,李志臣,姬长英,等.基于光照无关图的农业机器人视觉导航算法[J].农业工程学报,2009,25(11)：208-212.
[167]蔡晋辉,才辉,张光新,等.复杂环境下自动导引车路径识别算法[J].农业机械学报,2011,42(10)：20-24,89.
[168]赵博,毛恩荣,毛文华,等.农业车辆杂草环境下视觉导航路径识别方法[J].农业机械学报,2009,40(z1)：183-186.
[169]蒋海波,张云伟,郭志伟,等.室外机器人视觉导航的路径结构识别[J].计算机工程与应用,2010,46(7)：245-248.
[170]王福娟.温室环境视觉导航路径的识别[J].农机化研究,2010,32(6)：83-86.
[171]李娟娟,邱白晶,王天波.温室农用机器人的路径检测[J].农机化研究,2011,33(3)：151-154.
[172]蔡健荣,赵杰文,Rath Thomas,等.水果收获机器人避障路径规划[J].农业机械学报,2007,38(3)：102-105,135.
[173]梁喜凤,苗香雯,崔绍荣,等.番茄收获机械手运动学优化与仿真试验[J].农业机械学报,2005,36(7)：96-100.
[174]梁喜凤.番茄收获机械手机构分析与优化设计研究[D].浙江大学农业生物环境与能源 工程,2004.
[175]徐毓良,杨起帆.机器人三维空间无碰撞路径规划[J].机器人,1991,13(5)：1-6.
[176]Van Henten E J, Hemming J, Van Tuijl B A J, et al. Collision-free Motion Planning for a Cucumber Picking Robot[J]. Biosystems Engineering,2003,86(2):135-144.
[177]Lozano-Perez T. A simple motion-planning algorithm for general robot manipulators[J]. Robotics and Automation, IEEE Journal of,1987,3(3):224-238.
[178]Schwartz J T, Sharir M. On the'piano movers'problem-1:The case of a two-dimensional rigid polygonal body moving amidst polygonal barriers[J]. Communications on pure and applied mathematics,1983,36(3):345-398.
[179]王伟.障碍环境下机器人无碰自主路径规划的研究[D].哈尔滨工业大学机电控制及自动化,1997.
[180]陈靖波,赵猛,张珩.空间机械臂在线实时避障路径规划研究[J].控制工程,2007,14(4)：445-447,450.
[181]陈树人,戈志勇,王新忠,等.番茄采摘机械手C-空间障碍计算方法[J].农业机械学报,2008,39(1)：191-194.
[182]Lozano-Perez T. Spatial planning: A configuration space approach[J]. Computers, IEEE Transactions on,1983,100(2):108-120.
[183]Hart P E, Nilsson N J, Raphael B. A formal basis for the heuristic determination of minimum cost paths[J]. Systems Science and Cybernetics, IEEE Transactions on,1968,4(2):100-107.
[184]USDA. United States standards for grades of fresh tomatoes.[S].1991.
[185]北京市蔬菜贮藏加工研究所.SB/T 10331-2000番茄[S].行业标准-商业(CSIC-SB), 2000.
[186]斋藤隆,片同節男,王海廷.番茄生理基础[M].上海：上海科学技术出版社,1981.
[187]Smith A R. Color gamut transform pairs: Proceedings of the 5th Annual Conference on Computer Graphics and Interactive Techniques, New York, USA,1978[C]. ACM.
[188]Choi K, Lee G, Han Y J, et al. Tomato maturity evaluation using color image analysis[J], Transactions of American Society of Agricultural Engineers,1995,38(1):171.
[189]Gejima Y, Nagata M, Kenji H. Judging of tomato maturity by utilizing a low-resolution RGB color system: ASAE Annual International Meeting, Ottawa, Canada,2004[C].
[190]Gejima Y, Zhang H, Nagata M. Judgment on level of maturity for tomato quality using L* a* b* color image processing: Proceedings of 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics,2003[C].
[191]Syahrir W M, Suryanti A, Connsynn C. Color grading in tomato maturity estimator using image processing technique:2nd IEEE International Conference on Computer Science and Information Technology,2009.,2009[C].
[192]Zhang Y, Yin X, Zou X, et al. On-line Sorting Maturity of Cherry Tomato by Machine Vision:International Conference on Computer and Computing Technologies in Agriculture, Beijing,China,2008[C].
[193]张长利,房俊龙,潘伟.用遗传算法训练的多层前馈神经网络对番茄成熟度进行自动检测的研究[J].农业工程学报,2001,17(3)：153-156.
[194]尹建军,毛罕平,王新忠,等.自然条件下番茄成熟度机器人判别模型[J].农业机械学报,2009,40(10)：146-150,168.
[195]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[J].农业机械学报,2004,35(5)：122-124,135.
[196]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[J].计算机工程,2004,30(22)：155-156,171.
[197]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[J].农业工程学报,2006,22(10)：149-153.
[198]蒋焕煜,彭永石,申川,等.基于双目立体视觉技术的成熟番茄识别与定位[J].农业工程学报,2008,24(8)：279-283.
[199]董坦坦,姬长英,周俊,等.成熟番茄的图像识别及其位姿的获取研究[J].江西农业学报,2009,21(8)：152-155.
[200]Yin J, Mao H, Hu Y, et al. An automatic segmentation method for multi-tomatoes image under complicated natural background:Proceedings of SPIE-The International Society for Optical Engineering, Goa, India,2006[C].
[201]Donghai Han,王加华.水果内部品质近红外光谱无损检测研究进展[J].中国激光,2008,35(8)：1123-1131.
[202]谢丽娟,应义斌,于海燕,等.近红外光谱分析技术在蔬菜品质无损检测中的应用研究进展[J].光谱学与光谱分析,2007,27(6)：1131-1135.
[203]施佳慧,陈自力,邵咏妮,等.可见/近红外光谱分析技术快速鉴别航天育种番茄[J].光谱学与光谱分析,2011,31(2)：387-389.
[204]谢丽娟,应义斌,应铁进,等.可见/近红外光谱分析技术鉴别转基因番茄叶[J].光谱学与光谱分析,2008,28(5)：1062-1066.
[205]陆辉山,傅霞萍,谢丽娟,等.可见/近红外光估测完整柑橘水果可溶性固形物含量的研究[J].光谱学与光谱分析,2007,27(9)：1727-1730.
[206]夏俊芳,李小昱,李培武,等.基于小波变换的柑橘维生素C含量近红外光谱无损检测方法[J].农业工程学报,2007,23(6)：170-174.
[207]傅霞萍,应义斌,刘燕德,等.水果坚实度的近红外光谱检测分析试验研究[J].光谱学与光谱分析,2006,26(6)：1038-1041.
[208]应义斌,刘燕德,傅霞萍.基于小波变换的水果糖度近红外光谱检测研究[J].光谱学与光谱分析,2006,26(1)：63-66.
[209]史波林,赵镭,刘文,等.苹果内部品质近红外光谱检测的异常样本分析[J].农业机械学报,2010,41(2)：132-137.
[210]邓烈,何绍兰,易时来,等.与哈姆林甜橙果实内在品质主要参数同步相关的特征光谱研究[J].光谱学与光谱分析,2010,30(4)：1049-1052.
