参考文献/References:
[1]张文翔, 张兵园, 贡 宇, 等. 果蔬采摘机器人机械臂研究现状与展望[J]. 中国农机化学报, 2022,43(9): 232-237.
[2]WANG Z H, XUN Y, WANG Y K, et al. Review of smart robots for fruit and vegetable picking in agriculture[J]. International Journal of Agricultural and Biological Engineering, 2021,14(6): 33-54.
[3]VERBIEST R, RUYSEN K, VANWALLEGHEM T, et al. Automation and robotics in the cultivation of pome fruit: where do we stand today?[J]. Journal of Field Robotics, 2021,38(4): 513-531.
[4]BAI Y H, GUO Y X, ZHANG Q, et al. Multi-network fusion algorithm with transfer learning for green cucumber segmentation and recognition under complex natural environment[J]. Computers and Electronics in Agriculture, 2022,194: 1-27.
[5]姚成胜,肖雅雯,杨一单. 农业劳动力转移与农业机械化对中国粮食生产的关联影响分析[J]. 农业现代化研究, 2022,43(2): 1-15.
[6]MONTOYA-CAVERO L, DAZ DE LEN TORRES R, GMEZ-ESPINOSA A, et al. Vision systems for harvesting robots: produce detection and localization[J]. Computers and Electronics in Agriculture, 2022,192: 1-27.
[7]李会宾,史云. 果园采摘机器人研究综述[J]. 中国农业信息, 2019,31(6): 1-9.
[8]刘妤,刘洒,杨长辉,等. 基于双目立体视觉的重叠柑橘空间定位[J]. 中国农业科技导报, 2020,22(9): 104-112.
[9]JUN J, KIM J, SEOL J, et al. Towards an efficient tomato harvesting robot: 3D perception, manipulation, and end-effector[J]. IEEE Access, 2021,9: 17631-17640.
[10]LV J D, WANG Y J, NI H M, et al. Method for discriminating of the shape of overlapped apple fruit images[J]. Biosystems Engineering, 2019,186: 118-129.
[11]刘振宇,丁宇祺. 自然环境中被遮挡果实的识别方法研究[J]. 计算机应用研究, 2020,37(S2): 333-335.
[12]何斌,张亦博,龚健林,等. 基于改进YOLOv5的夜间温室番茄果实快速识别[J]. 农业机械学报, 2022,53(5): 1-10.
[13]雷旺雄,卢军. 葡萄采摘机器人采摘点的视觉定位[J]. 江苏农业学报, 2020,36(4): 1015-1021.
[14]陈志健,伍德林,刘路,等. 复杂背景下油茶果采收机重叠果实定位方法研究[J]. 安徽农业大学学报, 2021,48(5): 842-848.
[15]汪杰,陈曼龙,李奎,等. 基于HSV与形状特征融合的花椒图像识别[J]. 中国农机化学报, 2021,42(10): 180-185.
[16]杨帆,李鹏飞,刘庚,等. 橘子采摘机器人目标识别定位方法与实验研究[J]. 西安理工大学学报, 2018,34(4): 460-467.
[17]王瑾,王瑞荣,李晓红. 番茄采摘机器人目标识别方法研究[J]. 江苏农业科学, 2021,49(20): 217-222.
[18]赵立新,邢润哲,白银光,等. 深度学习在目标检测的研究综述[J]. 科学技术与工程, 2021,21(30): 12787-12795.
[19]包晓敏,王思琪. 基于深度学习的目标检测算法综述[J]. 传感器与微系统, 2022,41(4): 5-9.
[20]杨长辉,刘艳平,王毅,等. 自然环境下柑橘采摘机器人识别定位系统研究[J]. 农业机械学报, 2019,50(12): 14-22.
[21]傅隆生,冯亚利,ELKAMIL T,等. 基于卷积神经网络的田间多簇猕猴桃图像识别方法[J]. 农业工程学报, 2018,34(2): 205-211.
