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Active Perception Fruit Harvesting Robots — A Systematic Review

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Abstract

This paper studies the state-of-the-art of active perception solutions for manipulation in agriculture and suggests a possible architecture for an active perception system for harvesting in agriculture. Research and developing robots for agricultural context is a challenge, particularly for harvesting and pruning context applications. These applications normally consider mobile manipulators and their cognitive part has many challenges. Active perception systems look reasonable approach for fruit assessment robustly and economically. This systematic literature review focus in the topic of active perception for fruits harvesting robots. The search was performed in five different databases. The search resumed into 1034 publications from which only 195 publications where considered for inclusion in this review after analysis. We conclude that the most of researches are mainly about fruit detection and segmentation in two-dimensional space using evenly classic computer vision strategies and deep learning models. For harvesting, multiple viewpoint and visual servoing are the most commonly used strategies. The research of these last topics does not look robust yet, and require further analysis and improvements for better results on fruit harvesting.

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References

  1. Kitzes, J, et al: Shrink and share: humanity’s present and future ecological footprint. Philos. Trans. R. Soc. B: Biol. Sci. 363(1491), 467–475 (2007)

    Article  Google Scholar 

  2. Perry, M: Science and innovation strategic policy plans for the 2020s (eu, au, uk): Will they prepare us for the world in 2050?. Appl. Econ. Financ. 2(3), 76–84 (2015)

    Article  Google Scholar 

  3. Food and Ariculture Organization of the United States (2022) FAOSTAT Statistical Database. https://www.fao.org/faostat/en/, Last access on 25-01-2022

  4. Leshcheva, M, Ivolga, A: Human resources for agricultural organizations of agro-industrial region, areas for improvement. In: Subić, J., Kuzman, B., Vasile, A. J. (eds.) Thematic Proceedings, pp 386–400. Institute of Agricultural Economics, Belgrade (2018)

  5. Rica, R L V, et al.: Status of agriculture, forestry, fisheries and natural resources human resource in cebu and bohol, central philippines. J. Agric. Technol. Manag., 14–14 (2018)

  6. Schmitz, A, Moss, C B: Mechanized agriculture: machine adoption, farm size, and labor displacement. AgBioForum 18 (2015)

  7. McBratney, A, et al: Future directions of precision agriculture. Precis. Agric. 6(1), 7–23 (2005)

    Article  Google Scholar 

  8. euRobotics: Strategic research agenda for robotics in europe. http://ec.europa.eu/research/industrial_technologies/pdf/robotics-ppp-roadmap_en.pdf, Accessed: 2019-12-06 (2013)

  9. Roldán, J. J., et al.: Robots in agriculture: State of art and practical experiences. Service Robots (2018)

  10. dos Santos, F N, Sobreira, H, Campos, D, Morais, R, Moreira, A P, Contente, O: Towards a reliable robot for steep slope vineyards monitoring. J. Intell. Robot. Syst. 83(3-4), 429–444 (2016). https://doi.org/10.1007/s10846-016-0340-5

    Article  Google Scholar 

  11. Lehnert, C, McCool, C, Sa, I, Perez, T: Performance improvements of a sweet pepper harvesting robot in protected cropping environments. Journal of Field Robotics. https://doi.org/10.1002/rob.21973https://doi.org/ https://doi.org/10.1002/rob.2197310.1002/rob.21973 (2020)

  12. Xiong, Y, Ge, Y, From, P J: An obstacle separation method for robotic picking of fruits in clusters. Comput. Electron. Agric. 175, 105397 (2020). https://doi.org/10.1016/j.compag.2020.105397

    Article  Google Scholar 

  13. Martins, R C, Magalhāes, S, Jorge, P, Barroso, T, Santos, F: Metbots: Metabolomics Robots for Precision Viticulture. In: Progress in Artificial Intelligence, pp 156–166. Springer International Publishing (2019)

  14. Srinivasan, N, Prabhu, P, Smruthi, S S, Sivaraman, N V, Gladwin, S J, Rajavel, R, Natarajan, A R: Design of an autonomous seed planting robot. In: 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp 1–4 (2016)

  15. Terra, F, Rodrigues, L, Magalhães, S, Santos, F, Moura, P, Cunha, M: Pixelcroprobot, a cartesian multitask platform for microfarms automation. In: 2021 International Symposium of Asian Control Association on Intelligent Robotics and Industrial Automation (IRIA), pp 382–387 (2021)

  16. Vougioukas, S G: Agricultural robotics. Ann. Rev. Control Robot. Auton. Syst. 2(1), 365–392 (2019). https://doi.org/10.1146/annurev-control-053018-023617https://doi.org/10.1146/ https://doi.org/10.1146/annurev-control-053018-023617annurev-control-053018-023617

    Article  Google Scholar 

  17. Santos, L, et al: Path planning approach with the extraction of topological maps from occupancy grid maps in steep slope vineyards. In: 2019 IEEE International Conf. on Autonomous Robot Systems and Competitions (ICARSC), pp 1–7 (2019)

  18. Bertozzi, M, Broggi, A, Fascioli, A, Nichele, S: Stereo vision-based vehicle detection. In: Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511), pp 39–44 (2000)

  19. Bajcsy, R: Active Perception. Proc. IEEE 76(8), 966–1005 (1988). https://doi.org/10.1109/5.5968

    Article  Google Scholar 

  20. Bajcsy, R, Aloimonos, Y, Tsotsos, J K: Revisiting active perception. Auton. Robot. 42(2), 177–196 (2018). https://doi.org/10.1007/s10514-017-9615-3https://doi.org/10. https://doi.org/10.1007/s10514-017-9615-31007/s10514-017-9615-3, 1603.02729

    Article  Google Scholar 

  21. Hani, N, Isler, V: Visual servoing in orchard settings. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2016)

  22. Barth, R, Hemming, J, van Henten, E J: Design of an eye-in-hand sensing and servo control framework for harvesting robotics in dense vegetation. Biosyst. Eng. 146, 71–84 (2016). https://doi.org/10.1016/j.biosystemseng.2015.12.001

    Article  Google Scholar 

  23. Rehman, H U, Miura, J: Viewpoint planning for automated fruit harvesting using deep learning. In: 2021 IEEE/SICE International Symposium on System Integration (SII). IEEE (2021)

  24. Kurtser, P, Edan, Y: The use of dynamic sensing strategies to improve detection for a pepper harvesting robot. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2018)

  25. Kurtser, P, Edan, Y: Statistical models for fruit detectability: spatial and temporal analyses of sweet peppers. Biosyst. Eng. 171, 272–289 (2018). https://doi.org/10.1016/j.biosystemseng.2018.04.017https://doi.org/10.1016/j.biosystemseng. https://doi.org/10.1016/j.biosystemseng.2018.04.0172018.04.017

    Article  Google Scholar 

  26. Morrison, D, Corke, P, Leitner, J: Multi-view picking: Next-best-view reaching for improved grasping in clutter. In: 2019 International Conference on Robotics and Automation (ICRA), pp 8762–8768 (2019)

  27. Fu, X, Liu, Y, Wang, Z: Active Learning-Based Grasp for Accurate Industrial Manipulation. IEEE Trans. Autom. Sci. Eng. 16(4), 1610–1618 (2019). https://doi.org/10.1109/TASE.2019.2897791

    Article  Google Scholar 

  28. Kitaev, N, Mordatch, I, Patil, S, Abbeel, P: Physics-based trajectory optimization for grasping in cluttered environments. In: 2015 IEEE International Conference on Robotics and Automation (ICRA). http://ieeexplore.ieee.org/document/7139625/, pp 3102–3109. IEEE (2015)

  29. Sa, I, Lehnert, C, English, A, McCool, C, Dayoub, F, Upcroft, B, Perez, T: Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting-Combined Color and 3-D Information. IEEE Robot. Autom. Lett. 2(2), 765–772 (2017). https://doi.org/10.1109/LRA.2017.2651952https://doi. https://doi.org/10.1109/LRA.2017.2651952org/10.1109/LRA.2017.2651952, 1701.08608

    Article  Google Scholar 

  30. Lehnert, C, English, A, McCool, C, Tow, A W, Perez, T: Autonomous Sweet Pepper Harvesting for Protected Cropping Systems. IEEE Robot. Autom. Lett. 2(2), 872–879 (2017). https://doi.org/10.1109/LRA.2017.2655622, 1706.02023

    Article  Google Scholar 

  31. Soria, P R, Sukkar, F, Martens, W, Arrue, B C, Fitch, R: Multi-view probabilistic segmentation of pome fruit with a low-cost RGB-d camera. In: ROBOT 2017: Third iberian robotics conference, pp 320–331. Springer International Publishing (2017)

  32. Wendel, A, Underwood, J, Walsh, K: Maturity estimation of mangoes using hyperspectral imaging from a ground based mobile platform. Comput. Electron. Agric. 155, 298–313 (2018). https://doi.org/10.1016/j.compag.2018.10.021

    Article  Google Scholar 

  33. Zhao, M, Peng, Y, Li, L, Qiao, X: Detection and classification manipulator system for apple based on machine vision and optical technology. In: 2020 ASABE Annual International Virtual Meeting. American Society of Agricultural and Biological Engineers (2020)

  34. Gené-Mola, J., Gregorio, E, Guevara, J, Auat, F, Sanz-Cortiella, R, Escolà, A., Llorens, J, Morros, J.-R., Ruiz-Hidalgo, J, Vilaplana, V, Rosell-Polo, J R: Fruit detection in an apple orchard using a mobile terrestrial laser scanner. Biosyst. Eng. 187, 171–184 (2019). https://doi.org/10.1016/j.biosystemseng.2019.08.017

    Article  Google Scholar 

  35. Magalhães, S. A., Castro, L, Moreira, G, dos Santos, F N, Cunha, M, Dias, J, Moreira, A P: Evaluating the single-shot MultiBox detector and YOLO deep learning models for the detection of tomatoes in a greenhouse. Sensors 21(10), 3569 (2021). https://doi.org/10.3390/s21103569