[211]马兰,夏俊芳,张战锋,等.基于小波变换的番茄总糖近红外无损检测[J].农业工程学报,2009,25(10)：350-354.
[212]柳建良,黄桂颖,舒永培,等.贡柑不同采收期果实可溶性糖红外光谱学研究[J].西北农业学报,2009,18(1)：234-237.
[213]马兰,夏俊芳,张战锋,等.光谱预处理对近红外光谱无损检测番茄可溶性固形物含量的影响[J].华中农业大学学报,2008,27(5)：672-675.
[214]黄康,汀辉君,徐惠荣,等.基于最小二乘支持向量机的番茄汁糖酸度分析研究[J].光谱学与光谱分析,2009,29(4)：931-934.
[215]Norgaard L, Saudland A, Wagner J, et al. Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy[J]. Applied Spectroscopy,2000,54(3):413-419.
[216]Bulanon D M, Kataoka T, Ota Y, et al. Segmentation algorithm for the automatic recognition of Fuji apples at harvest[J]. BIOSYSTEMS ENGINEERING,2002,83(4):405-412.
[217]Plebe A, Grasso G. Localization of spherical fruits for robotic harvesting[J]. Machine Vision and Applications,2001,13(2):70-79.
[218]Slaughter D C, Harrell R C. Discriminating fruit for robotic harvest using color in natural outdoor scenes[J]. Transactions of the American Society of Agricultural Engineers, 1989,32(2):757-763.
[219]Tabb A L, Peterson D L, Park J. Segmentation of apple fruit from video via background modeling: 2006 ASABE Annual International Meeting, Portland, OR, United states, 2006[C].
[220]Zhao J, Tow J, Katupitiya J. On-tree fruit recognition using texture properties and color data: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Edmonton, AB, Canada,2005[C].
[221]蔡健荣,范军,李玉良,等.基于遗传算法的树上柑桔形状特征提取[J].江苏大学学报(自然科学版),2007,28(6)：469-472.
[222]蔡健荣,李玉良,范军,等.成熟柑橘的图像识别及空间定位研究[J].微计算机信息,2007,23(34)：224-225,314.
[223]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[J].农业工程学报,2008,24(1)：175-178.
[224]吕强,蔡健荣,赵杰文,等.自然场景下树上柑橘实时识别技术[J].农业机械学报,2010,41(2)：185-188,170.
[225]司永胜,乔军,刘刚,等.苹果采摘机器人果实识别与定位方法[J].农业机械学报,2010,41(9)：148-153.
[226]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取[J].农业机械学报, 2009,40(8)：161-165,73.
[227]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[J].农业工程学报,2010,26(7)：157-162.
[228]Chi Y T, Ling P P. Fast fruit identification for robotic tomato picker: ASAE Annual International Meeting 2004, Ottawa, ON, Canada,2004[C].
[229]Zhang F, Zhang N. Applying joint transform correlator in tomato recognition:American Society of Agricultural and Biological Engineers Annual International Meeting 2008,2950 Niles Road, St. Joseph, MI 49085-9659, United States,2008[C].
[230]谢志勇,张铁中,赵金英.基于Hough变换的成熟草莓识别技术[J].农业机械学报,2007,38(3)：106-109.
[231]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[J].农业工程学报,2006,22(10)：149-153.
[232]Limsiroratana S, Ikeda Y. On image analysis for harvesting tropical fruits:SICE 2002. Proceedings of the 41st SICE Annual Conference,2002[C].
[233]Gonzalez Rafael C., Woods Richard E., Eddins Steven L.,等.数字图像处理MATLAB版[M].电子工业出版社,2005.
[234]尹建军.基于颜色差异的果实自动识别方法与双目定位技术研究[D].江苏大学,2006.
[235]方如明,蔡健荣,许俐.计算机图像处理技术及其在农业工程中的应用[M].1999.
[236]Molto E, Martin B, Gutierrez A. Design and testing of an automatic machine for spraying at a constant distance from the tree canopy[J]. Journal of Agricultural Engineering Research, 2000,77(4):379-384.
[237]邹建军,曾爱军,何雄奎,等.果园自动对靶喷雾机红外探测控制系统的研制[J].农业工程学报,2007(01).
[238]Astrand B, Baerveldt A J. An agricultural mobile robot with vision-based perception for mechanical weed control[J]. Autonomous Robots,2002,13(1):21-35.
[239]卢蓉,范勇,陈念年,等.一种提取目标图像最小外接矩形的快速算法[J].计算机工程,2010,36(21)：178-180.
[240]Chin R T, WanD L H K, Iverson R D. A one-pass thinning algorithm and its parallel implementation[J]. Computer Vision, Graphics, and Image Processing,1987,40(1):30-40.
[241]Guo Z, Hall R W. Fast fully parallel thinning algorithms[J]. CVGIP: Image Understanding, 1992,55(3):317-328.
[242]Zhang T Y, Suen C Y. A fast thinning algorithm for thinning digital patterns[J]. Communications of ACM,1984,27(3):236-239.
[243]Guo Z, Hall R W. Parallel thinning with two-subiteration algorithms[J]. Communications of the ACM,1989,32(3):359-373.
[244]杨万海.多传感器数据融合及其应用[M].西安电子科技大学出版社,2004.
[245]何友,王国宏,彭应宁,等.多传感器信息融合及应用[M].电子工业出版社,2000.
[246]王耀南,李树涛.多传感器信息融合及其应用综述[J].控制与决策,2001,16(5)：518-522.
[247]刘继展.番茄采摘机器人真空吸持系统分析与优化控制研究[D].江苏大学,2010.
[248]胥芳,张立彬,计时鸣,等.农业机器人视觉传感系统的实现与应用研究[J].农业工程学报,2002,18(4)：180-185.
[249]Jimenez A R, Ceres R, Pons J L. A survey of computer vision methods for locating fruit on trees[J]. Transactions of the American Society of Agricultural Engineers, 2000,43(6):1911-1920.
[250]章毓晋.图像工程(下)：图像理解与计算机视觉[M].北京：清华大学出版社,2000.
[251]Buemi F, Massa M, Sandini G. Agrobot: a robotic system for greenhouse operations: 4th Workshop on robotics in Agriculture, IARP, Tolouse,1995 [C].
[252]Zhang Z. A flexible new technique for camera calibration[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2000,22(11):1330-1334.
[253]王新忠,毛罕平,仲琴,等.番茄收获机器人视觉系统标定技术的研究[J].农机化研究,2006(9)：80-82.
[254]陈树人,戈志勇,王新忠.基于MATLAB的番茄采摘机械手运动学仿真研究：中国农业机械学会2006年学术年会,中国江苏镇江,2006[C].
[255]陈树人,戈志勇,王新忠,等.番茄采摘机械手C-空间障碍计算方法[J].农业机械学报,2008,39(1)：191-194.
[256]尹建军.果蔬精确采摘的双目视觉伺服研究[D].江苏大学,2011.
[257]Astrand B, Baerveldt A J. A vision based row-following system for agricultural field machinery[J]. Mechatronics,2005,15(2):251-269.
[258]Gottschalk R, Burgos-Artizzu X P, Ribeiro A, et al. Real-time image processing for the guidance of a small agricultural field inspection vehicle[J]. International Journal of Intelligent Systems Technologies and Applications,2010,8(1):434-443.
