1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Control, Robotics, and Autonomous Systems
  4. Volume 2, 2019
  5. Article

Annual Review of Control, Robotics, and Autonomous Systems

Volume 2, 2019

Agricultural Robotics

Abstract

A key goal of contemporary agriculture is to dramatically increase production of food, feed, fiber, and biofuel products in a sustainable fashion, facing the pressure of diminishing farm labor supply. Agricultural robots can accelerate plant breeding and advance data-driven precision farming with significantly reduced labor inputs by providing task-appropriate sensing and actuation at fine spatiotemporal resolutions. This article highlights the distinctive challenges imposed on ground robots by agricultural environments, which are characterized by wide variations in environmental conditions, diversity and complexity of plant canopy structures, and intraspecies biological variation of physical and chemical characteristics and responses to the environment. Existing approaches to address these challenges are presented, along with their limitations; possible future directions are also discussed. Two key observations are that biology (breeding) and horticultural practices can reduce variabilities at the source and that publicly available benchmark data sets are needed to increase perception robustness and performance despite variability.

Keyword(s): agriculture, automation, precision farming, robotics
Loading

Article metrics loading...

/content/journals/10.1146/annurev-control-053018-023617
2019-05-03
2024-04-26
Loading full text...

Full text loading...

/deliver/fulltext/control/2/1/annurev-control-053018-023617.html?itemId=/content/journals/10.1146/annurev-control-053018-023617&mimeType=html&fmt=ahah

Literature Cited

  1. 1.  John AJ, Clark CEF, Freeman MJ, Kerrisk KL, Garcia SC, Halachmi I 2016. Milking robot utilization, a successful precision livestock farming evolution. Animal 10:1484–92
     [Google Scholar]
  2. 2.  Evered M, Burling P, Trotter M 2014. An investigation of predator response in robotic herding of sheep. Int. Proc. Chem. Biol. Environ. Eng. 63:49–54
     [Google Scholar]
  3. 3.  Vaughan R, Sumpter N, Henderson J, Frost A, Cameron S 2000. Experiments in automatic flock control. J. Robot. Auton. Syst. 31:109–17
     [Google Scholar]
  4. 4.  Trevelyan JP 1989. Sensing and control for sheep-shearing robots. IEEE J. Robot. Autom. 5:716–27
     [Google Scholar]
  5. 5.  Vroegindeweij BA, Blaauw SK, IJsselmuiden JMM, van Henten EJ 2018. Evaluation of the performance of PoultryBot, an autonomous mobile robotic platform for poultry houses. Biosyst. Eng. 174:295–315
     [Google Scholar]
  6. 6.  Von Borstel Luna FD, de la Rosa Aguilar E, Suárez Naranjo J, Gutiérrez Jagüey J 2017. Robotic system for automation of water quality monitoring and feeding in aquaculture shadehouse. IEEE Trans. Syst. Man Cybern. Syst. 47:1575–89
     [Google Scholar]
  7. 7.  Khan ZH, Khalid A, Iqbal J 2018. Towards realizing robotic potential in future intelligent food manufacturing systems. Innov. Food Sci. Emerg. Technol. 48:11–24
     [Google Scholar]
  8. 8.  Blackmore BS 2004. From precision farming to phytotechnology. Automation Technology for Off-Road Equipment: Proceedings of the October 2004 Conference162–65 St. Joseph, MI: Am. Soc. Agric. Biol. Eng.