[22]TANG Y C, CHEN M Y, WANG C L, et al. Recognition and localization methods for vision-based fruit picking robots: a review[J]. Frontiers in Plant Science, 2020,11: 1-17.
[23]魏宏飞,张季萌. 基于CMOS传感器的采摘机器人计算机视觉系统研究[J]. 农机化研究, 2022,44(12): 221-224.
[24]JIAO Y H, LUO R, LI Q W, et al. Detection and localization of overlapped fruits application in an apple harvesting robot[J]. Electronics, 2020,9(6): 1-14.
[25]MALIK M H, ZHANG T, LI H, et al. Mature tomato fruit detection algorithm based on improved HSV and watershed algorithm[J]. IFAC-PapersOnLine, 2018,51(17): 431-436.
[26]牛晗,伍希志. 基于大津算法连通域的松果多目标识别定位[J]. 江苏农业科学, 2021,49(15): 193-198.
[27]马帅,张艳,周桂红,等. 基于改进YOLOv4模型的自然环境下梨果实识别[J]. 河北农业大学学报, 2022,45(3): 105-111.
[28]XIONG J T, LIU Z, LIN R, et al. Green grape detection and picking-point calculation in a night-time natural environment using a charge-coupled device (CCD) vision sensor with artificial illumination[J]. Sensors, 2018,18(4): 1-17.
[29]毕松,高峰,陈俊文,等. 基于深度卷积神经网络的柑橘目标识别方法[J]. 农业机械学报, 2019,50(5): 181-186.
[30]ZHOU H Y, WANG X, AU W, et al. Intelligent robots for fruit harvesting: recent developments and future challenges[J]. Precision Agriculture, 2022,23(5): 1856-1907.
[31]刘妤,刘洒,杨长辉,等. 基于双目立体视觉的重叠柑橘空间定位[J]. 中国农业科技导报, 2020,22(9): 104-112.
[32]邹朋朋,张滋黎,王平,等. 基于共线向量与平面单应性的双目相机标定方法[J]. 光学学报, 2017,37(11): 244-252.
[33]杨皓天,万腾. 葡萄采摘机械臂的双目定位与抓取精度研究[J]. 农机化研究, 2022,44(12): 49-54.
[34]LING X, ZHAO Y S, GONG L, et al. Dual-arm cooperation and implementing for robotic harvesting tomato using binocular vision[J]. Robotics and Autonomous Systems, 2019,114: 134-143.
[35]庞超凡. 基于双目视觉小金桔果实的识别及定位采摘研究[D]. 郑州:河南农业大学, 2021.
[36]曹春卿,张吴平,李富忠,等. 自然场景下多目标苹果识别定位融合算法研究[J]. 湖北农业科学, 2022,61(7): 145-151.
[37]陈炎,杨丽丽,王振鹏. 双目视觉的匹配算法综述[J]. 图学学报, 2020,41(5): 702-708.
[38]LI Y J, FENG Q C, LI T, et al. Advance of target visual information acquisition technology for fresh fruit robotic harvesting: a review[J]. Agronomy, 2022,12(6): 1-19.
[39]FU L S, GAO F F, WU J Z, et al. Application of consumer RGB-D cameras for fruit detection and localization in field: a critical review[J]. Computers and Electronics in Agriculture, 2020,177: 1-12.
[40]GONGAL A, AMATYA S, KARKEE M, et al. Sensors and systems for fruit detection and localization: a review[J]. Computers and Electronics in Agriculture, 2015,116: 8-19.
[41]SUN Q X, CHAI X J, ZENG Z K, et al. Noise-tolerant RGB-D feature fusion network for outdoor fruit detect[J]. Computers and Electronics in Agriculture, 2022,198: 1-13.
[42]赵辉,李浩,岳有军,等. 基于RGB-D相机的矮砧苹果识别与定位[J]. 计算机工程与设计, 2020,41(8): 2278-2283.
[43]刘景娜. 基于Kinect的移动式番茄生长信息采集系统的研制[D]. 南京:南京农业大学, 2020.