    Article  Google Scholar 

  36. He, Y, Pan, F, Wang, B, Teng, Z, Wu, J: Transfer learning based fruits image segmentation for fruit-picking robots. In: 2020 IEEE 3rd International Conference on Computer and Communication Engineering Technology (CCET). IEEE (2020)

  37. Cecotti, H, Rivera, A, Farhadloo, M, Pedroza, M A: Grape detection with convolutional neural networks. Expert Syst. Appl. 159, 113588 (2020). https://doi.org/10.1016/j.eswa.2020.113588

    Article  Google Scholar 

  38. Jun, J, Kim, J, Seol, J, Kim, J, Son, H I: Towards an efficient tomato harvesting robot: 3d perception, manipulation, and end-effector. IEEE Access 9, 17631–17640 (2021). https://doi.org/10.1109/access.2021.3052240

    Article  Google Scholar 

  39. Barbole, D K, Jadhav, P M, Patil, S B: A review on fruit detection and segmentation techniques in agricultural field. In: Chen, J. I.-Z., Tavares, J. M. R. S., Iliyasu, A. M., Du, K.-L. (eds.) Second International Conference on Image Processing and Capsule Networks, pp 269–288. Springer International Publishing, Cham (2022)

  40. Fu, L, Gao, F, Wu, J, Li, R, Karkee, M, Zhang, Q: Application of consumer rgb-d cameras for fruit detection and localization in field: A critical review. Comput. Electron. Agric. 177, 105687 (2020). https://doi.org/10.1016/j.compag.2020.105687, https://www.sciencedirect.com/science/article/pii/S0168169920319530

    Article  Google Scholar 

  41. Naranjo-Torres, J, Mora, M, Hernández-García, R., Barrientos, R J, Fredes, C, Valenzuela, A: A review of convolutional neural network applied to fruit image processing, Appl. Sci. 10(10). https://doi.org/10.3390/app10103443, https://www.mdpi.com/2076-3417/10/10/3443 (2020)

  42. Wohlin, C, Runeson, P, Höst, M., Ohlsson, M C, Regnell, B, Wesslén, A.: Experimentation in software engineering. Springer, Berlin (2012)

  43. Page, M J, Moher, D, Bossuyt, P M, Boutron, I, Hoffmann, T C, Mulrow, C D, Shamseer, L, Tetzlaff, J M, Akl, E A, Brennan, S E, Chou, R, Glanville, J, Grimshaw, J M, Hróbjartsson, A., Lalu, M M, Li, T, Loder, E W, Mayo-Wilson, E, McDonald, S, McGuinness, L A, Stewart, L A, Thomas, J, Tricco, A C, Welch, V A, Whiting, P, McKenzie, J E: Prisma 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ 372. https://doi.org/10.1136/bmj.n160https://doi.org/10.1136/bmj. https://doi.org/10.1136/bmj.n160n160, https://www.bmj.com/content/372/bmj.n160 (2021)

  44. Freitas, V: Parsifal, Online, https://parsif.al/, Last accessed on 16 of December of 2021 (2021)

  45. ACM, I: ACM Digital Library, Online, http://portal.acm.org, Last Accessed on 16th of December of 2021 (2021)

  46. Elsevier: Engineering Village, Online, http://www.engineeringvillage.comhttp://www.engineering http://www.engineeringvillage.comvillage.com, Last accessed on 16th of December of 2021 (2021)

  47. IEEE: IEEE Xplore, Online, http://ieeexplore.ieee.org, Last accessed on 16 of December of 2021 (2021)

  48. Clarivate: Web of Science, https://www.webofscience.com/wos/woscc/basic-search, Last accessed on 20/10/2021 (2021)

  49. Elsevier B.V: Scopus, https://www.scopus.com/, Last accessed on 20/10/2021 (2021)

  50. Aloimonos, J, Weiss, I, Bandyopadhyay, A: Active vision. Int. J. Comput. Vis. 1(4), 333–356 (1988). https://doi.org/10.1007/BF00133571https://doi.org/10.1007/ https://doi.org/10.1007/BF00133571BF00133571

    Article  Google Scholar 

  51. Ballard, D H: Animate vision. Artif. Intell. 48(1), 57–86 (1991). https://doi.org/10.1016/0004-3702(91)90080-4

    Article  MathSciNet  Google Scholar 

  52. Rivlin, E., Rotstein, H.: Control of a Camera for Active Vision: Foveal Vision, Smooth Tracking and Saccade. Int. J. Comput. Vis. 39(2), 81–96 (2000). https://doi.org/10.1023/a:1008166825510

    Article  MATH  Google Scholar 

  53. Ognibene, D, Baldassare, G: Ecological Active Vision: Four Bioinspired Principles to Integrate Bottom–Up and Adaptive Top–Down Attention Tested With a Simple Camera-Arm Robot. IEEE Trans. Auton. Mental Dev. 7(1), 3–25 (2015). https://doi.org/10.1109/tamd.2014.2341351

    Article  Google Scholar 

  54. Chen, S, Li, Y, Kwok, N M: Active vision in robotic systems: A survey of recent developments. Int. J. Robot. Res. 30(11), 1343–1377 (2011). https://doi.org/10.1177/0278364911410755

    Article  Google Scholar 

  55. Gualtieri, M, Pas, A T, Saenko, K, Platt, R: High precision grasp pose detection in dense clutter. In: IEEE International Conference on Intelligent Robots and Systems, vol. 2016, pp 598–605. Institute of Electrical and Electronics Engineers Inc. (2016)

  56. Balkenius, C, Hulth, N: Attention as selection-for-action: A scheme for active perception. In: 1999 3rd European Workshop on Advanced Mobile Robots, Eurobot 1999 - Proceedings, pp 113–119. Institute of Electrical and Electronics Engineers Inc. (1999)

  57. Prescott, T J, Diamond, M E, Wing, A M: Active touch sensing. Philos. Trans. R. Soc. B: Biol. Sci. 366(1581), 2989–2995 (2011). https://doi.org/10.1098/rstb.2011.0167

    Article  Google Scholar 

  58. Mendes, J M, dos Santos, F N, Ferraz, N A, do Couto, P M, dos Santos, R M: Localization Based on Natural Features Detector for Steep Slope Vineyards. J. Intell. Robot. Syst. Theory Appl. 93(3-4), 433–446 (2018). https://doi.org/10.1007/s10846-017-0770-8

    Article  Google Scholar 

  59. Magalhães, S. A., dos Santos, F N, Martins, R C, Rocha, L F, Brito, J: Path Planning Algorithms Benchmarking for Grapevines Pruning and Monitoring. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11805 LNAI, pp 295–306. Springer (2019)

  60. Paulin, S, Botterill, T, Lin, J, Chen, X, Green, R: A comparison of sampling-based path planners for a grape vine pruning robot arm. In: ICARA 2015 - Proceedings of the 2015 6th International Conference on Automation, Robotics and Applications, pp 98–103. Institute of Electrical and Electronics Engineers Inc. (2015)

  61. Kurtser, P, Edan, Y: Planning the sequence of tasks for harvesting robots. Robot. Auton. Syst. 131, 103591 (2020). https://doi.org/10.1016/j.robot.2020.103591https://doi.org/ https://doi.org/10.1016/j.robot.2020.10359110.1016/j.robot.2020.103591

    Article  Google Scholar 

  62. Xu, W, Chen, H, Su, Q, Ji, C, Xu, W, Memon, M.-S., Zhou, J: Shadow detection and removal in apple image segmentation under natural light conditions using an ultrametric contour map. Biosyst. Eng. 184, 142–154 (2019). https://doi.org/10.1016/j.biosystemseng.2019.06.016https://doi.org/10.1016/j. https://doi.org/10.1016/j.biosystemseng.2019.06.016biosystemseng.2019.06.016

    Article  Google Scholar 

  63. Liu, H, Yu, Y, Sun, F, Gu, J: Visual–Tactile Fusion for Object Recognition. IEEE Trans. Autom. Sci. Eng. 14(2), 996–1008 (2017). https://doi.org/10.1109/TASE.2016.2549552, http://ieeexplore.ieee.org/document/7462208/

    Article  Google Scholar 

  64. Tejada, V F, Stoelen, M F, Kusnierek, K, Heiberg, N, Korsaeth, A: Proof-of-concept robot platform for exploring automated harvesting of sugar snap peas. Precis. Agric. 18(6), 952–972 (2017). https://doi.org/10.1007/s11119-017-9538-1

    Article  Google Scholar 

  65. Kaur, S, Randhawa, S, Malhi, A: An efficient ANFIS based pre-harvest ripeness estimation technique for fruits. Multimed. Tools Appl. 80(13), 19459–19489 (2021). https://doi.org/10.1007/s11042-021-10741-2https://doi.org/10.1007/ https://doi.org/10.1007/s11042-021-10741-2s11042-021-10741-2

    Article  Google Scholar 

  66. Li, W, Yuan, Y, Hu, S, Li, M, Feng, W, Zheng, J: Positioning of apple’s growth cycle based on pattern recognition. Mob. Inf. Syst. 2021, 1–11 (2021). https://doi.org/10.1155/2021/9687950

    Google Scholar 

  67. Perez-Borrero, I, Marin-Santos, D, Vasallo-Vazquez, M J, Gegundez-Arias, M E: A new deep-learning strawberry instance segmentation methodology based on a fully convolutional neural network. Neural Comput. Appl. 33(22), 15059–15071 (2021). https://doi.org/10.1007/s00521-021-06131-2

    Article  Google Scholar 

  68. Gai, R, Chen, N, Yuan, H: A detection algorithm for cherry fruits based on the improved YOLO-v4 model. Neural Comput. Appl. https://doi.org/10.1007/s00521-021-06029-z (2021)