     [Google Scholar]
  9. 9.  Araus JL, Cairns JE 2014. Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19:52–61
     [Google Scholar]
  10. 10.  Taylor JE, Charlton D, Yúnez-Naude A 2012. The end of farm labor abundance. Appl. Econ. Perspect. Policy 34:587–98
     [Google Scholar]
  11. 11.  Rye JF, Scott S 2018. International labour migration and food production in rural Europe: a review of the evidence. Sociol. Rural. 58:929–52
     [Google Scholar]
  12. 12.  Kwan F, Wu Y, Zhuo S 2018. Surplus agricultural labour and China's Lewis turning point. China Econ. Rev. 48:244–57
     [Google Scholar]
  13. 13.  Das M, N'Diaye P 2013. Chronicle of a decline foretold: Has China reached the Lewis turning point? Work. Pap. 13/26 Int. Monet. Fund Washington, DC:
  14. 14.  Bechar A, Vigneault C 2016. Agricultural robots for field operations: concepts and components. Biosyst. Eng. 149:94–111
     [Google Scholar]
  15. 15.  Bechar A, Vigneault C 2017. Agricultural robots for field operations. Part 2: operations and systems. Biosyst. Eng. 153:110–28
     [Google Scholar]
  16. 16.  Bergerman M, Billingsley J, Reid J, van Henten E 2016. Robotics in agriculture and forestry. Springer Handbook of Robotics B Siciliano, O Khatib 1065–77 Berlin: Springer
     [Google Scholar]
  17. 17.  Galceran E, Carreras M 2013. A survey on coverage path planning for robotics. Robot. Auton. Syst. 61:1258–76
     [Google Scholar]
  18. 18.  Latombe JC 1991. Exact cell decomposition. Robot Motion Planning200–47 Boston, MA: Springer
     [Google Scholar]
  19. 19.  Choset H, Pignon P 1998. Coverage path planning: the boustrophedon cellular decomposition. Field and Service Robotics A Zelinsky 203–9 London: Springer
     [Google Scholar]
  20. 20.  de Bruin S, Lerink P, Klompe A, van der Wal T, Heijting S 2009. Spatial optimisation of cropped swaths and field margins using GIS. Comput. Electron. Agric. 68:185–90
     [Google Scholar]
  21. 21.  Oksanen T, Visala A 2009. Coverage path planning algorithms for agricultural field machines. J. Field Robot. 26:651–68
     [Google Scholar]
  22. 22.  Jin J, Tang L 2010. Optimal coverage path planning for arable farming on 2D surfaces. Trans. ASABE 53:283–95
     [Google Scholar]
  23. 23.  Jin J, Tang L 2011. Coverage path planning on three‐dimensional terrain for arable farming. J. Field Robot. 28:424–40
     [Google Scholar]
  24. 24.  Hameed IA, la Cour-Harbo A, Osen OL 2016. Side-to-side 3D coverage path planning approach for agricultural robots to minimize skip/overlap areas between swaths. Robot. Auton. Syst. 76:36–45
     [Google Scholar]
  25. 25.  Bochtis DD, Vougioukas SG 2008. Minimising the non-working distance travelled by machines operating in a headland field pattern. Biosyst. Eng. 101:1–12
     [Google Scholar]
  26. 26.  Acar EU, Choset H, Rizzi AA, Atkar PN, Hull D 2002. Morse decompositions for coverage tasks. Int. J. Robot. Res. 21:331–44
     [Google Scholar]
  27. 27.  Ali O, Verlinden B, Van Oudheusden D 2009. Infield logistics planning for crop-harvesting operations. Eng. Optim. 41:183–97
     [Google Scholar]
  28. 28.  Bochtis DD, Sørensen CG 2009. The vehicle routing problem in field logistics part I. Biosyst. Eng. 104:447–57
     [Google Scholar]
  29. 29.  Hameed IA, Bochtis D, Sørensen CG 2013. An optimized field coverage planning approach for navigation of agricultural robots in fields involving obstacle areas. Int. J. Adv. Robot. Syst. 10:231–40
     [Google Scholar]
  30. 30.  Santoro E, Soler EM, Cherri AC 2017. Route optimization in mechanized sugarcane harvesting. Comput. Electron. Agric. 141:140–46
     [Google Scholar]
  31. 31.  Bochtis DD, Sørensen CG 2010. The vehicle routing problem in field logistics: part II. Biosyst. Eng. 105:180–88
     [Google Scholar]
  32. 32.  Conesa-Muñoz J, Bengochea-Guevara JM, Andujar D, Ribeiro A 2016. Route planning for agricultural tasks: a general approach for fleets of autonomous vehicles in site-specific herbicide applications. Comput. Electron. Agric. 127:204–20
     [Google Scholar]
  33. 33.  Drexl M 2012. Synchronization in vehicle routing—a survey of VRPs with multiple synchronization constraints. Transp. Sci. 46:297–316
     [Google Scholar]
  34. 34.  Thayer TC, Vougioukas SG, Goldberg K, Carpin S 2018. Routing algorithms for robot assisted precision irrigation. IEEE International Conference on Robotics and Automation2221–28 New York: IEEE
     [Google Scholar]
  35. 35.  Gracia C, Diezma-Iglesias B, Barreiro P 2013. A hybrid genetic algorithm for route optimization in the bale collecting problem. Span. J. Agric. Res. 11:603–14
     [Google Scholar]
  36. 36.  Reinecke M, Grothaus HP, Hembach G, Scheuren S, Hartanto R 2013. Dynamic and distributed infield-planning system for harvesting. 2013 ASABE Annual International Meeting pap. 131574280 St. Joseph, MI: Am. Soc. Agric. Biol. Eng.