[44]孙宝霞,郑镇辉,胡文馨,等. 基于RGB-D的龙眼实时检测与定位方法[J]. 林业工程学报, 2022,7(3): 150-157.
[45]张勤,陈建敏,李彬,等. 基于RGB-D信息融合和目标检测的番茄串采摘点识别定位方法[J]. 农业工程学报, 2021,37(18): 143-152.
[46]彭孝东,时磊,何静,等. 消费级RGB-D相机在农业领域应用现状与发展趋势[J]. 中国农机化学报, 2022,43(4): 206-215.
[47]彭育辉,江铭,马中原,等. 汽车自动驾驶关键技术研究进展[J]. 福州大学学报(自然科学版), 2021,49(5): 691-701.
[48]罗玉涛,秦瀚. 基于稀疏彩色点云的自动驾驶汽车3D目标检测方法[J]. 汽车工程, 2021,43(4): 492-500.
[49]GEN-MOLA J, GREGORIO E, GUEVARA J, et al. Fruit detection in an apple orchard using a mobile terrestrial laser scanner[J]. Biosystems Engineering, 2019,187: 171-184.
[50]MNDEZ V, PREZ-ROMERO A, SOLA-GUIRADO R, et al. In-field estimation of orange number and size by 3D laser scanning[J]. Agronomy, 2019,9(12): 1-18.
[51]TANG J, JIANG F G, LONG Y, et al. Identification of the yield of camellia oleifera based on color space by the optimized mean shift clustering algorithm using terrestrial laser scanning[J]. Remote Sensing, 2022,14(3): 1-18.
[52]温玉维,邓长勇,曾德培,等. 三维激光扫描仪在电力工程实测实量中的应用[J]. 测绘通报, 2021(10): 163-167.
[53]TSOULIAS N, PARAFOROS D S, XANTHOPOULOS G, et al. Apple shape detection based on geometric and radiometric features using a LiDAR laser scanner[J]. Remote Sensing, 2020,12(15): 1-18.
[54]WU Y T, WANG Y Y, ZHANG S W, et al. Deep 3D object detection networks using LiDAR data: a review[J]. IEEE Sensors Journal, 2021,21(2): 1152-1171.
[55]JIA W K, ZHANG Y, LIAN J, et al. Apple harvesting robot under information technology: a review[J]. International Journal of Advanced Robotic Systems, 2020,17(3): 1-16.
[56]ZHUANG J J, HOU C J, TANG Y, et al. Computer vision-based localisation of picking points for automatic litchi harvesting applications towards natural scenarios[J]. Biosystems Engineering, 2019,187: 1-20.
[57]JIAO Y H, LUO R, LI Q W, et al. Detection and localization of overlapped fruits application in an apple harvesting robot[J]. Electronics, 2020,9(6): 1-14.
[58]宁政通,罗陆锋,廖嘉欣,等. 基于深度学习的葡萄果梗识别与最优采摘定位[J]. 农业工程学报, 2021,37(9): 222-229.
[59]ZHUANG J J, LUO S M, HOU C J, et al. Detection of orchard citrus fruits using a monocular machine vision-based method for automatic fruit picking applications[J]. Computers and Electronics in Agriculture, 2018,152: 64-73.
[60]柳长源,赖楠旭,毕晓君. 基于深度图像的球形果实识别定位算法[J]. 农业机械学报, 2022,53(10): 228-235.
[61]LIN G C, TANG Y C, ZOU X J, et al. Fruit detection in natural environment using partial shape matching and probabilistic Hough transform[J]. Precision Agriculture, 2020,21(1): 160-177.
[62]LIU G X, NOUAZE J C, TOUKO P L, et al. YOLO-Tomato: a robust algorithm for tomato detection based on YOLOv3[J]. Sensors, 2020,20(7): 1-20.
[63]高梦圆,马双宝,董玉婕,等. 基于实例分割苹果采摘机器人视觉定位与检测[J]. 江苏农业科学, 2022,50(3): 201-208.