[259]Subramanian V, Burks T F, Arroyo A A. Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation [J]. Computers and Electronics in Agriculture,2006,53(2):130-143.
[260]Finlayson G D, Hordley S D, Lu C, et al. On the removal of shadows from images[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2006,28(1):59-68.
[261]Leemans V, Destain M F. Application of the Hough transform for seed row localisation using machine vision[J]. Biosystems engineering,2006,94(3):325-336.
[262]丁幼春,王书茂,陈度.基于图像旋转投影的导航路径检测算法[J].农业机械学报,2009,40(8)：155-160.
[263]张志斌,罗锡文,李庆,等.基于良序集和垄行结构的农机视觉导航参数提取算法[J].农业工程学报,2007,23(7)：122-126.
[264]张俊雄,曹峥勇,耿长兴,等.温室精准对靶喷雾机器人研制[J].农业工程学报,2009,25(S2)：70-73.
[265]刘洞波,刘国荣,胡慧,等.基于激光测距的温室移动机器人全局定位方法[J].农业机械学报,2010,41(5)：158-163.
[266]袁挺,李伟,谭豫之,等.温室环境下黄瓜采摘机器人信息获取[J].农业机械学报,2009,40(10)：151-155.
[267]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[J].江苏大学学报(自然科学版),2009,30(4)：343-346.
[268]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[J].北京工业大学学报,2008,34(10)：1021-1025.
[269]赵颖,陈兵旗,王书茂,等.基于机器视觉的耕作机器人行走目标直线检测[J].农业机械学报,2006,37(4)：83-86.
[270]安秋,李志臣,姬长英,等.基于光照无关图的农业机器人视觉导航算法[J].农业工程学报,2009,25(11)：208-212.
[271]赵博,王猛,毛恩荣,等.农业车辆视觉实际导航环境识别与分类[J].农业机械学报,2009,40(7)：166-170.
[2]Hayashi S, Shigematsu K, Yamamoto S, et al. Evaluation of a strawberry-harvesting robot in a field test[J]. Biosystems Engineering,2010,105(2):160-171.
[3]Sanders K F. Orange harvesting systems review[J]. Biosystems Engineering, 2005,90(2):115-125.
[4]神内新疆食品研究开发中心,中国农业科学院蔬菜花卉研究所,石河子大学,NY/T1517-2007加工用番茄[S].行业标准-农业,2007.
[5]中国农产品市场协会,农业部蔬菜品质监督检验测试中心(北京).NY/T 940-2006番茄等级规格[S].行业标准-农业(CSIC-NY),2006.
[6]Feng J, Wang S W, Liu G, et al. A Separating Method of Adjacent Apples Based on Machine Vision and Chain Code Information[M]//Computer and Computing Technologies In Agriculture V, PT I.2012:258-267.
[7]Ji W, Zhao D, Cheng F Y, et al. Automatic recognition vision system guided for apple harvesting robot[J]. Computers & Electrical Engineering,2012,38(5SI):1186-1195.
[8]Arefi A, Motlagh A M, Mollazade K, et al. Recognition and localization of ripen tomato based on machine vision[J]. Australian Journal of Crop Science,2011,5(10):1144-1149.
[9]Blanes C, Mellado M, Ortiz C, et al. Technologies for robot grippers in pick and place operations for fresh fruits and vegetables[J]. Spanish Journal of Agricultural Research, 2011,9(4):1130-1141.
[10]Dong F H, Heinemann W, Kasper R. Nonlinear Control Design for Row Guidance System of an Automated Asparagus Harvesting Robot[M]//2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).2011:1087-1092.
[11]He B, Liu G, Ji Y, et al. Auto Recognition of Navigation Path for Harvest Robot Based on Machine Vision[M]//Computer and Computing Technologies in Agriculture IV, PT 1. 2011:138-148.
[12]Li P L, Lee S H, Hsu H Y. Study on citrus fruit image data separability by segmentation methods[M]//PEEA 2011.2011.
[13]Reis M, Morais R, Pereira C, et al. A Low-Cost System to Detect Bunches of Grapes in Natural Environment from Color Images[M]//Advanced Concepts for Intelligent Vision Systems.2011:92-102.
[14]Wang X Z, Mao H P, Han X, et al. Vision-based judgment of tomato maturity under growth conditions[J]. African Journal of Biotechnology,2011,10(18):3616-3623.
[15]Yang H Q, Kuang B Y, Mouazen A M. In Situ Determination of Growing Stages and Harvest Time of Tomato (Lycopersicon esculentum) Fruits Using Fiber-Optic Visible-Near-Infrared (Vis-NIR) Spectroscopy[J]. Applied Spectroscopy,2011,65(8): 931-938.
[16]Zhao D A, Lv J D, Ji W, et al. Design and control of an apple harvesting robot[J]. Biosystems Engineering,2011,110(2):112-122.
[17]Hayashi S, Shigematsu K, Yamamoto S, et al. Evaluation of a strawberry-harvesting robot in a field test[J]. Biosystems Engineering,2010,105(2):160-171.
[18]Lu Q A, Lu H Z, Cai J R, et al. Feature extraction of near-spherical fruit with partial occlusion for robotic harvesting[J]. Maejo International Journal of Science and Technology, 2010,4(3):435-445.
[19]Van Henten E J, Schenk E J, van Willigenburg L G, et al. Collision-free inverse kinematics of the redundant seven-link manipulator used in a cucumber picking robot[J]. Biosystems Engineering,2010,106(2):112-124.
[20]蔡健荣,孙海波,李永平,等.基于双目立体视觉的果树三维信息获取与重构[J].农业机械学报,2012(03)：152-156.
[21]陈军,王虎,蒋浩然,等.猕猴桃采摘机器人末端执行器设计[J].农业机械学报,2012(10)：151-154.
[22]陈燕,蔡伟亮,向和平,等.面向机器人采摘的荔枝果梗力学特性[J].农业工程学报,2012(21)：53-58.
[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[J].农业机械学报,2012(06)：153-160.
[24]李斌,王海峰,黄文倩,等.菠萝采收机械低成本双目视觉平台搭建与田间试验(英文)[J].农业工程学报,2012(S2)：188-192.
[25]李寒,王库,曹倩,等.基于机器视觉的番茄多目标提取与匹配[J].农业工程学报,2012(05)：168-172.
[26]李立君,李昕,高自成,等.基于偏好免疫网络的油茶果采摘机器人图像识别算法[J].农业机械学报,2012(11)：209-213.
[27]李昕,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[J].农业工程学报,2012(14)：133-137.
[28]吕继东,赵德安,姬伟,等.苹果采摘机器人对振荡果实的快速定位采摘方法[J].农业工程学报,2012(13)：48-53.
[29]吕继东,赵德安,姬伟,等.采摘机器人振荡果实动态识别[J].农业机械学报,2012(05)：173-178.
[30]王海峰,李斌,刘广玉,等.菠萝采摘机械手的设计与试验(英文)[J].农业工程学报,2012(S2)：42-46.
[31]项荣,应义斌,蒋焕煜,等.基于双目立体视觉的番茄定位[J].农业工程学报,2012(05)：161-167.
[32]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[J].农业工程学报, 2012(14)：36-41.
[33]张凯良,杨丽,王粮局,等.高架草莓采摘机器人设计与试验[J].农业机械学报,2012(09)：165-172.