     [Google Scholar]
  37. 37.  Scheuren S, Stiene S, Hartanto R, Hertzberg J, Reinecke M 2013. Spatio-temporally constrained planning for cooperative vehicles in a harvesting scenario. Künstl. Intell. 27:341–46
     [Google Scholar]
  38. 38.  Vougioukas SG, Spanomitros Y, Slaughter D 2012. Dispatching and routing of robotic crop-transport aids for fruit pickers using mixed integer programming. 2012 ASABE Annual International Meeting pap. 21338247 St. Joseph, MI: Am. Soc. Agric. Biol. Eng.
     [Google Scholar]
  39. 39.  Blender T, Buchner T, Fernandez B, Pichlmaier B, Schlegel C 2016. Managing a mobile agricultural robot swarm for a seeding task. IECON 2016: 42nd Annual Conference of the IEEE Industrial Electronics Society6879–86 New York: IEEE
     [Google Scholar]
  40. 40.  Bertsimas DJ, Van Ryzin G 1991. A stochastic and dynamic vehicle routing problem in the Euclidean plane. Oper. Res. 39:601–15
     [Google Scholar]
  41. 41.  Bent RW, Van Hentenryck P 2004. Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Oper. Res. 52:977–87
     [Google Scholar]
  42. 42.  Kumar SN, Panneerselvam R 2012. A survey on the vehicle routing problem and its variants. Intell. Inf. Manag. 4:66–74
     [Google Scholar]
  43. 43.  Pavlou D, Orfanou A, Bochtis D, Tamvakidis S, Aidonis D 2017. Simulation and assessment of agricultural biomass supply chain systems. Int. J. Bus. Sci. Appl. Manag. 11:247–56
     [Google Scholar]
  44. 44.  Paden B, Čáp M, Yong SZ, Yershov D, Frazzoli E 2016. A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans. Intell. Veh. 1:33–55
     [Google Scholar]
  45. 45.  LaValle S, Hutchinson S 1998. Optimal motion planning for multiple robots having independent goals. IEEE Trans. Robot. Autom. 14:912–25
     [Google Scholar]
  46. 46.  Oksanen T, Visala A 2004. Optimal control of tractor-trailer system in headlands. Automation Technology for Off-Road Equipment: Proceedings of the 2004 Conference255–63 St. Joseph, MI: Am. Soc. Agric. Biol. Eng.
     [Google Scholar]
  47. 47.  Sabelhaus D, Röben F, zu Helligen LPM, Lammers PS 2013. Using continuous-curvature paths to generate feasible headland turn manoeuvres. Biosyst. Eng. 116:399–409
     [Google Scholar]
  48. 48.  Backman J, Piirainen P, Oksanen T 2015. Smooth turning path generation for agricultural vehicles in headlands. Biosyst. Eng. 139:76–86
     [Google Scholar]
  49. 49.  Cariou C, Gobor Z, Seiferth B, Berducat M 2017. Mobile robot trajectory planning under kinematic and dynamic constraints for partial and full field coverage. J. Field Robot. 34:1297–312
     [Google Scholar]
  50. 50.  Linker R, Blass T 2008. Path-planning algorithm for vehicles operating in orchards. Biosyst. Eng. 101:152–60
     [Google Scholar]
  51. 51.  Vougioukas SG, Blackmore S, Nielsen J, Fountas S 2006. A two-stage optimal motion planner for autonomous agricultural vehicles. Precis. Agric. 7:361–77
     [Google Scholar]
  52. 52.  Thuilot B, Cariou C, Martinet P, Berducat M 2002. Automatic guidance of a farm tractor relying on a single CP-DGPS. Auton. Robots 13:53–71
     [Google Scholar]
  53. 53.  O'Connor M, Bell T, Elkaim G, Parkinson B 1996. Automatic steering of farm vehicles using GPS. Proceedings of the Third International Conference on Precision Agriculture PC Robert, RH Rust, WE Larson 767–77 Madison, WI: Am. Soc. Agron.