  69. Alosaimi, W, Alyami, H, Uddin, M I: PeachNet: Peach diseases detection for automatic harvesting. Comput. Mater. Contin. 67(2), 1665–1677 (2021). https://doi.org/10.32604/cmc.2021.014950

    Google Scholar 

  70. Bhargava, A, Bansal, A: Classification and grading of multiple varieties of apple fruit. Food Anal. Methods 14(7), 1359–1368 (2021). https://doi.org/10.1007/s12161-021-01970-0

    Article  Google Scholar 

  71. Biffi, L J, Mitishita, E, Liesenberg, V, dos Santos, A A, Gonçalves, D. N., Estrabis, N V, de Andrade Silva, J, Osco, L P, Ramos, A P M, Centeno, J A S, Schimalski, M B, Rufato, L, Neto, S L R, Junior, J M, Gonçalves, W. N.: ATSS deep learning-based approach to detect apple fruits. Remote Sens. 13(1), 54 (2020). https://doi.org/10.3390/rs13010054

    Article  Google Scholar 

  72. Cai, J, Tao, J, Ma, Y, Fan, X, Cheng, L: Fruit image recognition and classification method based on improved single shot multi-box detector. J. Phys.: Conf. Ser. 1629(1), 012010 (2020). https://doi.org/10.1088/1742-6596/1629/1/012010

    Google Scholar 

  73. Mehta, S S, Rysz, M W, Ganesh, P, Burks, T F: Finite-time visual servo control for robotic fruit harvesting in the presence of fruit motion. In: 2020 ASABE Annual International Virtual Meeting. American Society of Agricultural and Biological Engineers (2020)

  74. Sepulveda, D, Fernandez, R, Navas, E, Armada, M, Gonzalez-De-Santos, P: Robotic aubergine harvesting using dual-arm manipulation. IEEE Access 8, 121889–121904 (2020). https://doi.org/10.1109/access.2020.3006919

    Article  Google Scholar 

  75. Liu, T.-H., Ehsani, R, Toudeshki, A, Zou, X.-J., Wang, H.-J.: Identifying immature and mature pomelo fruits in trees by elliptical model fitting in the cr-cb color space. Precis. Agric. 20(1), 138–156 (2018). https://doi.org/10.1007/s11119-018-9586-1

    Article  Google Scholar 

  76. Dai, N, Xie, H, Yang, X, Zhan, K, Liu, J: Recognition of cutting region for pomelo picking robot based on machine vision. In: 2019 Boston. American Society of Agricultural and Biological Engineers, Massachusetts (2019)

  77. Xie, H, Dai, N, Yang, X, Zhan, K, Liu, J: Research on recognition methods of pomelo fruit hanging on trees base on machine vision. In: 2019 Boston. American Society of Agricultural and Biological Engineers, Massachusetts (2019)

  78. Ji, W, Qian, Z, Xu, B, Tao, Y, Zhao, D, Ding, S: Apple tree branch segmentation from images with small gray-level difference for agricultural harvesting robot. Optik 127(23), 11173–11182 (2016). https://doi.org/10.1016/j.ijleo.2016.09.044

    Article  Google Scholar 

  79. Shen, T, Zhao, D, Jia, W, Chen, Y: Recognition and localization method of overlapping apples for apple harvesting robot. In: Computer and computing technologies in agriculture IX, pp 330–345. Springer International Publishing (2016)

  80. Mehta, SS, Burks, TF: Adaptive visual servo control of robotic harvesting systems. IFAC-PapersOnLine 49(16), 287–292 (2016). https://doi.org/10.1016/j.ifacol.2016.10.053

    Article  Google Scholar 

  81. Joey, A, Jane, Z, Bo, L: Automated pruning of greenhouse indeterminate tomato plants. In: Proceedings of the 2nd International Conference on Vision, Image and Signal Processing. ACM (2018)

  82. Liu, G, Mao, S, Jin, H, Kim, J H: A robust mature tomato detection in greenhouse scenes using machine learning and color analysis. In: Proceedings of the 2019 11th International Conference on Machine Learning and Computing - ICMLC '19. ACM Press (2019)

  83. Yang, R, Wu, M, Bao, Z, Zhang, P: Cherry recognition based on color channel transform. In: Proceedings of the 2019 International Conference on Artificial Intelligence and Computer Science. ACM (2019)

  84. Jie, Z, Jie, L, Kun, G, Zijie, N: Design of algorithm for apple rapid positioning based on YOLO target detection model. In: 2021 2nd International Conference on Artificial Intelligence and Information Systems. ACM (2021)

  85. Liang, Q, Long, J, Zhu, W, Wang, Y, Sun, W: Apple recognition based on convolutional neural network framework. In: 2018 13th World Congress on Intelligent Control and Automation (WCICA). IEEE (2018)

  86. Lamb, N, Chuah, M C: A strawberry detection system using convolutional neural networks. In: 2018 IEEE International Conference on Big Data (Big Data). IEEE (2018)

  87. Fu, L, Duan, J, Zou, X, Lin, J, Zhao, L, Li, J, Yang, Z: Fast and accurate detection of banana fruits in complex background orchards. IEEE Access 8, 196835–196846 (2020). https://doi.org/10.1109/access.2020.3029215

    Article  Google Scholar 

  88. Behera, S K, Mishra, N, Sethy, P K, Rath, A K: On-tree detection and counting of apple using color thresholding and CHT. In: 2018 International Conference on Communication and Signal Processing (ICCSP). IEEE (2018)

  89. Xu, Z.-F., Jia, R.-S., Liu, Y.-B., Zhao, C.-Y., Sun, H.-M.: Fast method of detecting tomatoes in a complex scene for picking robots. IEEE Access 8, 55289–55299 (2020). https://doi.org/10.1109/access.2020.2981823

    Article  Google Scholar 

  90. Longye, X, Zhuo, W, Haishen, L, Xilong, K, Changhui, Y: Overlapping citrus segmentation and reconstruction based on mask r-CNN model and concave region simplification and distance analysis. J. Phys.: Conf. Ser. 1345(3), 032064 (2019). https://doi.org/10.1088/1742-6596/1345/3/032064

    Google Scholar 

  91. Peng, H, Xue, C, Shao, Y, Chen, K, Xiong, J, Xie, Z, Zhang, L: Semantic segmentation of litchi branches using DeepLabV3+ model. IEEE Access 8, 164546–164555 (2020). https://doi.org/10.1109/access.2020.3021739

    Article  Google Scholar 

  92. Luo, L, Tang, Y, Zou, X, Wang, C, Zhang, P, Feng, W: Robust grape cluster detection in a vineyard by combining the AdaBoost framework and multiple color components. Sensors 16(12), 2098 (2016). https://doi.org/10.3390/s16122098

    Article  Google Scholar 

  93. Liang, Q, Zhu, W, Long, J, Wang, Y, Sun, W, Wu, W: A real-time detection framework for on-tree mango based on SSD network. In: Intelligent Robotics and Applications, pp 423–436. Springer International Publishing (2018)

  94. Wang, C, Luo, Q, Chen, X, Yi, B, Wang, H: Citrus recognition based on YOLOv4 neural network. J. Phys.: Conf. Ser. 1820(1), 012163 (2021). https://doi.org/10.1088/1742-6596/1820/1/012163https://doi.org/10.1088/1742-6596/1820/ https://doi.org/10.1088/1742-6596/1820/1/0121631/012163

    Google Scholar 

  95. He, Z.-L., Xiong, J.-T., Lin, R, Zou, X, Tang, L.-Y., Yang, Z.-G., Liu, Z, Song, G: A method of green litchi recognition in natural environment based on improved LDA classifier. Comput. Electron. Agric. 140, 159–167 (2017). https://doi.org/10.1016/j.compag.2017.05.029https://doi.org/10.1016/j. https://doi.org/10.1016/j.compag.2017.05.029compag.2017.05.029

    Article  Google Scholar 

  96. Xiong, J, Lin, R, Liu, Z, He, Z, Tang, L, Yang, Z, Zou, X: The recognition of litchi clusters and the calculation of picking point in a nocturnal natural environment. Biosyst. Eng. 166, 44–57 (2018). https://doi.org/10.1016/j.biosystemseng.2017.11.005

    Article  Google Scholar 

  97. Gu, D, Zhu, K, Shao, Y, Wu, W, Gong, L, Liu, C: 3d scanning and multiple point cloud registration with active view complementation for panoramically imaging large-scale plants. In: Intelligent Robotics and Applications, pp 329–341. Springer International Publishing (2019)

  98. Eizentals, P, Oka, K: 3d pose estimation of green pepper fruit for automated harvesting. Comput. Electron. Agric. 128, 127–140 (2016). https://doi.org/10.1016/j.compag.2016.08.024

    Article  Google Scholar 

  99. Sa, I, Ge, Z, Dayoub, F, Upcroft, B, Perez, T, McCool, C: DeepFruits: A fruit detection system using deep neural networks. Sensors 16(8), 1222 (2016). https://doi.org/10.3390/s16081222

    Article  Google Scholar 

  100. Liang, C, Xiong, J, Zheng, Z, Zhong, Z, Li, Z, Chen, S, Yang, Z: A visual detection method for nighttime litchi fruits and fruiting stems. Comput. Electron. Agric. 169, 105192 (2020). https://doi.org/10.1016/j.compag.2019.105192

    Article  Google Scholar 

  101. Kuznetsova, A, Maleva, T, Soloviev, V: Detecting apples in orchards using YOLOv3. In: Computational Science and Its Applications – ICCSA 2020, pp 923–934. Springer International Publishing (2020)

  102. Fu, L, Feng, Y, Majeed, Y, Zhang, X, Zhang, J, Karkee, M, Zhang, Q: Kiwifruit detection in field images using faster r-CNN with ZFNet. IFAC-PapersOnLine 51(17), 45–50 (2018). https://doi.org/10.1016/j.ifacol.2018.08.059