[34]崔鹏,陈志,张小超.苹果采摘机器人仿生机械手静力学分析与仿真[J].农业机械学报,2011(02)：149-153.
[35]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[J].农业机械学报,2011(02)：154-157.
[36]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[J].机器人,2011(06)：726-730.
[37]卢伟,宋爱国,蔡健荣,等.柑橘采摘机器人结构设计及运动学算法[J].东南大学学报(自然科学版),2011(01)：95-100.
[38]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[J].农业工程学报,2011(04)：345-349.
[39]叶敏,邹湘军,蔡沛锋,等.水果采摘机器人通用夹持机构设计[J].农业机械学报,2011(S1)：177-180.
[40]张凯良,杨丽,张铁中.草莓收获机器人采摘执行机构设计与试验[J].农业机械学报,2011(09)：155-161.
[41]周俊,张高阳,刘锐,等.基于粒子滤波的苹果采摘机器人目标姿态估算[J].农业机械学报,2011(03)：161-165.
[42]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[J].农业机械学报,2010(11)：153-157.
[43]冯青春,纪超,张俊雄,等.黄瓜采摘机械臂结构优化与运动分析[J].农业机械学报,2010(S1)：244-248.
[44]居洪玲,姬长英.一种多用途采摘机器人末端执行器的设计[J].浙江农业学报,2010,22(3)：370-373.
[45]李斌,汪懋华,李莉.基于单目视觉的田间菠萝果实识别[J].农业工程学报,2010(10)：345-349.
[46]李理,殷国富,刘柯岐.田间果蔬采摘机器人视觉传感器设计与试验[J].农业机械学报,2010(5)：152-157.
[47]李智国,刘继展,李萍萍.机器人采摘中番茄力学特性与机械损伤的关系[J].农业工程学报,2010(5)：112-116.
[48]刘继展,李萍萍,倪齐,等.番茄采摘机器人真空吸盘装置设计与试验[J].农业机械学报,2010(10)：170-173.
[49]刘兆祥,刘刚,乔军.苹果采摘机器人三维视觉传感器设计[J].农业机械学报,2010(2)：171-175.
[50]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[J].农业工程学报, 2010(7)：107-112.
[51]司永胜,乔军,刘刚,等.苹果采摘机器人果实识别与定位方法[J].农业机械学报,2010(9)：148-153.
[52]屠珺,刘成良,李彦明,等.基于光照无关图的苹果图像识别方法[J].农业工程学报,2010(S2)：26-31.
[53]吴刚,谭或,郑永军,等.基于改进Hough变换的收获机器人行走目标直线检测[J].农业机械学报,2010(2)：176-179.
[54]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[J].农业机械学报,2010(4)：151-156.
[55]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[J].农业机械学报,2010(6)：158-162.
[56]Van Henten E J, Hemming J, Van Tuijl B, et al. Collision-free motion planning for a cucumber picking robot[J]. Biosystems Engineering,2003,86(2):135-144.
[57]van Henten E J, Hemming J, van Tuijl B, et al. An autonomous robot for harvesting cucumbers in greenhouses[J]. Autonomous Robots,2002,13(3):241-258.
[58]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[C].
[59]Yin H, Chai Y, Yang S X, et al. Ripe tomato detection for robotic vision harvesting systems in greenhouses[J]. Transactions of the ASABE,2011,54(4):1539-1546.
[60]蔡健荣,赵杰文,Rath Thomas,等.水果收获机器人避障路径规划[J].农业机械学报,2007,38(3)：102-105,135.
[61]梁喜凤,王永维,苗香雯.番茄收获机械手避障运动规划[J].农业机械学报,2008,39(11)：89-93.
[62]魏新华,丁为民,陈玉仑,等.茄子收获机器人机械臂避障路径规划[J].农业机械学报,2008,39(11)：94-98.
[63]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[J].农业工程学报,2006,22(10)：149-153.
[64]Slaughter D C, Harrell R C. Color vision in robotic fruit harvesting[J]. Transactions of the ASAE,1987,30(4):1144-1148.
[65]Schertz C E, Brown G K. Basic considerations in mechanizing citrus harvest[J]. Transactions of the ASAE,1968,11(2):343-346.
[66]Slaughter D C, Harrell R C. Discriminating fruit for robotic harvest using color in natural outdoor scenes[J]. Transactions of the ASAE,1989,32(2):757-763.
[67]Harrell R C, Slaughter D C, Adsit P D. Fruit-tracking system for robotic harvesting[J]. Machine Vision and Applications,1989,2(2):69-80.
[68]徐惠荣,应义斌.红外热成像在树上柑桔识别中的应用研究[J].红外与毫米波学报, 2004,23(5)：353-356.
[69]徐惠荣,叶尊忠,应义斌.基于彩色信息的树上柑橘识别研究[J].农业工程学报,2005,21(5)：98-101.
[70]Kurtulmus F, Lee W S, Vardar A. Green citrus detection using'eigenfruit', color and circular Gabor texture features under natural outdoor conditions[J]. Computers and Electronics in Agriculture,2011,78(2):140-149.
[71]Parrish Jr. E A, Goksel A K. Pictorial pattern recognition applied to fruit harvesting. [J]. Transactions of the ASAE,1977,20(5):822-827.
[73]D'Esnon A G, Rabatel G, Pellenc R, et al. Magali: A self-propelled robot to pick apples[J]. American Society of Agricultural Engineers,1987.
[74]Rabatel G. A vision system for Magali, the fruit picking robot: Int. Conf. Agricultural Engineering, Paris,1988[C].
[76]Rakun J, Stajnko D, Zazula D. Detecting fruits in natural scenes by using spatial-frequency based texture analysis and multiview geometry[J]. Computers and Electronics in Agriculture, 2011,76(1):80-88.
[77]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取[J].农业机械学报,2009,40(8)：161-165,73.
[78]司永胜,刘刚,高瑞.基于K-均值聚类的绿色苹果识别技术[J].农业机械学报,2009,40(z1)：100-104.
[79]司永胜,乔军,刘刚,等.苹果采摘机器人果实识别与定位方法[J].农业机械学报,2010,41(9)：148-153.
[80]Sites P W, Delwiche M J. Computer vision to locate fruit on a tree.[J]. Transactions of the ASAE,1988,31(1):257-263,272.
[81]Liu Y, Chen B, Qiao J. Development of a Machine Vision Algorithm for Recognition of Peach Fruit in a Natural Scene[J]. Transactions of the ASABE,2011,54(2):695-702.
[82]Kapach K, Barnea E, Mairon R, et al. Computer vision for fruit harvesting robots-state of the art and challenges ahead[J]. International Journal of Computational Vision and Robotics, 2012,3(1):4-34.
[83]Van Henten E J, Van Tuijl B A J, Hemming J, et al. Field Test of an Autonomous Cucumber Picking Robot[J]. Biosystems Engineering,2003,86(3):305-313.
[84]张铁中,林宝龙,高锐.水果采摘机器人视觉系统的目标提取[J].中国农业大学学报,2004,9(2)：68-72.
[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[J].中国农业大学学报,2006,11(1)：84-86.
[86]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[J].中国农业大学学报,2005,10(1)：48-51.
[87]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[J].中国农业大学学报,2004,9(4)：65-68.