     [Google Scholar]
  54. 54.  Ehsani MR, Upadhyaya SK, Mattson ML 2004. Seed location mapping using RTK GPS. Trans. ASAE 47:909–14
     [Google Scholar]
  55. 55.  Griepentrog HW, Nørremark M, Nielsen H, Blackmore BS 2005. Seed mapping of sugar beet. Precis. Agric. 6:157–65
     [Google Scholar]
  56. 56.  Sun H, Slaughter DC, Ruiz MP, Gliever C, Upadhyaya SK, Smith RF 2010. RTK GPS mapping of transplanted row crops. Comput. Electron. Agric. 71:32–37
     [Google Scholar]
  57. 57.  Abidine A, Heidman B, Upadhyaya S, Hills D 2004. Autoguidance system operated at high speed causes almost no tomato damage. Calif. Agric. 58:44–47
     [Google Scholar]
  58. 58.  Bakker T, van Asselt K, Bontsema J, Müller J, van Straten G 2011. Autonomous navigation using a robot platform in a sugar beet field. Biosyst. Eng. 109:357–68
     [Google Scholar]
  59. 59.  Bergerman M, Maeta SM, Zhang J, Freitas GM, Hamner B et al. 2015. Robot farmers: autonomous orchard vehicles help tree fruit production. IEEE Robot. Autom. Mag. 22:54–63
     [Google Scholar]
  60. 60.  Bayar G, Bergerman M, Koku AB 2016. Improving the trajectory tracking performance of autonomous orchard vehicles using wheel slip compensation. Biosyst. Eng. 146:149–64
     [Google Scholar]
  61. 61.  Cariou C, Lenain R, Thuilot B, Berducat M 2009. Automatic guidance of a four‐wheel‐steering mobile robot for accurate field operations. J. Field Robot. 26:504–18
     [Google Scholar]
  62. 62.  Kayacan E, Kayacan E, Khanesar MA 2015. Identification of nonlinear dynamic systems using type-2 fuzzy neural networks—a novel learning algorithm and a comparative study. IEEE Trans. Ind. Electron. 62:1716–24
     [Google Scholar]
  63. 63.  Taghia J, Wang X, Lam S, Katupitiya J 2017. A sliding mode controller with a nonlinear disturbance observer for a farm vehicle operating in the presence of wheel slip. Auton. Robots 41:71–88
     [Google Scholar]
  64. 64.  Lenain R, Thuilot B, Cariou C, Martinet P 2006. High accuracy path tracking for vehicles in presence of sliding: application to farm vehicle automatic guidance for agricultural tasks. Auton. Robots 21:79–97
     [Google Scholar]
  65. 65.  Backman J, Oksanen T, Visala A 2012. Navigation system for agricultural machines: nonlinear model predictive path tracking. Comput. Electron. Agric. 82:32–43
     [Google Scholar]
  66. 66.  Kayacan E, Kayacan E, Ramon H, Saeys W 2015. Robust tube-based decentralized nonlinear model predictive control of an autonomous tractor-trailer system. IEEE/ASME Trans. Mechatron. 20:447–56
     [Google Scholar]
  67. 67.  Tian L, Slaughter DC, Norris RF 1997. Outdoor field machine vision identification of tomato seedlings for automated weed control. Trans. Am. Soc. Agric. Eng. 40:1761–68
     [Google Scholar]
  68. 68.  Montalvo M, Pajares G, Guerrero JM, Romeo J, Guijarro M et al. 2012. Automatic detection of crop rows in maize fields with high weeds pressure. Expert Syst. Appl. 39:11889–97
     [Google Scholar]
  69. 69.  Reid J, Searcy S 1987. Vision-based guidance of an agriculture tractor. IEEE Control Syst. Mag. 7:39–43
     [Google Scholar]
  70. 70.  Han S, Zhang Q, Ni B, Reid JF 2004. A guidance directrix approach to vision-based vehicle guidance systems. Comput. Electron. Agric. 43:179–95
     [Google Scholar]
  71. 71.  Marchant JA, Andersen HJ, Onyango CM 2001. Evaluation of an imaging sensor for detecting vegetation using different waveband combinations. Comput. Electron. Agric. 32:101–17
     [Google Scholar]
  72. 72.  Kaizu Y, Imou K 2008. A dual-spectral camera system for paddy rice seedling row detection. Comput. Electron. Agric. 63:49–56
     [Google Scholar]
  73. 73.  Philipp I, Rath T 2002. Improving plant discrimination in image processing by use of different colour space transformations. Comput. Electron. Agric. 35:1–15
     [Google Scholar]
  74. 74.  Meyer GE, Neto JC 2008. Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 63:282–93
     [Google Scholar]
  75. 75.  Ronneberger O, Fischer P, Brox T 2015. U-net: convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention: MICCAI 2015 N Navab, J Hornegger, W Wells, A Frangi 234–241 Cham, Switz.: Springer
     [Google Scholar]
  76. 76.  Shkanaev AY, Polevoy DV, Panchenko AV, Krokhina DA, Nailevish SR 2018. Analysis of straw row in the image to control the trajectory of the agricultural combine harvester. Tenth International Conference on Machine Vision (ICMV 2017) pap. 1069602. Proc. SPIE 10696 Bellingham, WA: Int. Soc. Opt. Photon.