    Article  Google Scholar 

  103. Mehta, SS, MacKunis, W, Burks, TF: Robust visual servo control in the presence of fruit motion for robotic citrus harvesting. Comput. Electron. Agric. 123, 362–375 (2016). https://doi.org/10.1016/j.compag.2016.03.007https://doi.org/10. https://doi.org/10.1016/j.compag.2016.03.0071016/j.compag.2016.03.007

    Article  Google Scholar 

  104. Gongal, A, Karkee, M, Amatya, S: Apple fruit size estimation using a 3d machine vision system. Inf. Process. Agric. 5(4), 498–503 (2018). https://doi.org/10.1016/j.inpa.2018.06.002

    Google Scholar 

  105. Bargoti, S, Underwood, J: Deep fruit detection in orchards. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2017)

  106. Changhui, Y, Youcheng, H, Lin, H, Sa, L, Yanping, L: Overlapped fruit recognition for citrus harvesting robot in natural scenes. In: 2017 2nd International Conference on Robotics and Automation Engineering (ICRAE). IEEE (2017)

  107. Puttemans, S, Vanbrabant, Y, Tits, L, Goedeme, T: Automated visual fruit detection for harvest estimation and robotic harvesting. In: 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE (2016)

  108. Harel, B, van Essen, R, Parmet, Y, Edan, Y: Viewpoint analysis for maturity classification of sweet peppers. Sensors 20(13), 3783 (2020). https://doi.org/10.3390/s20133783

    Article  Google Scholar 

  109. Kuznetsova, A, Maleva, T, Soloviev, V: Using YOLOv3 algorithm with pre- and post-processing for apple detection in fruit-harvesting robot. Agronomy 10(7), 1016 (2020). https://doi.org/10.3390/agronomy10071016https://doi.org/ https://doi.org/10.3390/agronomy1007101610.3390/agronomy10071016

    Article  Google Scholar 

  110. Jia, W, Mou, S, Wang, J, Liu, X, Zheng, Y, Lian, J, Zhao, D: Fruit recognition based on pulse coupled neural network and genetic elman algorithm application in apple harvesting robot. Int. J. Adv. Robot. Syst. 17(1), 172988141989747 (2020). https://doi.org/10.1177/1729881419897473

    Article  Google Scholar 

  111. Silwal, A., Karkee, M., Zhang, Q.: A Hierarchical Approach to Apple Identification for Robotic Harvesting. Trans. ASABE 59(5), 1079–1086 (2016). https://doi.org/10.13031/trans.59.11619

    Article  Google Scholar 

  112. Yu, Y, Zhang, K, Liu, H, Yang, L, Zhang, D: Real-time visual localization of the picking points for a ridge-planting strawberry harvesting robot. IEEE Access 8, 116556–116568 (2020). https://doi.org/10.1109/access.2020.3003034

    Article  Google Scholar 

  113. Yu, Y, Zhang, K, Yang, L, Zhang, D: Fruit detection for strawberry harvesting robot in non-structural environment based on mask-RCNN. Comput. Electron. Agric. 163, 104846 (2019). https://doi.org/10.1016/j.compag.2019.06.001

    Article  Google Scholar 

  114. Malik, M H, Zhang, T, Li, H, Zhang, M, Shabbir, S, Saeed, A: Mature tomato fruit detection algorithm based on improved HSV and watershed algorithm. IFAC-PapersOnLine 51(17), 431–436 (2018). https://doi.org/10.1016/j.ifacol.2018.08.183

    Article  Google Scholar 

  115. Wang, C, Tang, Y, Zou, X, SiTu, W, Feng, W: A robust fruit image segmentation algorithm against varying illumination for vision system of fruit harvesting robot. Optik 131, 626–631 (2017). https://doi.org/10.1016/j.ijleo.2016.11.177

    Article  Google Scholar 

  116. Bresilla, K, Perulli, G D, Boini, A, Morandi, B, Grappadelli, L C, Manfrini, L: Single-shot convolution neural networks for real-time fruit detection within the tree. Front. Plant Sci. 10. https://doi.org/10.3389/fpls.2019.00611 (2019)

  117. Kirk, R, Cielniak, G, Mangan, M: L*a*b*fruits: A rapid and robust outdoor fruit detection system combining bio-inspired features with one-stage deep learning networks. Sensors 20(1), 275 (2020). https://doi.org/10.3390/s20010275

    Article  Google Scholar 

  118. Ogorodnikova, O M, Ali, W: Method of ripe tomato detecting for a harvesting robot. In: PHYSICS, TECHNOLOGIES AND INNOVATION (PTI-2019): Proceedings of the VI international young researchers’ conference. AIP Publishing (2019)

  119. Chen, C, Li, B, Liu, J, Bao, T, Ren, N: Monocular positioning of sweet peppers: An instance segmentation approach for harvest robots. Biosyst. Eng. 196, 15–28 (2020). https://doi.org/10.1016/j.biosystemseng.2020.05.005

    Article  Google Scholar 

  120. Ge, Y, Xiong, Y, Tenorio, G L, From, P J: Fruit localization and environment perception for strawberry harvesting robots. IEEE Access 7, 147642–147652 (2019). https://doi.org/10.1109/access.2019.2946369https://doi.org/10.1109/access. https://doi.org/10.1109/access.2019.29463692019.2946369

    Article  Google Scholar 

  121. Zhao, Y, Gong, L, Huang, Y, Liu, C: Robust tomato recognition for robotic harvesting using feature images fusion. Sensors 16(2), 173 (2016). https://doi.org/10.3390/s16020173

    Article  Google Scholar 

  122. TsoTsos, JK: A Framework for Visual Motion Understanding. Phd dissertation. University of Toronto (1980)

  123. Bradski, G: The OpenCV Library. Dr Dobb’s Journal of Software Tools (2000)

  124. Luo, L, Tang, Y, Lu, Q, Chen, X, Zhang, P, Zou, X: A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard. Comput. Industry 99, 130–139 https://doi.org/10.1016/j.compind.2018.03.017https://doi.org/10.1016/j. https://doi.org/10.1016/j.compind.2018.03.017compind.2018.03.017, https://linkinghub.elsevier.com/retrieve/pii/S0166361517305298 (2018)

  125. Calli, B, Caarls, W, Wisse, M, Jonker, PP: Active Vision via Extremum Seeking for Robots in Unstructured Environments: Applications in Object Recognition and Manipulation. IEEE Trans. Autom. Sci. Eng. 15(4), 1810–1822. https://doi.org/10.1109/TASE.2018.2807787https://doi.org/10. https://doi.org/10.1109/TASE.2018.28077871109/TASE.2018.2807787, https://ieeexplore.ieee.org/document/8310020/ (2018)

  126. Lehnert, C, Tsai, D, Eriksson, A, McCool, C: 3D Move to See: Multi-perspective visual servoing towards the next best view within unstructured and occluded environments. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 3890–3897. IEEE, https://doi.org/10.1109/IROS40897.2019.8967918https://doi.org/10. https://doi.org/10.1109/IROS40897.2019.89679181109/IROS40897.2019.8967918, 1809.07896, https://ieeexplore.ieee.org/document/8967918/, 1809.07896 (2019)

  127. Waibel, M, Beetz, M, Civera, J, D’Andrea, R, Elfring, J, Gálvez-López, D., Häussermann, K., Janssen, R, Montiel, J, Perzylo, A, Schießle, B, Tenorth, M, Zweigle, O, De Molengraft, R.: RoboEarth. IEEE Robot. Autom. Mag. 18(2), 69–82. https://doi.org/10.1109/MRA.2011.941632, http://ieeexplore.ieee.org/document/5876227/http://ieeexplore.ieee. http://ieeexplore.ieee.org/document/5876227/org/document/5876227/ (2011)

  128. Mohanarajah, G, Hunziker, D, D’Andrea, R, Waibel, M: Rapyuta: A Cloud Robotics Platform. IEEE Trans. Autom. Sci. Eng. 12(2), 481–493. https://doi.org/10.1109/TASE.2014.2329556https://doi.org/10.1109/TASE.2014. https://doi.org/10.1109/TASE.2014.23295562329556, http://ieeexplore.ieee.org/document/6853392/ (2015)

  129. von Wichert, G, Klimowicz, C, Neubauer, W, Wosch, T, Lawitzky, G, Caspari, R, Heger, HJ, Witschel, P: The robotic bar - an integrated demonstration of man-robot interaction in a service scenario. In: Proceedings. 11th IEEE International Workshop on Robot and Human Interactive Communication, pp. 374–379. IEEE, https://doi.org/10.1109/ROMAN.2002.1045651, http://ieeexplore.ieee.org/document/1045651/ (2002)

  130. Jiao, Y, Luo, R, Li, Q, Deng, X, Yin, X, Ruan, C, Jia, W: Detection and localization of overlapped fruits application in an apple harvesting robot. Electronics 9(6), 1023 (2020). https://doi.org/10.3390/electronics9061023

    Article  Google Scholar 

  131. Ostovar, A, Ringdahl, O, Hellström, T.: Adaptive image thresholding of yellow peppers for a harvesting robot. Robotics 7(1), 11 (2018). https://doi.org/10.3390/robotics7010011

    Article  Google Scholar 

  132. Stein, M, Bargoti, S, Underwood, J: Image based mango fruit detection, localisation and yield estimation using multiple view geometry. Sensors 16(11), 1915 (2016). https://doi.org/10.3390/s16111915https://doi.org/10.3390/ https://doi.org/10.3390/s16111915s16111915