[88]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[J].中国农业大学学报,2007,12(2)：67-71.
[89]Illingworth J, Kittler J. Survey of the Hough transform[J]. Computer Vision, Graphics, and Image Processing,1988,44(1):87-116.
[90]Whittaker A D, Miles G E, Mitchell O R, et al. Fruit location in a partially occluded image.[J]. Transactions of the ASAE,1987,30(3):591-596.
[91]Kondo N, Nishitsuji Y, Ling P P, et al. Visual feedback guided robotic cherry tomato harvesting[J]. Transactions Of The ASAE,1996,39(6):2331-2338.
[92]张瑞合,姬长英,沈明霞,等.计算机视觉技术在番茄收获中的应用[J].农业机械学报,2001,32(5)：50-52.
[93]蔡健荣,赵杰文.自然场景下成熟水果的计算机视觉识别[J].农业机械学报,2005,36(2)：61-64.
[94]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[J].农业工程学报,2006,22(10)：149-153.
[95]尹建军,王新忠,毛罕平,等.RGB与HSI颜色空间下番茄图像分割的对比研究[J].农机化研究,2006(11)：171-174.
[96]李明喜.基于多源图像融合的收获目标准确定位研究[D].江苏大学农业机械化工程,2008.
[97]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[J].农业工程学报,2008,24(6)：174-178.
[98]陈慧.基于FCM聚类算法的水果图像识别的研究[D].云南大学模式识别与智能系统,2008.
[99]董坦坦.成熟番茄的图像识别及其位姿的获取研究[D].南京农业大学农业电气化与自动化,2009.
[100]陈君,朱红.基于互信息最佳阈值迭代的图像分割方法研究[J].武汉理工大学学报(交通科学与工程版),2011,35(3)：641-644.
[101]郭文川,朱新华,郭康权.果品内在品质无损检测技术的研究进展[J].农业工程学报,2001,17(5)：1-5.
[102]Zhang L, McCarthy M J. Measurement and evaluation of tomato maturity using magnetic resonance imaging[J]. Postharvest Biology and Technology,2012,67(0):37-43.
[103]Van de Poel B, Bulens I, Hertog M L A T, et al. Model-based classification of tomato fruit development and ripening related to physiological maturity[J]. Postharvest Biology and Technology,2012,67(0):59-67.
[104]Wang X, Mao H, Han X, et al. Vision-based judgment of tomato maturity under growth conditions[J]. African Journal of Biotechnology,2011,10(18):3616-3623.
[105]王慧慧,孙永海,刘晶晶,等.基于二维离散小波的鲜玉米果穗成熟度的等级评定[J].吉林大学学报(工学版),2011,41(2)：574-578.
[106]王慧慧,孙永海,张贵林,等.基于压力和图像的鲜玉米果穗成熟度分级方法[J].农业工程学报,2010,26(7)：369-373.
[107]马培恰,吴文,王平,等.贡柑采收期果肉颜色变化与果实成熟度的关系研究[J].广东农业科学,2010,37(10)：63-64.
[108]刘鹏,屠康,苏子鹏,等.基于多传感器融合技术的苹果成熟度检测研究[J].江苏农业学报,2010,26(3)：670-672.
[109]尹建军,毛罕平,王新忠,等.自然条件下番茄成熟度机器人判别模型[J].农业机械学报,2009(10)：146-150.
[110]应义斌,饶秀勤,马俊福.柑橘成熟度机器视觉无损检测方法研究[J].农业工程学报,2004,20(2)：144-147.
[111]张长利,房俊龙,潘伟.用遗传算法训练的多层前馈神经网络对番茄成熟度进行自动检测的研究[J].农业工程学报,2001,17(3)：153-156.
[112]王乐妍,张冬仙,章海军,等.基于激光光致发光光谱的果实成熟度测试方法研究[J].光谱学与光谱分析,2008,28(12)：2772-2776.
[113]周轩明.板栗成熟度指标的筛选及近红外检测方法初探[D].中国农业大学中国农业大学果树,2007.
[114]Clment A, Dorais M, Vernon M. Multivariate approach to the measurement of tomato maturity and gustatory attributes and their rapid assessment by Vis-NIR spectroscopy.[J]. Journal of Agricultural and Food Chemistry,2008,56(5):1538-1544.
[115]张生伟.便携式西瓜成熟度测试装置的研制[D].扬州大学机械制造及其自动化,2010.
[116]贺昀轩.基于超声波的芒果成熟度检测研究[D].广西大学检测技术与自动化装置,2009.
[117]Lien C, Ay C, Ting C. Non-destructive impact test for assessment of tomato maturity[J]. Journal of Food Engineering,2009,91(3):402-407.
[118]王书茂,焦群英,籍俊杰.西瓜成熟度无损检验的冲击振动方法[J].农业工程学报,1999(03).
[119]陈志远,张继澍,刘亚龙,等.番茄成熟度与其电学参数关系的研究[J].西北植物学报,2008(4)：4826-4830.
[120]郭文川,朱新华,周闯鹏,等.西红柿成熟度与电特性关系的无损检测研究[J].农业现代化研究,2002,23(6)：458-460.
[121]Gomez A H, Hu G, Wang J, et al. Evaluation of tomato maturity by electronic nose[J]. Computers and Electronics in Agriculture,2006,54(1):44-52.
[122]周亦斌,王俊.基于电子鼻的番茄成熟度及贮藏时间评价的研究[J].农业工程学报,2005,21(4)：113-117.
[123]张云鹤,乔晓军,王成,等.基于机器视觉的作物茎秆直径变化测量仪研发[J].农机化研究,2011,33(12)：158-160,164.
[124]乔桂新,温维亮,郭新宇,等.植物茎秆可视化研究[J].计算机工程与应用,2011,47(35)：164-167.
[125]毛文华,王辉,赵博,等.基于株心颜色的玉米田间杂草识别方法[J].农业工程学报,2009.
[126]沈宝国,陈树人,尹建军,等.基于颜色特征的棉田绿色杂草图像识别方法[J].农业工程学报,2009(6)：163-167.
[127]毛文华,曹晶晶,姜红花,等.基于多特征的田间杂草识别方法[J].农业工程学报,2007(11).
[128]毛文华,王一鸣,张小超.基于机器视觉的苗期杂草实时分割算法[J].农业机械学报,2005(01).
[129]纪寿文,王荣本,陈佳娟,等.应用计算机图像处理技术识别玉米苗期田间杂草的研究[J].农业工程学报,2001,17(2)：154-156.
[130]吕俊伟,马成林,左春柽.用彩色图像识别田间洋葱和杂草的研究(英文)[J].农业工程学报,2001(05).
[131]Guyer D E, Miles G E, Schreiber M M, et al. Machine vision and image processing for plant identification[J]. Transactions of the ASAE,1986,29(6):1500-1507.
[132]Woebbecke D M, Meyer G E, Von Bargen K, et al. Shape features for identifying young weeds using image analysis[J]. Transactions of the ASAE,1995,38(1):271-281.
[133]Meyer G E, Mehta T, Kocher M F, et al. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying[J]. Transactions of the American Society of Agricultural Engineers,1998,41(4):1189-1197.
[134]吕俊伟,马成林,于永胜.采用多光谱图像融合提高作物和杂草灰度比值[J].农业工程学报,2005(11).