     [Google Scholar]
  77. 77.  Hague T, Tillett ND 2001. A bandpass filter-based approach to crop row location and tracking. Mechatronics 11:1–12
     [Google Scholar]
  78. 78.  Tillett ND, Hague T, Miles SJ 2002. Inter-row vision guidance for mechanical weed control in sugar beet. Comput. Electron. Agric. 33:163–77
     [Google Scholar]
  79. 79.  Søgaard HT, Olsen HJ 2003. Determination of crop rows by image analysis without segmentation. Comput. Electron. Agric. 38:141–58
     [Google Scholar]
  80. 80.  Ball D, Upcroft B, Wyeth G, Corke P, English A et al. 2016. Vision‐based obstacle detection and navigation for an agricultural robot. J. Field Robot. 33:1107–30
     [Google Scholar]
  81. 81.  Billingsley J, Schoenfisch M 1995. Vision-guidance of agricultural vehicles. Auton. Robots 2:65–76
     [Google Scholar]
  82. 82.  Åstrand B, Baerveldt AJ 2005. A vision based row-following system for agricultural field machinery. Mechatronics 15:251–69
     [Google Scholar]
  83. 83.  Leemans V, Destain MF 2006. Application of the Hough transform for seed row localisation using machine vision. Biosyst. Eng. 94:325–36
     [Google Scholar]
  84. 84.  Winterhalter W, Fleckenstein FV, Dornhege C, Burgard W 2018. Crop row detection on tiny plants with the pattern hough transform. IEEE Robot. Autom. Lett. 3:3394–401
     [Google Scholar]
  85. 85.  Kise M, Zhang Q, Más FR 2005. A stereovision-based crop row detection method for tractor-automated guidance. Biosyst. Eng. 90:357–67
     [Google Scholar]
  86. 86.  Wang Q, Zhang Q, Rovira-Más F, Tian L 2011. Stereovision-based lateral offset measurement for vehicle navigation in cultivated stubble fields. Biosyst. Eng. 109:258–65
     [Google Scholar]
  87. 87.  Subramanian V, Burks TF, Arroyo AA 2006. Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation. Comput. Electron. Agric. 53:130–43
     [Google Scholar]
  88. 88.  Zhang J, Kantor G, Bergerman M, Singh S 2012. Monocular visual navigation of an autonomous vehicle in natural scene corridor-like environments. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems3659–66 New York: IEEE
     [Google Scholar]
  89. 89.  Radcliffe J, Cox J, Bulanon DM 2018. Machine vision for orchard navigation. Comput. Ind. 98:165–71
     [Google Scholar]
  90. 90.  Sharifi M, Chen X 2015. A novel vision based row guidance approach for navigation of agricultural mobile robots in orchards. 2015 6th International Conference on Automation, Robotics and Applications251–55 New York: IEEE
     [Google Scholar]
  91. 91.  Barawid OC, Mizushima A, Ishii K, Noguchi N 2007. Development of an autonomous navigation system using a two-dimensional laser scanner in an orchard application. Biosyst. Eng. 96:139–49
     [Google Scholar]
  92. 92.  Andersen JC, Ravn O, Andersen NA 2010. Autonomous rule-based robot navigation in orchards. IFAC Proc. Vol. 43:1643–48
     [Google Scholar]
  93. 93.  Hansen S, Bayramoglu E, Andersen JC, Ravn O, Andersen N, Poulsen NK 2011. Orchard navigation using derivative free Kalman filtering. 2011 American Control Conference4679–84 New York: IEEE
     [Google Scholar]
  94. 94.  Libby J, Kantor G 2011. Deployment of a point and line feature localization system for an outdoor agriculture vehicle. 2011 IEEE International Conference on Robotics and Automation1565–70 New York: IEEE
     [Google Scholar]
  95. 95.  Marden S, Whitty M 2014. GPS-free localisation and navigation of an unmanned ground vehicle for yield forecasting in a vineyard Paper presented at the Recent Advances in Agricultural Robotics International Workshop, 13th International Conference on Intelligent Autonomous Systems (IAS-13), Padova, Italy, July 18
  96. 96.  Zhang J, Chambers A, Maeta S, Bergerman M, Singh S 2013. 3D perception for accurate row following: methodology and results. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems5306–13 New York: IEEE
     [Google Scholar]
  97. 97.  Durand-Petiteville A, Le Flecher E, Cadenat V, Sentenac T, Vougioukas S 2018. Tree detection with low-cost 3D sensors for autonomous navigation in orchards. IEEE Robot. Autom. Lett. 3:3876–83
     [Google Scholar]
  98. 98.  Moorehead SJ, Wellington CK, Gilmore BJ, Vallespi C 2012. Automating orchards: a system of autonomous tractors for orchard maintenance Paper presented at the Workshop on Agricultural Robotics, IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Port., Oct. 7–12
  99. 99.  Zhang J, Maeta S, Bergerman M, Singh S 2014. Mapping orchards for autonomous navigation. 2014 ASABE Annual International Meeting pap. 141838567. St Joseph, MI: Am. Soc. Agric. Biol. Eng.