    Article  Google Scholar 

  133. Jia, W, Tian, Y, Luo, R, Zhang, Z, Lian, J, Zheng, Y: Detection and segmentation of overlapped fruits based on optimized mask r-CNN application in apple harvesting robot. Comput. Electron. Agric. 172, 105380 (2020). https://doi.org/10.1016/j.compag.2020.105380https://doi.org/10.1016/j. https://doi.org/10.1016/j.compag.2020.105380compag.2020.105380

    Article  Google Scholar 

  134. Kang, H, Chen, C: Fast implementation of real-time fruit detection in apple orchards using deep learning. Comput. Electron. Agric. 168, 105108 (2020). https://doi.org/10.1016/j.compag.2019.105108https://doi.org/10.1016/j.compag. https://doi.org/10.1016/j.compag.2019.1051082019.105108

    Article  Google Scholar 

  135. Wan, S, Goudos, S: Faster r-CNN for multi-class fruit detection using a robotic vision system. Comput. Netw. 168, 107036 (2020). https://doi.org/10.1016/j.comnet.2019.107036

    Article  Google Scholar 

  136. Benavides, M, Cantón-Garbín, M., Sánchez-Molina, J. A., Rodríguez, F.: Automatic tomato and peduncle location system based on computer vision for use in robotized harvesting. Appl. Sci. 10(17), 5887 (2020). https://doi.org/10.3390/app10175887

    Article  Google Scholar 

  137. Mao, S, Li, Y, Ma, Y, Zhang, B, Zhou, J, Wang, K: Automatic cucumber recognition algorithm for harvesting robots in the natural environment using deep learning and multi-feature fusion. Comput. Electron. Agric. 170, 105254 (2020). https://doi.org/10.1016/j.compag.2020.105254

    Article  Google Scholar 

  138. Lv, J, Wang, Y, Xu, L, Gu, Y, Zou, L, Yang, B, Ma, Z: A method to obtain the near-large fruit from apple image in orchard for single-arm apple harvesting robot. Sci. Hortic. 257, 108758 (2019). https://doi.org/10.1016/j.scienta.2019.108758

    Article  Google Scholar 

  139. Lin, G, Tang, Y, Zou, X, Cheng, J, Xiong, J: Fruit detection in natural environment using partial shape matching and probabilistic hough transform. Precis. Agric. 21(1), 160–177 (2019). https://doi.org/10.1007/s11119-019-09662-w

    Article  Google Scholar 

  140. Jidong, L, De-An, Z, Wei, J, Shihong, D: Recognition of apple fruit in natural environment. Optik 127(3), 1354–1362 (2016). https://doi.org/10.1016/j.ijleo.2015.10.177

    Article  Google Scholar 

  141. Lee, B, Kam, D, Min, B, Hwa, J, Oh, S: A vision servo system for automated harvest of sweet pepper in korean greenhouse environment. Appl. Sci. 9(12), 2395 (2019). https://doi.org/10.3390/app9122395

    Article  Google Scholar 

  142. Zhang, L, Gui, G, Khattak, A M, Wang, M, Gao, W, Jia, J: Multi-task cascaded convolutional networks based intelligent fruit detection for designing automated robot. IEEE Access 7, 56028–56038 (2019). https://doi.org/10.1109/access.2019.2899940

    Article  Google Scholar 

  143. Song, Z, Zhou, Z, Wang, W, Gao, F, Fu, L, Li, R, Cui, Y: Canopy segmentation and wire reconstruction for kiwifruit robotic harvesting. Comput. Electron. Agric. 181, 105933 (2021). https://doi.org/10.1016/j.compag.2020.105933

    Article  Google Scholar 

  144. Arad, B, Kurtser, P, Barnea, E, Harel, B, Edan, Y, Ben-Shahar, O: Controlled lighting and illumination-independent target detection for real-time cost-efficient applications. the case study of sweet pepper robotic harvesting. Sensors 19(6), 1390 (2019). https://doi.org/10.3390/s19061390

    Article  Google Scholar 

  145. Zhuang, J, Hou, C, Tang, Y, He, Y, Guo, Q, Zhong, Z, Luo, S: Computer vision-based localisation of picking points for automatic litchi harvesting applications towards natural scenarios. Biosyst. Eng. 187, 1–20 (2019). https://doi.org/10.1016/j.biosystemseng.2019.08.016https://doi.org/10.1016/j. https://doi.org/10.1016/j.biosystemseng.2019.08.016biosystemseng.2019.08.016

    Article  Google Scholar 

  146. Preter, A D, Anthonis, J, Baerdemaeker, J D: Development of a robot for harvesting strawberries. IFAC-PapersOnLine 51(17), 14–19 (2018). https://doi.org/10.1016/j.ifacol.2018.08.054

    Article  Google Scholar 

  147. Ozturk, B, Kirci, M, Gunes, E O: Detection of green and orange color fruits in outdoor conditions for robotic applications. In: 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE (2016)

  148. Zemmour, E, Kurtser, P, Edan, Y: Automatic parameter tuning for adaptive thresholding in fruit detection. Sensors 19(9), 2130 (2019). https://doi.org/10.3390/s19092130

    Article  Google Scholar 

  149. Lv, J, Shen, G, Ma, Z: Acquisition of fruit region in green apple image based on the combination of segmented regions. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC). IEEE (2017)

  150. Chanzhui, Y, Yi, W, Yanning, L, Lin, H: Reconstruction method of overlapped citrus fruits in natural scenes based on convex hull. In: 2017 International Conference on Computer Systems, Electronics and Control (ICCSEC). IEEE (2017)

  151. Jana, S, Basak, S, Parekh, R: Automatic fruit recognition from natural images using color and texture features. In: 2017 Devices for Integrated Circuit (DevIC). IEEE (2017)

  152. Li, Q, Jia, W, Sun, M, Hou, S, Zheng, Y: A novel green apple segmentation algorithm based on ensemble u-net under complex orchard environment. Comput. Electron. Agric. 180, 105900 (2021). https://doi.org/10.1016/j.compag.2020.105900

    Article  Google Scholar 

  153. Zhang, C, Zou, K, Pan, Y: A method of apple image segmentation based on color-texture fusion feature and machine learning. Agronomy 10(7), 972 (2020). https://doi.org/10.3390/agronomy10070972https://doi.org/10.3390/ https://doi.org/10.3390/agronomy10070972agronomy10070972

    Article  Google Scholar 

  154. Liu, X, Chen, S W, Liu, C, Shivakumar, S S, Das, J, Taylor, C J, Underwood, J, Kumar, V: Monocular camera based fruit counting and mapping with semantic data association. IEEE Robot. Autom. Lett. 4 (3), 2296–2303 (2019). https://doi.org/10.1109/lra.2019.2901987https://doi.org/10. https://doi.org/10.1109/lra.2019.29019871109/lra.2019.2901987

    Article  Google Scholar 

  155. Sabzi, S, Pourdarbani, R, Kalantari, D, Panagopoulos, T: Designing a fruit identification algorithm in orchard conditions to develop robots using video processing and majority voting based on hybrid artificial neural network. Appl. Sci. 10(1), 383 (2020). https://doi.org/10.3390/app10010383

    Article  Google Scholar 

  156. Badeka, E, Kalabokas, T, Tziridis, K, Nicolaou, A, Vrochidou, E, Mavridou, E, Papakostas, G A, Pachidis, T: Grapes visual segmentation for harvesting robots using local texture descriptors. In: Lecture Notes in Computer Science, pp 98–109. Springer International Publishing (2019)

  157. Chu, P, Li, Z, Lammers, K, Lu, R, Liu, X: Deep learning-based apple detection using a suppression mask r-CNN. Pattern Recogn. Lett. 147, 206–211 (2021). https://doi.org/10.1016/j.patrec.2021.04.022https://doi.org/10.1016/j. https://doi.org/10.1016/j.patrec.2021.04.022patrec.2021.04.022

    Article  Google Scholar 

  158. Xue, X, Guomin, Z, Yun, Q, Zhuang, L, Jian, W, Lin, H, Jingchao, F, Xiuming, G: Detection of young green apples in orchard environment using adaptive ratio chromatic aberration and HOG-SVM. In: Computer and computing technologies in agriculture XI, pp 253–268. Springer International Publishing (2019)

  159. Davidson, J R, Silwal, A, Hohimer, C J, Karkee, M, Mo, C, Zhang, Q: Proof-of-concept of a robotic apple harvester. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2016)

  160. Ghiani, L, Sassu, A, Palumbo, F, Mercenaro, L, Gambella, F: In-field automatic detection of grape bunches under a totally uncontrolled environment. Sensors 21(11), 3908 (2021). https://doi.org/10.3390/s21113908https:// https://doi.org/10.3390/s21113908doi.org/10.3390/s21113908

    Article  Google Scholar 

  161. Lee, J, Nazki, H, Baek, J, Hong, Y, Lee, M: Artificial intelligence approach for tomato detection and mass estimation in precision agriculture. Sustainability 12(21), 9138 (2020). https://doi.org/10.3390/su12219138

    Article  Google Scholar 

  162. Mu, Y, Chen, T.-S., Ninomiya, S, Guo, W: Intact detection of highly occluded immature tomatoes on plants using deep learning techniques. Sensors 20(10), 2984 (2020). https://doi.org/10.3390/s20102984

    Article  Google Scholar 

  163. Ilyas, T, Umraiz, M, Khan, A, Kim, H: DAM: Hierarchical adaptive feature selection using convolution encoder decoder network for strawberry segmentation. Front. Plant Sci 12. https://doi.org/10.3389/fpls.2021.591333(2021)

  164. Liu, X, Zhao, D, Jia, W, Ruan, C, Tang, S, Shen, T: A method of segmenting apples at night based on color and position information. Comput. Electron. Agric. 122, 118–123 (2016). https://doi.org/10.1016/j.compag.2016.01.023