[135]毛文华,王月青,王一鸣,等.苗期作物和杂草的光谱分析与识别[J].光谱学与光谱分析,2005(06).
[136]肖健.果树对靶喷雾系统中图像识别技术[D].中国农业大学农业机械化工程,2005.
[137]赵茂程,郑加强,林小静,等.基于分形理论的树木图像分割方法[J].农业机械学报,2004,35(2)：72-75.
[138]赵茂程,郑加强,林小静,等.基于BP神经网络的树形识别系统研究[J].林业科学,2004,40(1)：154-157.
[139]Lee W S, Slaughter D C, Giles D K. Robotic weed control system for tomatoes using machine vision system and precision chemical application:Paper - American Society of Agricultural Engineers, Minneapolis, MN, USA,1997[C].
[140]周俊.农用轮式移动机器人视觉导航系统的研究[D].南京农业大学南京农业大学农业机械化工程,2003.
[141]罗锡文,臧英,周志艳.精细农业中农情信息采集技术的研究进展[J].农业工程学报,2006,22(1)：167-173.
[142]Tillett N D, Hague T, Miles S J. Inter-row vision guidance for mechanical weed control in sugar beet[J]. Computers and Electronics in Agriculture,2002,33(3):163-177.
[143]Subramanian V, Burks T F, Arroyo A A. Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation[J]. Computers and Electronics in Agriculture,2006,53(2):130-143.
[144]Kise M, Zhang Q, Rovira Mas F. A stereovision-based crop row detection method for tractor-automated guidance[J]. Biosystems Engineering,2005,90(4):357-367.
[145]Kise M, Zhang Q. Development of a stereovision sensing system for 3D crop row structure mapping and tractor guidance[J]. Biosystems Engineering,2008,101(2):191-198.
[146]Sgaard H T, Olsen H J. Determination of crop rows by image analysis without segmentation[J]. Computers and Electronics in Agriculture,2003,38(2):141-158.
[147]Neris L D O, Roda V O, Trindade Jr. O. A method for agricultural machine guidance on row crops based on the vanishing point: 2007 IEEE International Conference on Vehicular Electronics and Safety, ICVES, Beijing, China,2007[C].
[148]Han S, Zhang Q, Ni B, et al. A guidance directrix approach to vision-based vehicle guidance systems[J]. Computers and Electronics in Agriculture,2004,43(3):179-195.
[149]Gottschalk R, Burgos Artizzu X P, Ribeiro A, et al. Real-time image processing for the guidance of a small agricultural field inspection vehicle:15th International Conference on Mechatronics and Machine Vision in Practice, M2VIP'O8, Auckland, New zealand,2008[C].
[150]Astrand B, Baerveldt A J. A vision based row-following system for agricultural field machinery [J]. Mechatronics,2005,15(2):251-269.
[151]Rovira Mas F, Zhang Q, Reid J F, et al. Hough-transform-based vision algorithm for crop row detection of an automated agricultural vehicle[J]. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering,2005,219(8):999-1010.
[152]Bakker T, Wouters H, van Asselt K, et al. A vision based row detection system for sugar beet[J]. Computers and Electronics in Agriculture,2008,60(1):87-95.
[153]Leemans V, Destain M F. Application of the Hough Transform for Seed Row Localisation using Machine Vision[J]. Biosystems Engineering,2006,94(3):325-336.
[154]Leemans V, Destain M F. Line cluster detection using a variant of the Hough transform for culture row localisation[J]. Image and Vision Computing,2006,24(5):541-550.
[155]Leemans V, Destain M F. A computer-vision based precision seed drill guidance assistance[J]. Computers and Electronics in Agriculture,2007,59(1-2):1-12.
[156]周俊,姬长英.基于知识的视觉导航农业机器人行走路径识别[J].农业工程学报, 2003,19(6)：101-105.
[157]周俊,姬长英.农业机器人视觉导航中多分辨率路径识别[J].农业机械学报,2003,34(6)：120-123.
[158]周俊,刘成良,姬长英.农用轮式移动机器人相对位姿的求解方法[J].中国图象图形学报,2005,10(3)：310-314.
[159]Zhang F, Ying Y, Shen C, et al. Stereovision-based 3D field recognition for automatic ghuidance system of off-road vehicle: Proceedings of SPIE - The International Society for Optical Engineering, Boston, MA, United states,2005[C].
[160]Zhang F, Yin Y, Jiang H, et al. Correlation Analysis-based Image Segmentation Approch for Automatic Agriculture Vehicle[J]. Jounral of Zhejiang University,2005,6A(10):1158-1162.
[161]张志斌,罗锡文,周学成,等.基于Hough变换和Fisher准则的垄线识别算法[J].中国图象图形学报,2007,12(12)：2164-2168.
[162]张志斌,罗锡文,李庆,等.基于良序集和垄行结构的农机视觉导航参数提取算法[J].农业工程学报,2007,23(7)：122-126.
[163]张志斌,罗锡文,王在满.基于良序子集的最近邻垄行图像识别算法[J].中国图象图形学报,2007,12(11)：2048-2051.
[164]王晓燕,陈媛,陈兵旗,等.免耕覆盖地科秆行茬导航路径的图像检测[J].农业机械学报,2009,40(6)：158-163.
[165]陈娇,姜国权,杜尚丰,等.基于垄线平行特征的视觉导航多垄线识别[J].农业工程学报,2009,25(12)：107-113.
[166]安秋,李志臣,姬长英,等.基于光照无关图的农业机器人视觉导航算法[J].农业工程学报,2009,25(11)：208-212.
[167]蔡晋辉,才辉,张光新,等.复杂环境下自动导引车路径识别算法[J].农业机械学报,2011,42(10)：20-24,89.
[168]赵博,毛恩荣,毛文华,等.农业车辆杂草环境下视觉导航路径识别方法[J].农业机械学报,2009,40(z1)：183-186.
[169]蒋海波,张云伟,郭志伟,等.室外机器人视觉导航的路径结构识别[J].计算机工程与应用,2010,46(7)：245-248.
[170]王福娟.温室环境视觉导航路径的识别[J].农机化研究,2010,32(6)：83-86.
[171]李娟娟,邱白晶,王天波.温室农用机器人的路径检测[J].农机化研究,2011,33(3)：151-154.
[172]蔡健荣,赵杰文,Rath Thomas,等.水果收获机器人避障路径规划[J].农业机械学报,2007,38(3)：102-105,135.
[173]梁喜凤,苗香雯,崔绍荣,等.番茄收获机械手运动学优化与仿真试验[J].农业机械学报,2005,36(7)：96-100.
[174]梁喜凤.番茄收获机械手机构分析与优化设计研究[D].浙江大学农业生物环境与能源 工程,2004.
[175]徐毓良,杨起帆.机器人三维空间无碰撞路径规划[J].机器人,1991,13(5)：1-6.
[176]Van Henten E J, Hemming J, Van Tuijl B A J, et al. Collision-free Motion Planning for a Cucumber Picking Robot[J]. Biosystems Engineering,2003,86(2):135-144.
[177]Lozano-Perez T. A simple motion-planning algorithm for general robot manipulators[J]. Robotics and Automation, IEEE Journal of,1987,3(3):224-238.