     [Google Scholar]
  100. 100.  Subramanian V, Burks TF 2007. Autonomous vehicle turning in the headlands of citrus groves. 2007 ASAE Annual Meeting pap. 071015 St. Joseph, MI: Am. Soc. Agric. Biol. Eng.
     [Google Scholar]
  101. 101.  Bayar G, Bergerman M, Koku AB, Konukseven E 2015. Localization and control of an autonomous orchard vehicle. Comput. Electron. Agric. 115:118–28
     [Google Scholar]
  102. 102.  Janai J, Güney F, Behl A, Geiger A 2017. Computer vision for autonomous vehicles: problems, datasets and state-of-the-art. arXiv:1704.05519 [cs.CV]
  103. 103.  Holopainen JK, Gershenzon J 2010. Multiple stress factors and the emission of plant VOCs. Trends Plant Sci 15:176–84
     [Google Scholar]
  104. 104.  Parker GG 1995. Structure and microclimate of forest canopies. Forest Canopies: A Review of Research on a Biological Frontier M Lowman, N Nadkarni 73–106 San Diego, CA: Academic
     [Google Scholar]
  105. 105.  Fountas S, Paraforos D, Cavalaris C, Karamoutis C, Gemtos TA et al. 2013. A five-point penetrometer with GPS for measuring soil compaction variability. Comput. Electron. Agric. 96:109–16
     [Google Scholar]
  106. 106.  Mueller-Sim T, Jenkins M, Abel J, Kantor G 2017. The Robotanist: a ground-based agricultural robot for high-throughput crop phenotyping. IEEE International Conference on Robotics and Automation3634–39 New York: IEEE
     [Google Scholar]
  107. 107.  Mason MT 2018. Toward robotic manipulation. Annu. Rev. Control Robot. Auton. Syst. 1:1–28
     [Google Scholar]
  108. 108.  Hunt ER, Daughtry CS 2018. What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?. Int. J. Remote Sens. 39:5345–76
     [Google Scholar]
  109. 109.  Ojha T, Misra S, Raghuwanshi NS 2015. Wireless sensor networks for agriculture: the state-of-the-art in practice and future challenges. Comput. Electron. Agric. 118:66–84
     [Google Scholar]
  110. 110.  Mulla DJ 2013. Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst. Eng. 114:358–71
     [Google Scholar]
  111. 111.  Lee WS, Alchanatis V, Yang C, Hirafuji M, Moshou D, Li C 2010. Sensing technologies for precision specialty crop production. Comput. Electron. Agric. 74:2–33
     [Google Scholar]
  112. 112.  Sankaran S, Mishra A, Ehsani R, Davis C 2010. A review of advanced techniques for detecting plant diseases. Comput. Electron. Agric. 72:1–13
     [Google Scholar]
  113. 113.  Mahlein AK, Kuska MT, Behmann J, Polder G, Walter A 2018. Hyperspectral sensors and imaging technologies in phytopathology: state of the art. Annu. Rev. Phytopathol. 56:535–58
     [Google Scholar]
  114. 114.  Fennimore SA, Slaughter DC, Siemens MC, Leon RG, Saber MN 2016. Technology for automation of weed control in specialty crops. Weed Technol 30:823–37
     [Google Scholar]
  115. 115.  Liu H, Lee SH, Chahl JS 2017. A review of recent sensing technologies to detect invertebrates on crops. Precis. Agric. 18:635–66
     [Google Scholar]
  116. 116.  Hamuda E, Glavin M, Jones E 2016. A survey of image processing techniques for plant extraction and segmentation in the field. Comput. Electron. Agric. 125:184–99
     [Google Scholar]