    Article  Google Scholar 

  165. Li, D, Zhao, H, Zhao, X, Gao, Q, Xu, L: Cucumber detection based on texture and color in greenhouse. Int. J. Pattern Recognit. Artif. Intell. 31(08), 1754016 (2017). https://doi.org/10.1142/s0218001417540167https://doi.org/10.1142/ https://doi.org/10.1142/s0218001417540167s0218001417540167

    Article  Google Scholar 

  166. Hu, C, Liu, X, Pan, Z, Li, P: Automatic detection of single ripe tomato on plant combining faster r-CNN and intuitionistic fuzzy set. IEEE Access 7, 154683–154696 (2019). https://doi.org/10.1109/access.2019.2949343

    Article  Google Scholar 

  167. Lawal, M O: Tomato detection based on modified YOLOv3 framework. Sci. Rep. 11(1). https://doi.org/10.1038/s41598-021-81216-5https://doi.org/10.1038/s41598-021- https://doi.org/10.1038/s41598-021-81216-581216-5 (2021)

  168. Xue, X, Guomin, Z, Yun, Q, Zhuang, L, Jian, W, Lin, H, Jingchao, F, Xiuming, G: Detection of overlapped apples in orchard scene using improved k-means and distance least square. In: Computer and computing technologies in agriculture XI, pp 269–284. Springer International Publishing (2019)

  169. Fan, P, Lang, G, Yan, B, Lei, X, Guo, P, Liu, Z, Yang, F: A method of segmenting apples based on gray-centered RGB color space. Remote Sens. 13(6), 1211 (2021). https://doi.org/10.3390/rs13061211https://doi.org/10.3390/ https://doi.org/10.3390/rs13061211rs13061211

    Article  Google Scholar 

  170. Pérez-Borrero, I., Marín-Santos, D., Gegúndez-Arias, M. E., Cortés-Ancos, E.: A fast and accurate deep learning method for strawberry instance segmentation. Comput. Electron. Agric. 178, 105736 (2020). https://doi.org/10.1016/j.compag.2020.105736

    Article  Google Scholar 

  171. Suo, R, Gao, F, Zhou, Z, Fu, L, Song, Z, Dhupia, J, Li, R, Cui, Y: Improved multi-classes kiwifruit detection in orchard to avoid collisions during robotic picking. Comput. Electron. Agric. 182, 106052 (2021). https://doi.org/10.1016/j.compag.2021.106052https://doi.org/10.1016/j.compag.2021. https://doi.org/10.1016/j.compag.2021.106052106052

    Article  Google Scholar 

  172. Ji, W, Chen, G, Xu, B, Meng, X, Zhao, D: Recognition method of green pepper in greenhouse based on least-squares support vector machine optimized by the improved particle swarm optimization. IEEE Access 7, 119742–119754 (2019). https://doi.org/10.1109/access.2019.2937326

    Article  Google Scholar 

  173. Fan, P, Lang, G, Guo, P, Liu, Z, Yang, F, Yan, B, Lei, X: Multi-feature patch-based segmentation technique in the gray-centered RGB color space for improved apple target recognition. Agriculture 11(3), 273 (2021). https://doi.org/10.3390/agriculture11030273https://doi.org/10.3390/ https://doi.org/10.3390/agriculture11030273agriculture11030273

    Article  Google Scholar 

  174. Li, D, Shen, M, Li, D, Yu, X: Green apple recognition method based on the combination of texture and shape features. In: 2017 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE (2017)

  175. Wang, H, Dong, L, Zhou, H, Luo, L, Lin, G, Wu, J, Tang, Y: YOLOv3-litchi detection method of densely distributed litchi in large vision scenes. Math. Probl. Eng. 2021, 1–11 (2021). https://doi.org/10.1155/2021/8883015

    Article  Google Scholar 

  176. Yang, Q, Chen, Y, Xun, Y, Bao, G: Superpixel-based segmentation algorithm for mature citrus. Int. J. Agric. Biol. Eng. 13(4), 166–171 (2020). https://doi.org/10.25165/j.ijabe.20201304.5607

    Google Scholar 

  177. Gonzalez, S, Arellano, C, Tapia, J E: Deepblueberry: Quantification of blueberries in the wild using instance segmentation. IEEE Access 7, 105776–105788 (2019). https://doi.org/10.1109/access.2019.2933062https://doi.org/10.1109/ https://doi.org/10.1109/access.2019.2933062access.2019.2933062

    Article  Google Scholar 

  178. Lv, J, Ni, H, Wang, Q, Yang, B, Xu, L: A segmentation method of red apple image. Sci. Hortic. 256, 108615 (2019). https://doi.org/10.1016/j.scienta.2019.108615

    Article  Google Scholar 

  179. He, Z, Xiong, J, Chen, S, Li, Z, Chen, S, Zhong, Z, Yang, Z: A method of green citrus detection based on a deep bounding box regression forest. Biosyst. Eng. 193, 206–215 (2020). https://doi.org/10.1016/j.biosystemseng.2020.03.001

    Article  Google Scholar 

  180. Titus, A B, Narayanan, T, Das, G P: Vision system for coconut farm cable robot. In: 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM). IEEE (2017)

  181. Ji, Y, Zhao, Q, Bi, S, Shen, T: Apple grading method based on features of color and defect. In: 2018 37th Chinese Control Conference (CCC). IEEE (2018)

  182. Adão, T., Pádua, L., Pinho, T. M., Hruška, J., Sousa, A, Sousa, J J, Morais, R, Peres, E: Multi-purpose chestnut clusters detection using deep learning: a preliminary approach. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. XLII-3/W8, 1–7 (2019). https://doi.org/10.5194/isprs-archives-xlii-3-w8-1-2019

    Article  Google Scholar 

  183. Vitzrabin, E, Edan, Y: Adaptive thresholding with fusion using a RGBD sensor for red sweet-pepper detection. Biosyst. Eng. 146, 45–56 (2016). https://doi.org/10.1016/j.biosystemseng.2015.12.002https://doi.org/10.1016/j.biosystemseng.2015. https://doi.org/10.1016/j.biosystemseng.2015.12.00212.002

    Article  Google Scholar 

  184. Xu, Z.-F., Jia, R.-S., Sun, H.-M., Liu, Q.-M., Cui, Z: Light-YOLOv3: fast method for detecting green mangoes in complex scenes using picking robots. Appl. Intell. 50(12), 4670–4687 (2020). https://doi.org/10.1007/s10489-020-01818-w

    Article  Google Scholar 

  185. Mehta, SS, Ton, C, Rysz, M, Ganesh, P, Kan, Z, Burks, TF: On achieving bounded harvest times in robotic fruit harvesting: A finite-time visual servo control approach. IFAC-PapersOnLine 52(30), 114–119 (2019). https://doi.org/10.1016/j.ifacol.2019.12.507https://doi.org/10.1016/j.ifacol.2019.12. https://doi.org/10.1016/j.ifacol.2019.12.507507

    Article  MathSciNet  Google Scholar 

  186. Lawal, O M: YOLOMuskmelon: Quest for fruit detection speed and accuracy using deep learning. IEEE Access 9, 15221–15227 (2021). https://doi.org/10.1109/access.2021.3053167

    Article  Google Scholar 

  187. Aguiar, A S, Magalhães, S A, dos Santos, F N, Castro, L, Pinho, T, Valente, J, Martins, R, Boaventura-Cunha, J: Grape bunch detection at different growth stages using deep learning quantized models. Agronomy 11(9). https://doi.org/10.3390/agronomy11091890https://doi.org/10.3390/ https://doi.org/10.3390/agronomy11091890agronomy11091890, https://www.mdpi.com/2073-4395/11/9/1890 (2021)

  188. Fu, L, Majeed, Y, Zhang, X, Karkee, M, Zhang, Q: Faster r-CNN-based apple detection in dense-foliage fruiting-wall trees using RGB and depth features for robotic harvesting. Biosyst. Eng. 197, 245–256 (2020). https://doi.org/10.1016/j.biosystemseng.2020.07.007https://doi.org/10.1016/j.biosystemseng. https://doi.org/10.1016/j.biosystemseng.2020.07.0072020.07.007

    Article  Google Scholar 

  189. Lin, G, Tang, Y, Zou, X, Xiong, J, Fang, Y: Color-, depth-, and shape-based 3d fruit detection. Precis. Agric. 21(1), 1–17 (2019). https://doi.org/10.1007/s11119-019-09654-w

    Article  Google Scholar 

  190. Chen, W, Lu, S, Liu, B, Li, G, Qian, T: Detecting citrus in orchard environment by using improved YOLOv4. Sci. Program. 2020, 1–13 (2020). https://doi.org/10.1155/2020/8859237

    Google Scholar 

  191. Yoshida, T, Fukao, T, Hasegawa, T, and: Cutting point detection using a robot with point clouds for tomato harvesting. J. Robot. Mechatron. 32(2), 437–444 (2020). https://doi.org/10.20965/jrm.2020.p0437https://doi.org/10. https://doi.org/10.20965/jrm.2020.p043720965/jrm.2020.p0437

    Article  Google Scholar 

  192. Yoshida, T, Fukao, T, Hasegawa, T: A tomato recognition method for harvesting with robots using point clouds. In: 2019 IEEE/SICE International Symposium on System Integration (SII). IEEE (2019)

  193. Sarabu, H, Ahlin, K, Hu, A-P: Leveraging deep learning and RGB-d cameras for cooperative apple-picking robot arms. In: 2019 Boston. American Society of Agricultural and Biological Engineers, Massachusetts (2019)

  194. Yoshida, T, Fukao, T, Hasegawa, T, and: Fast detection of tomato peduncle using point cloud with a harvesting robot. J. Robot. Mechatron. 30(2), 180–186 (2018). https://doi.org/10.20965/jrm.2018.p0180https://doi.org/10. https://doi.org/10.20965/jrm.2018.p018020965/jrm.2018.p0180