[178]Schwartz J T, Sharir M. On the'piano movers'problem-1:The case of a two-dimensional rigid polygonal body moving amidst polygonal barriers[J]. Communications on pure and applied mathematics,1983,36(3):345-398.
[179]王伟.障碍环境下机器人无碰自主路径规划的研究[D].哈尔滨工业大学机电控制及自动化,1997.
[180]陈靖波,赵猛,张珩.空间机械臂在线实时避障路径规划研究[J].控制工程,2007,14(4)：445-447,450.
[181]陈树人,戈志勇,王新忠,等.番茄采摘机械手C-空间障碍计算方法[J].农业机械学报,2008,39(1)：191-194.
[182]Lozano-Perez T. Spatial planning: A configuration space approach[J]. Computers, IEEE Transactions on,1983,100(2):108-120.
[183]Hart P E, Nilsson N J, Raphael B. A formal basis for the heuristic determination of minimum cost paths[J]. Systems Science and Cybernetics, IEEE Transactions on,1968,4(2):100-107.
[184]USDA. United States standards for grades of fresh tomatoes.[S].1991.
[185]北京市蔬菜贮藏加工研究所.SB/T 10331-2000番茄[S].行业标准-商业(CSIC-SB), 2000.
[186]斋藤隆,片同節男,王海廷.番茄生理基础[M].上海：上海科学技术出版社,1981.
[187]Smith A R. Color gamut transform pairs: Proceedings of the 5th Annual Conference on Computer Graphics and Interactive Techniques, New York, USA,1978[C]. ACM.
[188]Choi K, Lee G, Han Y J, et al. Tomato maturity evaluation using color image analysis[J], Transactions of American Society of Agricultural Engineers,1995,38(1):171.
[189]Gejima Y, Nagata M, Kenji H. Judging of tomato maturity by utilizing a low-resolution RGB color system: ASAE Annual International Meeting, Ottawa, Canada,2004[C].
[190]Gejima Y, Zhang H, Nagata M. Judgment on level of maturity for tomato quality using L* a* b* color image processing: Proceedings of 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics,2003[C].
[191]Syahrir W M, Suryanti A, Connsynn C. Color grading in tomato maturity estimator using image processing technique:2nd IEEE International Conference on Computer Science and Information Technology,2009.,2009[C].
[192]Zhang Y, Yin X, Zou X, et al. On-line Sorting Maturity of Cherry Tomato by Machine Vision:International Conference on Computer and Computing Technologies in Agriculture, Beijing,China,2008[C].
[193]张长利,房俊龙,潘伟.用遗传算法训练的多层前馈神经网络对番茄成熟度进行自动检测的研究[J].农业工程学报,2001,17(3)：153-156.
[194]尹建军,毛罕平,王新忠,等.自然条件下番茄成熟度机器人判别模型[J].农业机械学报,2009,40(10)：146-150,168.
[195]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[J].农业机械学报,2004,35(5)：122-124,135.
[196]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[J].计算机工程,2004,30(22)：155-156,171.
[197]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[J].农业工程学报,2006,22(10)：149-153.
[198]蒋焕煜,彭永石,申川,等.基于双目立体视觉技术的成熟番茄识别与定位[J].农业工程学报,2008,24(8)：279-283.
[199]董坦坦,姬长英,周俊,等.成熟番茄的图像识别及其位姿的获取研究[J].江西农业学报,2009,21(8)：152-155.
[200]Yin J, Mao H, Hu Y, et al. An automatic segmentation method for multi-tomatoes image under complicated natural background:Proceedings of SPIE-The International Society for Optical Engineering, Goa, India,2006[C].
[201]Donghai Han,王加华.水果内部品质近红外光谱无损检测研究进展[J].中国激光,2008,35(8)：1123-1131.
[202]谢丽娟,应义斌,于海燕,等.近红外光谱分析技术在蔬菜品质无损检测中的应用研究进展[J].光谱学与光谱分析,2007,27(6)：1131-1135.
[203]施佳慧,陈自力,邵咏妮,等.可见/近红外光谱分析技术快速鉴别航天育种番茄[J].光谱学与光谱分析,2011,31(2)：387-389.
[204]谢丽娟,应义斌,应铁进,等.可见/近红外光谱分析技术鉴别转基因番茄叶[J].光谱学与光谱分析,2008,28(5)：1062-1066.
[205]陆辉山,傅霞萍,谢丽娟,等.可见/近红外光估测完整柑橘水果可溶性固形物含量的研究[J].光谱学与光谱分析,2007,27(9)：1727-1730.
[206]夏俊芳,李小昱,李培武,等.基于小波变换的柑橘维生素C含量近红外光谱无损检测方法[J].农业工程学报,2007,23(6)：170-174.
[207]傅霞萍,应义斌,刘燕德,等.水果坚实度的近红外光谱检测分析试验研究[J].光谱学与光谱分析,2006,26(6)：1038-1041.
[208]应义斌,刘燕德,傅霞萍.基于小波变换的水果糖度近红外光谱检测研究[J].光谱学与光谱分析,2006,26(1)：63-66.
[209]史波林,赵镭,刘文,等.苹果内部品质近红外光谱检测的异常样本分析[J].农业机械学报,2010,41(2)：132-137.
[210]邓烈,何绍兰,易时来,等.与哈姆林甜橙果实内在品质主要参数同步相关的特征光谱研究[J].光谱学与光谱分析,2010,30(4)：1049-1052.
[211]马兰,夏俊芳,张战锋,等.基于小波变换的番茄总糖近红外无损检测[J].农业工程学报,2009,25(10)：350-354.
[212]柳建良,黄桂颖,舒永培,等.贡柑不同采收期果实可溶性糖红外光谱学研究[J].西北农业学报,2009,18(1)：234-237.
[213]马兰,夏俊芳,张战锋,等.光谱预处理对近红外光谱无损检测番茄可溶性固形物含量的影响[J].华中农业大学学报,2008,27(5)：672-675.
[214]黄康,汀辉君,徐惠荣,等.基于最小二乘支持向量机的番茄汁糖酸度分析研究[J].光谱学与光谱分析,2009,29(4)：931-934.
[215]Norgaard L, Saudland A, Wagner J, et al. Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy[J]. Applied Spectroscopy,2000,54(3):413-419.
[216]Bulanon D M, Kataoka T, Ota Y, et al. Segmentation algorithm for the automatic recognition of Fuji apples at harvest[J]. BIOSYSTEMS ENGINEERING,2002,83(4):405-412.
[217]Plebe A, Grasso G. Localization of spherical fruits for robotic harvesting[J]. Machine Vision and Applications,2001,13(2):70-79.
[218]Slaughter D C, Harrell R C. Discriminating fruit for robotic harvest using color in natural outdoor scenes[J]. Transactions of the American Society of Agricultural Engineers, 1989,32(2):757-763.
[219]Tabb A L, Peterson D L, Park J. Segmentation of apple fruit from video via background modeling: 2006 ASABE Annual International Meeting, Portland, OR, United states, 2006[C].
[220]Zhao J, Tow J, Katupitiya J. On-tree fruit recognition using texture properties and color data: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, Edmonton, AB, Canada,2005[C].
[221]蔡健荣,范军,李玉良,等.基于遗传算法的树上柑桔形状特征提取[J].江苏大学学报(自然科学版),2007,28(6)：469-472.