[64]BENAVIDES M, CANTN-GARBN M, SNCHEZ-MOLINA J A, et al. Automatic tomato and peduncle location system based on computer vision for use in robotized harvesting[J]. Applied Sciences, 2020,10(17): 1-21.
[65]刘芳,刘玉坤,林森,等. 基于改进型YOLO的复杂环境下番茄果实快速识别方法[J]. 农业机械学报, 2020,51(6): 229-237.
[66]ZHONG Z, XIONG J T, ZHENG Z H, et al. A method for litchi picking points calculation in natural environment based on main fruit bearing branch detection[J]. Computers and Electronics in Agriculture, 2021,189: 1-11.
[67]YU Y, ZHANG K l, YANG L, et al. Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN[J]. Computers and Electronics in Agriculture, 2019,163: 1-9.
[68]杨长辉,刘艳平,王毅,等. 自然环境下柑橘采摘机器人识别定位系统研究[J]. 农业机械学报, 2019,50(12): 14-22.
[69]黄彤镔,黄河清,李震,等. 基于YOLOv5改进模型的柑橘果实识别方法[J]. 华中农业大学学报, 2022,41(4): 170-177.
[70]TANG Y F, ZHANG Y W, ZHU Y. A research on the fruit recognition algorithm based on the multi-feature fusion[C]. Harbin: ICMCCE, 2020.
[71]ZHANG C L, ZHANG K F, GE L Z, et al. A method for organs classification and fruit counting on pomegranate trees based on multi-features fusion and support vector machine by 3D point cloud[J]. Scientia Horticulturae, 2021,278: 1-9.
[72]雷欢,焦泽昱,马敬奇,等. 基于多特征融合与SVM的苹果品种快速识别算法[J]. 自动化与信息工程, 2020,41(4): 13-17.
[73]WU J G, ZHANG B H, ZHOU J, et al. Automatic recognition of ripening tomatoes by combining multi-feature fusion with a bi-layer classification strategy for harvesting robots[J]. Sensors, 2019,19(3): 1-22.
[74]汪杰,陈曼龙,李奎,等. 基于HSV与形状特征融合的花椒图像识别[J]. 中国农机化学报, 2021,42(10): 180-185.
[75]WU G, LI B, ZHU Q B, et al. Using color and 3D geometry features to segment fruit point cloud and improve fruit recognition accuracy[J]. Computers and Electronics in Agriculture, 2020,174: 1-8.
[76]杨晓静,张福东,胡长斌. 机器学习综述[J]. 科技经济市场, 2021(10): 40-42.
[77]JIANG T, GRADUS J L, ROSELLINI A J. Supervised machine learning: a brief primer[J]. Behavior Therapy, 2020,51(5): 675-687.
[78]郑太雄,江明哲,冯明驰. 基于视觉的采摘机器人目标识别与定位方法研究综述[J]. 仪器仪表学报, 2021,42(9): 28-51.
[79]雷欢,焦泽昱,马敬奇,等. 基于多特征融合与SVM的苹果品种快速识别算法[J]. 自动化与信息工程, 2020,41(4): 13-17.
[80]ALZUBI J, NAYYAR A, KUMAR A. Machine learning from theory to algorithms: an overview[C]. Bangalore, INDIA: NCCI, 2018.
[81]LUO L F, TANG Y C, LU Q H, et al. A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard[J]. Computers in Industry, 2018,99: 130-139.
[82]余凯,贾磊,陈雨强,等. 深度学习的昨天、今天和明天[J]. 计算机研究与发展, 2013,50(9): 1799-1804.
[83]GUO Y M, LIU Y, OERLEMANS A, et al. Deep learning for visual understanding: a review[J]. Neurocomputing, 2016,187: 27-48.
[84]李旭,李振海,杨海滨,等. 基于Faster R-CNN网络的茶叶嫩芽检测[J]. 农业机械学报, 2022,53(5): 217-214.
[85]ZHENG C, CHEN P F, PANG J, et al. A mango picking vision algorithm on instance segmentation and key point detection from RGB images in an open orchard[J]. Biosystems Engineering, 2021,206: 32-54.