  117. 117.  Singh A, Ganapathysubramanian B, Singh AK, Sarkar S 2016. Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci 21:110–24
     [Google Scholar]
  118. 118.  Verrelst J, Malenovský Z, Van der Tol C, Camps-Valls G, Gastellu-Etchegorry JP et al. 2018. Quantifying vegetation biophysical variables from imaging spectroscopy data: a review on retrieval methods. Surv. Geophys. In press. https://doi.org/10.1007/s10712-018-9478-y
    [Crossref]
  119. 119.  Gongal A, Amatya S, Karkee M, Zhang Q, Lewis K 2015. Sensors and systems for fruit detection and localization: a review. Comput. Electron. Agric. 116:8–19
     [Google Scholar]
  120. 120.  Tabb A, Medeiros H 2017. A robotic vision system to measure tree traits. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems6005–12 New York: IEEE
     [Google Scholar]
  121. 121.  Dong W, Isler V 2018. Tree morphology for phenotyping from semantics-based mapping in orchard environments. arXiv:1804.05905 [cs.RO]
  122. 122.  Gibbs JA, Pound M, French AP, Wells DM, Murchie E, Pridmore T 2017. Approaches to three-dimensional reconstruction of plant shoot topology and geometry. Funct. Plant Biol. 44:62–75
     [Google Scholar]
  123. 123.  Barbedo JGA 2016. A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst. Eng. 144:52–60
     [Google Scholar]
  124. 124.  Mittler R 2006. Abiotic stress, the field environment and stress combination. Trends Plant Sci 11:15–19
     [Google Scholar]
  125. 125.  Kamilaris A, Prenafeta-Boldú FX 2018. Deep learning in agriculture: a survey. Comput. Electron. Agric. 147:70–90
     [Google Scholar]
  126. 126.  Dias PA, Tabb A, Medeiros H 2018. Apple flower detection using deep convolutional networks. Comput. Ind. 99:17–28
     [Google Scholar]
  127. 127.  Sa I, Ge Z, Dayoub F, Upcroft B, Perez T, McCool C 2016. DeepFruits: a fruit detection system using deep neural networks. Sensors 16:1222–45
     [Google Scholar]
  128. 128.  Tao Y, Zhou J 2017. Automatic apple recognition based on the fusion of color and 3D feature for robotic fruit picking. Comput. Electron. Agric. 142:388–96
     [Google Scholar]
  129. 129.  Bargoti S, Underwood J 2017. Deep fruit detection in orchards. 2017 IEEE International Conference on Robotics and Automation3626–33 New York: IEEE
     [Google Scholar]
  130. 130.  Chostner B 2017. See & Spray: the next generation of weed control. Resource July/Aug. 4–5
  131. 131.  Mahmood HS, Hoogmoed WB, van Henten EJ 2012. Sensor data fusion to predict multiple soil properties. Precis. Agric. 13:628–45
     [Google Scholar]
  132. 132.  Mehta SS, Ton C, Asundi S, Burks TF 2017. Multiple camera fruit localization using a particle filter. Comput. Electron. Agric. 142:139–54
     [Google Scholar]
  133. 133.  Danson FM, Disney MI, Gaulton R, Schaaf C, Strahler A 2018. The terrestrial laser scanning revolution in forest ecology. Interface Focus 8:20180001
     [Google Scholar]
  134. 134.  Jimenez AR, Ceres R, Pons JL 2000. A survey of computer vision methods for locating fruit on trees. Trans. ASAE 43:1911–20
     [Google Scholar]
  135. 135.  Bulanon DM, Burks TF, Alchanatis V 2007. Study on fruit visibility for robotic harvesting. 2007 ASAE Annual Meeting pap. 073124 St. Joseph, MI: Am. Soc. Agric. Biol. Eng.