[222]蔡健荣,李玉良,范军,等.成熟柑橘的图像识别及空间定位研究[J].微计算机信息,2007,23(34)：224-225,314.
[223]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[J].农业工程学报,2008,24(1)：175-178.
[224]吕强,蔡健荣,赵杰文,等.自然场景下树上柑橘实时识别技术[J].农业机械学报,2010,41(2)：185-188,170.
[225]司永胜,乔军,刘刚,等.苹果采摘机器人果实识别与定位方法[J].农业机械学报,2010,41(9)：148-153.
[226]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取[J].农业机械学报, 2009,40(8)：161-165,73.
[227]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[J].农业工程学报,2010,26(7)：157-162.
[228]Chi Y T, Ling P P. Fast fruit identification for robotic tomato picker: ASAE Annual International Meeting 2004, Ottawa, ON, Canada,2004[C].
[229]Zhang F, Zhang N. Applying joint transform correlator in tomato recognition:American Society of Agricultural and Biological Engineers Annual International Meeting 2008,2950 Niles Road, St. Joseph, MI 49085-9659, United States,2008[C].
[230]谢志勇,张铁中,赵金英.基于Hough变换的成熟草莓识别技术[J].农业机械学报,2007,38(3)：106-109.
[231]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[J].农业工程学报,2006,22(10)：149-153.
[232]Limsiroratana S, Ikeda Y. On image analysis for harvesting tropical fruits:SICE 2002. Proceedings of the 41st SICE Annual Conference,2002[C].
[233]Gonzalez Rafael C., Woods Richard E., Eddins Steven L.,等.数字图像处理MATLAB版[M].电子工业出版社,2005.
[234]尹建军.基于颜色差异的果实自动识别方法与双目定位技术研究[D].江苏大学,2006.
[235]方如明,蔡健荣,许俐.计算机图像处理技术及其在农业工程中的应用[M].1999.
[236]Molto E, Martin B, Gutierrez A. Design and testing of an automatic machine for spraying at a constant distance from the tree canopy[J]. Journal of Agricultural Engineering Research, 2000,77(4):379-384.
[237]邹建军,曾爱军,何雄奎,等.果园自动对靶喷雾机红外探测控制系统的研制[J].农业工程学报,2007(01).
[238]Astrand B, Baerveldt A J. An agricultural mobile robot with vision-based perception for mechanical weed control[J]. Autonomous Robots,2002,13(1):21-35.
[239]卢蓉,范勇,陈念年,等.一种提取目标图像最小外接矩形的快速算法[J].计算机工程,2010,36(21)：178-180.
[240]Chin R T, WanD L H K, Iverson R D. A one-pass thinning algorithm and its parallel implementation[J]. Computer Vision, Graphics, and Image Processing,1987,40(1):30-40.
[241]Guo Z, Hall R W. Fast fully parallel thinning algorithms[J]. CVGIP: Image Understanding, 1992,55(3):317-328.
[242]Zhang T Y, Suen C Y. A fast thinning algorithm for thinning digital patterns[J]. Communications of ACM,1984,27(3):236-239.
[243]Guo Z, Hall R W. Parallel thinning with two-subiteration algorithms[J]. Communications of the ACM,1989,32(3):359-373.
[244]杨万海.多传感器数据融合及其应用[M].西安电子科技大学出版社,2004.
[245]何友,王国宏,彭应宁,等.多传感器信息融合及应用[M].电子工业出版社,2000.
[246]王耀南,李树涛.多传感器信息融合及其应用综述[J].控制与决策,2001,16(5)：518-522.
[247]刘继展.番茄采摘机器人真空吸持系统分析与优化控制研究[D].江苏大学,2010.
[248]胥芳,张立彬,计时鸣,等.农业机器人视觉传感系统的实现与应用研究[J].农业工程学报,2002,18(4)：180-185.
[249]Jimenez A R, Ceres R, Pons J L. A survey of computer vision methods for locating fruit on trees[J]. Transactions of the American Society of Agricultural Engineers, 2000,43(6):1911-1920.
[250]章毓晋.图像工程(下)：图像理解与计算机视觉[M].北京：清华大学出版社,2000.
[251]Buemi F, Massa M, Sandini G. Agrobot: a robotic system for greenhouse operations: 4th Workshop on robotics in Agriculture, IARP, Tolouse,1995 [C].
[252]Zhang Z. A flexible new technique for camera calibration[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2000,22(11):1330-1334.
[253]王新忠,毛罕平,仲琴,等.番茄收获机器人视觉系统标定技术的研究[J].农机化研究,2006(9)：80-82.
[254]陈树人,戈志勇,王新忠.基于MATLAB的番茄采摘机械手运动学仿真研究：中国农业机械学会2006年学术年会,中国江苏镇江,2006[C].
[255]陈树人,戈志勇,王新忠,等.番茄采摘机械手C-空间障碍计算方法[J].农业机械学报,2008,39(1)：191-194.
[256]尹建军.果蔬精确采摘的双目视觉伺服研究[D].江苏大学,2011.
[257]Astrand B, Baerveldt A J. A vision based row-following system for agricultural field machinery[J]. Mechatronics,2005,15(2):251-269.
[258]Gottschalk R, Burgos-Artizzu X P, Ribeiro A, et al. Real-time image processing for the guidance of a small agricultural field inspection vehicle[J]. International Journal of Intelligent Systems Technologies and Applications,2010,8(1):434-443.
[259]Subramanian V, Burks T F, Arroyo A A. Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation [J]. Computers and Electronics in Agriculture,2006,53(2):130-143.
[260]Finlayson G D, Hordley S D, Lu C, et al. On the removal of shadows from images[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on,2006,28(1):59-68.
[261]Leemans V, Destain M F. Application of the Hough transform for seed row localisation using machine vision[J]. Biosystems engineering,2006,94(3):325-336.
[262]丁幼春,王书茂,陈度.基于图像旋转投影的导航路径检测算法[J].农业机械学报,2009,40(8)：155-160.
[263]张志斌,罗锡文,李庆,等.基于良序集和垄行结构的农机视觉导航参数提取算法[J].农业工程学报,2007,23(7)：122-126.
[264]张俊雄,曹峥勇,耿长兴,等.温室精准对靶喷雾机器人研制[J].农业工程学报,2009,25(S2)：70-73.
[265]刘洞波,刘国荣,胡慧,等.基于激光测距的温室移动机器人全局定位方法[J].农业机械学报,2010,41(5)：158-163.
[266]袁挺,李伟,谭豫之,等.温室环境下黄瓜采摘机器人信息获取[J].农业机械学报,2009,40(10)：151-155.
[267]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[J].江苏大学学报(自然科学版),2009,30(4)：343-346.
[268]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[J].北京工业大学学报,2008,34(10)：1021-1025.
[269]赵颖,陈兵旗,王书茂,等.基于机器视觉的耕作机器人行走目标直线检测[J].农业机械学报,2006,37(4)：83-86.
[270]安秋,李志臣,姬长英,等.基于光照无关图的农业机器人视觉导航算法[J].农业工程学报,2009,25(11)：208-212.
[271]赵博,王猛,毛恩荣,等.农业车辆视觉实际导航环境识别与分类[J].农业机械学报,2009,40(7)：166-170.