    Article  Google Scholar 

  195. Tao, Y, Zhou, J: An automatic segmentation and recognition method of apple tree point clouds in the real scene based on the fusion of color and 3d feature. In: 2017 Spokane. American Society of Agricultural and Biological Engineers, Washington (2017)

  196. Tian, Y, Duan, H, Luo, R, Zhang, Y, Jia, W, Lian, J, Zheng, Y, Ruan, C, Li, C: Fast recognition and location of target fruit based on depth information. IEEE Access 7, 170553–170563 (2019). https://doi.org/10.1109/access.2019.2955566

    Article  Google Scholar 

  197. Nguyen, T T, Vandevoorde, K, Wouters, N, Kayacan, E, Baerdemaeker, J G D, Saeys, W: Detection of red and bicoloured apples on tree with an RGB-d camera. Biosyst. Eng. 146, 33–44 (2016). https://doi.org/10.1016/j.biosystemseng.2016.01.007

    Article  Google Scholar 

  198. Silwal, A, Davidson, J R, Karkee, M, Mo, C, Zhang, Q, Lewis, K: Design, integration, and field evaluation of a robotic apple harvester. J. Field Robot. 34(6), 1140–1159 (2017). https://doi.org/10.1002/rob.21715

    Article  Google Scholar 

  199. Xiaomei, H, Bowen, N, Jianfei, C: Research on the location of citrus picking point based on structured light camera. In: 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC). IEEE (2019)

  200. Quan, Q, Lanlan, T, Xiaojun, Q, Kai, J, Qingchun, F: Selecting candidate regions of clustered tomato fruits under complex greenhouse scenes using RGB-d data. In: 2017 3rd International Conference on Control, Automation and Robotics (ICCAR). IEEE (2017)

  201. Anh, N P T, Hoang, S, Tai, D V, Quoc, B L C: Developing robotic system for harvesting pineapples. In: 2020 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE (2020)

  202. Arad, B, Balendonck, J, Barth, R, Ben-Shahar, O, Edan, Y, Hellström, T., Hemming, J, Kurtser, P, Ringdahl, O, Tielen, T, Tuijl, B: Development of a sweet pepper harvesting robot. J. Field Robot. 37(6), 1027–1039 (2020). https://doi.org/10.1002/rob.21937https://doi.org/10.1002/ https://doi.org/10.1002/rob.21937rob.21937

    Article  Google Scholar 

  203. Kang, H, Zhou, H, Chen, C: Visual perception and modeling for autonomous apple harvesting. IEEE Access 8, 62151–62163 (2020). https://doi.org/10.1109/access.2020.2984556

    Article  Google Scholar 

  204. Sarabu, H, Ahlin, K, Hu, A-P: Graph-based cooperative robot path planning in agricultural environments. In: 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE (2019)

  205. Fujinaga, T, Yasukawa, S, Ishii, K: Evaluation of tomato fruit harvestability for robotic harvesting. In: 2021 IEEE/SICE International Symposium on System Integration (SII). IEEE (2021)

  206. Tu, S, Pang, J, Liu, H, Zhuang, N, Chen, Y, Zheng, C, Wan, H, Xue, Y: Passion fruit detection and counting based on multiple scale faster r-CNN using RGB-d images. Precis. Agric. 21 (5), 1072–1091 (2020). https://doi.org/10.1007/s11119-020-09709-3

    Article  Google Scholar 

  207. Li, J, Tang, Y, Zou, X, Lin, G, Wang, H: Detection of fruit-bearing branches and localization of litchi clusters for vision-based harvesting robots. IEEE Access 8, 117746–117758 (2020). https://doi.org/10.1109/access.2020.3005386

    Article  Google Scholar 

  208. Kang, H, Zhou, H, Wang, X, Chen, C: Real-time fruit recognition and grasping estimation for robotic apple harvesting. Sensors 20(19), 5670 (2020). https://doi.org/10.3390/s20195670

    Article  Google Scholar 

  209. Barnea, E, Mairon, R, Ben-Shahar, O: Colour-agnostic shape-based 3d fruit detection for crop harvesting robots. Biosyst. Eng. 146, 57–70 (2016). https://doi.org/10.1016/j.biosystemseng.2016.01.013https://doi.org/10.1016/j.biosystemseng.2016. https://doi.org/10.1016/j.biosystemseng.2016.01.01301.013

    Article  Google Scholar 

  210. Kang, C.: Fruit detection and segmentation for apple harvesting using visual sensor in orchards. Sensors 19(20), 4599 (2019). https://doi.org/10.3390/s19204599

    Article  Google Scholar 

  211. Ge, Y, Xiong, Y, From, P J: Instance segmentation and localization of strawberries in farm conditions for automatic fruit harvesting. IFAC-PapersOnLine 52(30), 294–299 (2019). https://doi.org/10.1016/j.ifacol.2019.12.537

    Article  Google Scholar 

  212. Lin, G, Tang, Y, Zou, X, Wang, C: Three-dimensional reconstruction of guava fruits and branches using instance segmentation and geometry analysis. Comput. Electron. Agric. 184, 106107 (2021). https://doi.org/10.1016/j.compag.2021.106107

    Article  Google Scholar 

  213. Kang, H, Chen, C: Fruit detection, segmentation and 3d visualisation of environments in apple orchards. Comput. Electron. Agric. 171, 105302 (2020). https://doi.org/10.1016/j.compag.2020.105302https://doi.org/10.1016/j. https://doi.org/10.1016/j.compag.2020.105302compag.2020.105302

    Article  Google Scholar 

  214. Wu, G, Li, B, Zhu, Q, Huang, M, Guo, Y: Using color and 3d geometry features to segment fruit point cloud and improve fruit recognition accuracy. Comput. Electron. Agric. 174, 105475 (2020). https://doi.org/10.1016/j.compag.2020.105475

    Article  Google Scholar 

  215. Yang, CH, Xiong, LY, Wang, Z, Wang, Y, Shi, G, Kuremot, T, Zhao, WH, Yang, Y: Integrated detection of citrus fruits and branches using a convolutional neural network. Comput. Electron. Agric. 174, 105469 (2020). https://doi.org/10.1016/j.compag.2020.105469https://doi.org/10.1016/j. https://doi.org/10.1016/j.compag.2020.105469compag.2020.105469

    Article  Google Scholar 

  216. Lin, G, Zhu, L, Li, J, Zou, X, Tang, Y: Collision-free path planning for a guava-harvesting robot based on recurrent deep reinforcement learning. Comput. Electron. Agric. 188, 106350 (2021). https://doi.org/10.1016/j.compag.2021.106350

    Article  Google Scholar 

  217. Wu, G, Zhu, Q, Huang, M, Guo, Y, Qin, J: Automatic recognition of juicy peaches on trees based on 3d contour features and colour data. Biosyst. Eng. 188, 1–13 (2019). https://doi.org/10.1016/j.biosystemseng.2019.10.002https://doi.org/ https://doi.org/10.1016/j.biosystemseng.2019.10.00210.1016/j.biosystemseng.2019.10.002

    Article  Google Scholar 

  218. Barth, R, Hemming, J, Henten, E J V: Angle estimation between plant parts for grasp optimisation in harvest robots. Biosyst. Eng. 183, 26–46 (2019). https://doi.org/10.1016/j.biosystemseng.2019.04.006https://doi.org/10.1016/j. https://doi.org/10.1016/j.biosystemseng.2019.04.006biosystemseng.2019.04.006

    Article  Google Scholar 

  219. Xiong, Y, Peng, C, Grimstad, L, From, P J, Isler, V: Development and field evaluation of a strawberry harvesting robot with a cable-driven gripper. Comput. Electron. Agric. 157, 392–402 (2019). https://doi.org/10.1016/j.compag.2019.01.009

    Article  Google Scholar 

  220. Vitzrabin, E, Edan, Y: Changing task objectives for improved sweet pepper detection for robotic harvesting. IEEE Robot. Autom. Lett. 1(1), 578–584 (2016). https://doi.org/10.1109/lra.2016.2523553https://doi.org/10.1109/lra. https://doi.org/10.1109/lra.2016.25235532016.2523553

    Article  Google Scholar 

  221. Zhao, X, Li, H, Zhu, Q, Huang, M, Guo, Y, and, J Q: Automatic sweet pepper detection based on point cloud images using subtractive clustering. Int. J. Agric. Biol. Eng. 13(3), 154–160 (2020). https://doi.org/10.25165/j.ijabe.20201303.5460

    Google Scholar 

  222. Yu, L, Xiong, J, Fang, X, Yang, Z, Chen, Y, Lin, X, Chen, S: A litchi fruit recognition method in a natural environment using RGB-d images. Biosyst. Eng. 204, 50–63 (2021). https://doi.org/10.1016/j.biosystemseng.2021.01.015https://doi.org/ https://doi.org/10.1016/j.biosystemseng.2021.01.01510.1016/j.biosystemseng.2021.01.015

    Article  Google Scholar 

  223. Lin, G, Tang, Y, Zou, X, Li, J, Xiong, J: In-field citrus detection and localisation based on RGB-d image analysis. Biosyst. Eng. 186, 34–44 (2019). https://doi.org/10.1016/j.biosystemseng.2019.06.019https://doi.org/10.1016/j.biosystemseng. https://doi.org/10.1016/j.biosystemseng.2019.06.0192019.06.019

    Article  Google Scholar 

  224. Lin, G, Tang, Y, Zou, X, Xiong, J, Li, J: Guava detection and pose estimation using a low-cost RGB-d sensor in the field. Sensors 19(2), 428 (2019). https://doi.org/10.3390/s19020428

    Article  Google Scholar 

  225. Xiang, R: Image segmentation for whole tomato plant recognition at night. Comput. Electron. Agric. 154, 434–442 (2018). https://doi.org/10.1016/j.compag.2018.09.034