[86]WANG P, NIU T, HE D J. Tomato young fruits detection method under near color background based on improved Faster R-CNN with attention mechanism[J]. Agriculture, 2021,11(11): 1-13.
[87]李章维,胡安顺,王晓飞. 基于视觉的目标检测方法综述[J]. 计算机工程与应用, 2020,56(8): 1-9.
[88]XU Z B, HUANG X P, HUANG Y, et al. A real-time Zanthoxylum target detection method for an intelligent picking robot under a complex background, based on an improved YOLOv5s architecture[J]. Sensors, 2022,22(2): 1-15.
[89]何斌,张亦博,龚健林,等. 基于改进YOLO v5的夜间温室番茄果实快速识别[J]. 农业机械学报, 2022,53(5): 201-208.
[90]杨福增,雷小燕,刘志杰,等. 基于CenterNet的密集场景下多苹果目标快速识别方法[J]. 农业机械学报, 2022,53(2): 265-273.
[91]MONTOYA-CAVERO L, TORRES R D D L, GMEZ-ESPINOSA A, et al. Vision systems for harvesting robots: produce detection and localization[J]. Computers and Electronics in Agriculture, 2022,192: 1-27.
[92]LI T, FENG Q C, QIU Q, et al. Occluded apple fruit detection and localization with a frustum-based point-cloud-processing approach for robotic harvesting[J]. Remote Sensing, 2022,14(3): 1-18.
[93]熊棣文,孔文斌,冯洋. 在树柑桔果实识别与定位技术发展现状及展望[J]. 中国南方果树, 2021,50(2): 185-190.
[94]BENAVIDES M, CANTN-GARBN M, SNCHEZ-MOLINA J A, et al. Automatic tomato and peduncle location system based on computer vision for use in robotized harvesting[J]. Applied Sciences, 2020,10(17): 1-21.
[95]LI J H, TANG Y C, ZOU X J, et al. Detection of fruit-bearing branches and localization of litchi clusters for vision-based harvesting robots[J]. IEEE Access, 2020,8: 117746-117758.
[96]罗陆锋,邹湘军,熊俊涛,等. 自然环境下葡萄采摘机器人采摘点的自动定位[J]. 农业工程学报, 2015,31(2): 14-21.
[97]任亚婧,张宁宁,徐媛媛,等. 基于视觉识别的成熟苹果识别及采摘定位系统[J]. 现代电子技术, 2021,44(11): 73-77.
[98]RONG J C, DAI G L, WANG P B. A peduncle detection method of tomato for autonomous harvesting[J]. Complex & Intelligent Systems, 2021,8(4): 2955-2969.
[99]LI Y T, HE L Y, JIA J M, et al. In-field tea shoot detection and 3D localization using an RGB-D camera[J]. Computers and Electronics in Agriculture, 2021,185: 1-12.
[100]王芳,崔丹丹,李林. 基于深度学习的采摘机器人目标识别定位算法[J]. 电子测量技术, 2021,44(20): 162-167.
[101]李瑞龙. 自动驾驶场景下的三维目标检测技术研究[D]. 长春:中国科学院大学(中国科学院长春光学精密机械与物理研究所), 2022.
[102]齐锐丽. 基于机器视觉花椒目标识别与定位技术研究[D]. 汉中:陕西理工大学, 2020.
[103]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, 2010,41(6): 158-162.
[104]胡小平,左富勇,谢珂. 微装配机器人手眼标定方法研究[J]. 仪器仪表学报, 2012,33(7): 1521-1526.
[105]KLAUS H S, GERD H. Optimal hand-eye calibration[C]. Beijing: IEEE, 2006.
[106]卜令昕. 结构化果园苹果收获机器人关键技术研究[D]. 杨凌:西北农林科技大学, 2021.
[107]LI D H, SUN X X, ELKHOUCHLAA H, et al. Fast detection and location of longan fruits using UAV images[J]. Computers and Electronics in Agriculture, 2021,190: 1-15.