     [Google Scholar]
  136. 136.  Silwal A, Davidson JR, Karkee M, Mo C, Zhang Q, Lewis K 2017. Design, integration, and field evaluation of a robotic apple harvester. J. Field Robot. 34:1140–59
     [Google Scholar]
  137. 137.  Salisbury K, Steere D 2017. Final project report: phase 2 system integration Rep., Abund. Robot. Menlo Park, CA:
  138. 138.  Oberti R, Marchi M, Tirelli P, Calcante A, Iriti M et al. 2016. Selective spraying of grapevines for disease control using a modular agricultural robot. Biosyst. Eng. 146:203–15
     [Google Scholar]
  139. 139.  Calcante A, Brambilla M, Oberti R, Bisaglia C 2015. A retrofit variable-rate control system for pressurized slurry tankers. Appl. Eng. Agric. 31:569–79
     [Google Scholar]
  140. 140.  Forouzanmehr E, Loghavi M 2012. Design, development and field evaluation of a map-based variable rate granular fertilizer application control system. Agric. Eng. Int. CIGR J. 14:255–61
     [Google Scholar]
  141. 141.  Ohi N, Lassak K, Watson R, Strader J, Du Y et al. 2018. Design of an autonomous precision pollination robot. arXiv:1808.10010 [cs.RO]
  142. 142.  Yang L, Noguchi N, Takai R 2016. Development and application of a wheel-type robot tractor. Eng. Agric. Environ. Food 9:131–40
     [Google Scholar]
  143. 143.  He L, Zhou J, Zhang Q, Charvet HJ 2016. A string twining robot for high trellis hop production. Comput. Electron. Agric. 121:207–14
     [Google Scholar]
  144. 144.  Bac CW, van Henten EJ, Hemming J, Edan Y 2014. Harvesting robots for high‐value crops: state‐of‐the‐art review and challenges ahead. J. Field Robot. 31:888–911
     [Google Scholar]
  145. 145.  Burks T, Villegas F, Hannan M, Flood S, Sivaraman B et al. 2005. Engineering and horticultural aspects of robotic fruit harvesting: opportunities and constraints. HortTechnology 15:79–87
     [Google Scholar]
  146. 146.  Van Henten EJ, Van Tuijl BAJ, Hoogakker GJ, Van Der Weerd MJ, Hemming J et al. 2006. An autonomous robot for de-leafing cucumber plants grown in a high-wire cultivation system. Biosyst. Eng. 94:317–23
     [Google Scholar]
  147. 147.  Alenyà G, Dellen B, Foix S, Torras C 2013. Robotized plant probing: leaf segmentation utilizing time-of-flight data. IEEE Robot. Autom. Mag. 20:50–59
     [Google Scholar]
  148. 148.  Michaels A, Haug S, Albert A 2015. Vision-based high-speed manipulation for robotic ultra-precise weed control. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems5498–505 New York: IEEE
     [Google Scholar]
  149. 149.  Mehta SS, MacKunis W, Burks TF 2016. Robust visual servo control in the presence of fruit motion for robotic citrus harvesting. Comput. Electron. Agric. 123:362–75
     [Google Scholar]
  150. 150.  Zhao Y, Gong L, Huang Y, Liu C 2016. A review of key techniques of vision-based control for harvesting robot. Comput. Electron. Agric. 127:311–23
     [Google Scholar]
  151. 151.  You K, Burks TF 2016. Development of a robotic fruit picking end effector and an adaptable controller. 2016 ASABE Annual International Meeting pap. 162455128 St. Joseph, MI: Am. Soc. Agric. Biol. Eng.
     [Google Scholar]
  152. 152.  Sarig Y 1993. Robotics of fruit harvesting: a state-of-the-art review. J. Agric. Eng. Res. 54:265–80
     [Google Scholar]
  153. 153.  Blanes C, Mellado M, Ortiz C, Valera A 2011. Technologies for robot grippers in pick and place operations for fresh fruits and vegetables. Span. J. Agric. Res. 9:1130–41
     [Google Scholar]
  154. 154.  Cuevas-Velasquez H, Li N, Tylecek R, Saval-Calvo M, Fisher RB 2018. Hybrid multi-camera visual servoing to moving target. arXiv:1803.02285 [cs.CV]
  155. 155.  Lee AX, Levine S, Abbeel P 2017. Learning visual servoing with deep features and fitted Q-iteration. arXiv:1703.11000 [cs.LG]
  156. 156.  Quillen D, Jang E, Nachum O, Finn C, Ibarz J, Levine S 2018. Deep reinforcement learning for vision-based robotic grasping: a simulated comparative evaluation of off-policy methods. arXiv:1802.10264 [cs.RO]
  157. 157.  Rus D, Tolley MT 2015. Design, fabrication and control of soft robots. Nature 521:467–75
     [Google Scholar]
  158. 158.  Slaughter DC, Giles DK, Downey D 2008. Autonomous robotic weed control systems: a review. Comput. Electron. Agric. 61:63–78
     [Google Scholar]
  159. 159.  Bloch V, Degani A, Bechar A 2018. A methodology of orchard architecture design for an optimal harvesting robot. Biosyst. Eng. 166:126–37
     [Google Scholar]
  160. 160.  Schueller JK 2012. Another report from 25 years hence. Resource July/Aug. 16–17
  161. 161.  Vougioukas SG, Arikapudi R, Munic J 2016. A study of fruit reachability in orchard trees by linear-only motion. IFAC-PapersOnLine 49:16277–80
     [Google Scholar]
/content/journals/10.1146/annurev-control-053018-023617
Loading
Agricultural Robotics
Annual Review of Control, Robotics, and Autonomous Systems 2, 365 (2019); https://doi.org/10.1146/annurev-control-053018-023617
/content/journals/10.1146/annurev-control-053018-023617
/content/journals/10.1146/annurev-control-053018-023617
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error