    Article  Google Scholar 

  226. Xu, D, Chen, L, Mou, X, Wu, Q, Sun, G: 3d reconstruction of camellia oleifera fruit recognition and fruit branch based on kinect camera. In: 2021 2nd International Conference on Artificial Intelligence and Information Systems. ACM (2021)

  227. Joseph, SP, Wijerathna, L L M C, Epa, K G R D, Egalla, E K W A P K, Abeygunawardhana, P W K, Silva, R D: Smart harvesting based on image processing. In: 2020 International Computer Symposium (ICS). IEEE (2020)

  228. Hou, X, Xie, Y, Wang, L: Recognition and location of persimmons based on k-means and epipolar constraint SIFT matching algorithm. In: 2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE). IEEE (2020)

  229. Yang, Y, Ma, X, Mu, C, Wang, Z: Rapid recognition and localization based on deep learning and random filtering. In: 2019 5th International Conference on Control, Automation and Robotics (ICCAR). IEEE (2019)

  230. Pothen, Z S, Nuske, S: Texture-based fruit detection via images using the smooth patterns on the fruit. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2016)

  231. Feng, Q, Zou, W, Fan, P, Zhang, C, Wang, X, and: Design and test of robotic harvesting system for cherry tomato. Int J. Agric. Biol. Eng. 11(1), 96–100 (2018). https://doi.org/10.25165/j.ijabe.20181101.2853https://doi.org/10.25165/j.ijabe. https://doi.org/10.25165/j.ijabe.20181101.285320181101.2853

    Google Scholar 

  232. Zhou, T, Zhang, D, Zhou, M, Xi, H, Chen, X: System design of tomatoes harvesting robot based on binocular vision. In: 2018 Chinese Automation Congress (CAC). IEEE (2018)

  233. Zhang, J: Target extraction of fruit picking robot vision system. J. Phys.: Conf. Ser. 1423 (1), 012061 (2019). https://doi.org/10.1088/1742-6596/1423/1/012061https://doi.org/10. https://doi.org/10.1088/1742-6596/1423/1/0120611088/1742-6596/1423/1/012061

    Google Scholar 

  234. Wang, Y, Yang, Y, Yang, C, Zhao, H, Chen, G, Zhang, Z, Fu, S, Zhang, M, Xu, H: End-effector with a bite mode for harvesting citrus fruit in random stalk orientation environment. Comput. Electron. Agric. 157, 454–470 (2019). https://doi.org/10.1016/j.compag.2019.01.015https://doi.org/10. https://doi.org/10.1016/j.compag.2019.01.0151016/j.compag.2019.01.015

    Article  Google Scholar 

  235. Onishi, Y, Yoshida, T, Kurita, H, Fukao, T, Arihara, H, Iwai, A: An automated fruit harvesting robot by using deep learning. Robomech J. 6(1). https://doi.org/10.1186/s40648-019-0141-2 (2019)

  236. Zapotezny-Anderson, P, Lehnert, C: Towards active robotic vision in agriculture: A deep learning approach to visual servoing in occluded and unstructured protected cropping environments. IFAC-PapersOnLine 52(30), 120–125 (2019). https://doi.org/10.1016/j.ifacol.2019.12.508https://doi.org/10. https://doi.org/10.1016/j.ifacol.2019.12.5081016/j.ifacol.2019.12.508

    Article  MathSciNet  Google Scholar 

  237. Wang, C, Tang, Y, Zou, X, Luo, L, Chen, X: Recognition and matching of clustered mature litchi fruits using binocular charge-coupled device (CCD) color cameras. Sensors 17(11), 2564 (2017). https://doi.org/10.3390/s17112564

    Article  Google Scholar 

  238. Chen, M, Tang, Y, Zou, X, Huang, K, Huang, Z, Zhou, H, Wang, C, Lian, G: Three-dimensional perception of orchard banana central stock enhanced by adaptive multi-vision technology. Comput. Electron. Agric. 174, 105508 (2020). https://doi.org/10.1016/j.compag.2020.105508

    Article  Google Scholar 

  239. Williams, H AM, Jones, M H, Nejati, M, Seabright, M J, Bell, J, Penhall, N D, Barnett, J J, Duke, M D, Scarfe, A J, Ahn, H S, Lim, J, MacDonald, B A: Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms. Biosyst. Eng. 181, 140–156 (2019). https://doi.org/10.1016/j.biosystemseng.2019.03.007

    Article  Google Scholar 

  240. Yin, W, Wen, H, Ning, Z, Ye, J, Dong, Z, Luo, L: Fruit detection and pose estimation for grape cluster–harvesting robot using binocular imagery based on deep neural networks. Front. Robot. AI 8. https://doi.org/10.3389/frobt.2021.626989 (2021)

  241. Paturkar, A: Apple detection for harvesting robot using computer vision. HELIX 8(6), 4370–4374 (2018). https://doi.org/10.29042/2018-4370-4374https://doi.org/10. https://doi.org/10.29042/2018-4370-437429042/2018-4370-4374

    Article  Google Scholar 

  242. Altaheri, H, Alsulaiman, M, Muhammad, G: Date fruit classification for robotic harvesting in a natural environment using deep learning. IEEE Access 7, 117115–117133 (2019). https://doi.org/10.1109/access.2019.2936536

    Article  Google Scholar 

  243. Nilay, K, Mandal, S, Agarwal, Y, Gupta, R, Patel, M, Kumar, S, Shah, P, Dey, S, Annanya: A proposal of FPGA-based low cost and power efficient autonomous fruit harvester. In: 2020 6th International Conference on Control, Automation and Robotics (ICCAR). IEEE (2020)

  244. Xiong, Y, Ge, Y, From, P J: Push and drag: An active obstacle separation method for fruit harvesting robots. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2020)

  245. Rasolzadeh, B, Björkman, M., Huebner, K, Kragic, D: An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World. Int. J. Robot. Res. 29(2-3), 133–154 (2010). https://doi.org/10.1177/0278364909346069

    Article  Google Scholar 

  246. Jian, L, Mingrui, Z, Xifeng, G: A fruit detection algorithm based on r-FCN in natural scene. In: 2020 Chinese Control And Decision Conference (CCDC). IEEE (2020)

  247. Wen, C, Zhang, H, Li, H, Li, H, Chen, J, Guo, H, Cheng, S: Multi-scene citrus detection based on multi-task deep learning network. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE (2020)

  248. Mai, X, Zhang, H, Meng, M. Q.-H.: Faster r-CNN with classifier fusion for small fruit detection. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2018)

  249. Gao, F, Fu, L, Zhang, X, Majeed, Y, Li, R, Karkee, M, Zhang, Q: Multi-class fruit-on-plant detection for apple in SNAP system using faster r-CNN. Comput. Electron. Agric. 176, 105634 (2020). https://doi.org/10.1016/j.compag.2020.105634

    Article  Google Scholar 

  250. Yang, Q, Chen, C, Dai, J, Xun, Y, Bao, G: Tracking and recognition algorithm for a robot harvesting oscillating apples. Int. J. Agric. Biol. Eng. 13(5), 163–170 (2020). https://doi.org/10.25165/j.ijabe.20201305.5520https://doi.org/10. https://doi.org/10.25165/j.ijabe.20201305.552025165/j.ijabe.20201305.5520

    Google Scholar 

  251. Zhao, Y, Gong, L, Zhou, B, Huang, Y, Liu, C: Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis. Biosyst. Eng. 148, 127–137 (2016). https://doi.org/10.1016/j.biosystemseng.2016.05.001https://doi. https://doi.org/10.1016/j.biosystemseng.2016.05.001org/10.1016/j.biosystemseng.2016.05.001

    Article  Google Scholar 

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Funding

The research leading to these results has received funding from National Funds through the Portuguese funding agency, FCT – Fundação para a Ciência e Tecnologia and from the European Social fund (FSE), within the scholarship agreement number SFRH/BD/147117/2019. The research leading to these results has also received funding from the European Union’s Horizon 2020 – The EU Framework Programme for Research and Innovation 2014-2020, under grant agreement No. 857202.

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Authors and Affiliations

  1. CRIIS - Centre of Industrial Robotics and Intelligent System, INESC TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, Campus da FEUP, Rua Dr. Roberto Frias s/n, 4200-465, Porto, Portugal

    Sandro Augusto Magalhães, António Paulo Moreira & Filipe Neves dos Santos

  2. Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias s/n, 4200-465, Porto, Portugal

    Sandro Augusto Magalhães & António Paulo Moreira

  3. Institute of Systems and Robotics, Department of Electrical Engineering and Computers, University of Coimbra, Rua Silvio Lima – Pólo II, 3030-290, Coimbra, Portugal

    Jorge Dias

  4. Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science, Technology and Research (KU), Abu Dhabi, 127788, United Arab Emirates

    Jorge Dias

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  1. Sandro Augusto Magalhães
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  3. Filipe Neves dos Santos
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Contributions

Conceptualization, S.A.M. and F.N.d.S.; data curation, S.A.M.; funding acquisition, S.A.M and F.N.d.S.; investigation, S.A.M.; methodology, S.A.M. ; project administration, S.A.M, F.N.d.S., J.D. and A.P.M; supervision, F.N.d.S., J.D. and A.P.M.; validation, F.N.d.S., J.D. and A.P.M.; writing—original draft, S.A.M.; writing—review and editing, S.A.M., F.N.d.S., J.D. and A.P.M.

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The research leading to these results has received funding from National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e Tecnologia and from the European Social Fund (FSE), within the scholarship agreement number SFRH/BD/147117/2019. The research leading to these results has also received funding from the European Union’s Horizon 2020 - The EU Framework Programme for Research and Innovation 2014-2020, under grant agreement No. 857202.

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Magalhães, S.A., Moreira, A.P., Santos, F.N.d. et al. Active Perception Fruit Harvesting Robots — A Systematic Review. J Intell Robot Syst 105, 14 (2022). https://doi.org/10.1007/s10846-022-01595-3

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