Task-based agricultural mobile robots in arable farming: A review

Krishnaswamy R. Aravind, Purushothaman Raja, Manuel Pérez-Ruiz

Abstract


In agriculture (in the context of this paper, the terms “agriculture” and “farming” refer to only the farming of crops and exclude the farming of animals), smart farming and automated agricultural technology have emerged as promising methodologies for increasing the crop productivity without sacrificing produce quality. The emergence of various robotics technologies has facilitated the application of these techniques in agricultural processes. However, incorporating this technology in farms has proven to be challenging because of the large variations in shape, size, rate and type of growth, type of produce, and environmental requirements for different types of crops. Agricultural processes are chains of systematic, repetitive, and time-dependent tasks. However, some agricultural processes differ based on the type of farming, namely permanent crop farming and arable farming. Permanent crop farming includes permanent crops or woody plants such as orchards and vineyards whereas arable farmingincludestemporary crops such as wheat and rice. Major operations in open arable farming include tilling, soil analysis, seeding, transplanting, crop scouting, pest control, weed removal and harvesting and robots can assist in performing all of these tasks. Each specific operation requires axillary devices and sensors with specific functions. This article reviews the latest advances in the application of mobile robots in these agricultural operations for open arable farming and provide an overview of the systems and techniques that are used. This article also discusses various challenges for future improvements in using reliable mobile robots for arable farming.


Keywords


precision agriculture; task-based agricultural robots; soil analysis; seeding; weed detection; harvesting; crop scouting robot

Full Text:

PDF HTML XML

References


Adamchuk VI, Lund ED, Sethuramasamyraja B, Morgan MT, Dobermann A, Marx DB, 2005. Direct measurement of soil chemical properties on-the-go using ion-selective electrodes. Comput Electron Agric 48: 272-294. https://doi.org/10.1016/j.compag.2005.05.001

Antonelli MG, Auriti L, Zonel PB, Raparelli T, 2011. Development of a new harvesting module for saffron flower detachment. Romanian Rev Precis Mech Opt Mechatron 39: 163-168.

Arikapudi R, Durand-Petiteville A, Vougioukas S, 2014. Model-based assessment of robotic fruit harvesting cycle times. ASABE Ann Int Meeting Paper No. 1913999.

Baharom SNA, Shibusawa S, Kodaira M, Kanda R, 2015. Multiple-depth mapping of soil properties using a visible and near infrared real-time soil. Eng Agr Environ Food 8: 13-17. https://doi.org/10.1016/j.eaef.2015.01.002

Baier S, Clements M, Griffiths C, Ihrig J, 2009. Biofuels impact on crop and food prices. Int Financ Discuss Papers. Board of Governors of the Federal System. http://www.federalreserve.gov/pubs/ifdp/2009/967/ifdp967.pdf.

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: 357-368. https://doi.org/10.1016/j.biosystemseng.2011.05.001

Blackmore BS, 2009. New concepts in agricultural automation. HGCA Conference; October 2009.

Blackmore BS, Fountas S, Tang S, Have H, 2004a. Systems requirements for a small autonomous tractor. CIGR J Sci Res Dev Manuscript PM 04: 001.

Blackmore BS, Girepentrog HW, Nielsen H, Norremark M, Resting-Jeppersen J, 2004b. Development of a deterministic autonomous tractor. CIGR Olympics of Agr Eng Int Conf: October 2004.

Blackmore BS, Greipentrog HW, Fountas S, Gemtos TA, 2007. A specification for an autonomous crop production mechanization system. CIGR E- J 2007; Manuscript PM 06032: 9.

Blasco J, Aleixos N, Roger JM, Rabatel G, Moltó E, 2002. Robotics weed control using machine vision. Biosyst Eng 83: 149-157. https://doi.org/10.1006/bioe.2002.0109

Bongiovanni R, Lowenberg-Deboer J, 2004. Precision agriculture and sustainability. Precis Agric 5: 359-387. https://doi.org/10.1023/B:PRAG.0000040806.39604.aa

Busemeyer L, Mentrup D, Möller K, Wunder E, Alheit K, Hahn V, Maurer HP, Reif JC, Würschum T, Müller J, et al., 2013. BreedVision - A multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors 13: 2830-2847. https://doi.org/10.3390/s130302830

Chamen WCT, Dowler D, Leede PR, Longstaff DJ, 1994. Design, operation and performance of a gantry system: experience in arable cropping. J Agric Eng Res 59: 45-60. https://doi.org/10.1006/jaer.1994.1063

Chapman S, Merz T, Chan A, Jackway P, Hrabar S, Dreccer M, Holland E, Zheng B, Ling T, Jimenez-Berni J, 2014. Pheno-copter: A low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping. Agronomy 4: 279-301. https://doi.org/10.3390/agronomy4020279

Chatzimichali AP, Gerogilas IP, Tourassis VD, 2009. Design of an advanced prototype robot for white asparagus harvesting. IEEE/ASME Int Conf on Advanced Intelligent Mechatronics; July. pp: 887-892.

Chen B, Tojo S, Watanabe K, 2003. Machine vision for a micro weeding robot in a paddy field. Biosyst Eng 85: 393-404. https://doi.org/10.1016/S1537-5110(03)00078-3

Chocron O, Delaleau E, Pleureau JL, 2007. Flatness based control of a mechatronic weed killer autonomous robot. IEEE Int Symp on Indust Electron; June. pp: 2214-2219.

Comba L, Gay P, Piccarolo P, Aimonino RD, 2010. Robotics and automation for crop management: trends and perspective. Int Conf on Work Safety and Risk Prevention in Agro-Food and Forest Systems: September. pp: 471-478.

Córcoles JI, Ortega JF, Hernández D, Moreno MA, 2013. Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle. Biosyst Eng 115: 31-42. https://doi.org/10.1016/j.biosystemseng.2013.02.002

Emmi L, Gonzalez-de-Soto M, Pajares G, Gonzalez-de-Santos P, 2014. New trends in robotics for agriculture: integration and assessment of a real fleet of robots. Sci World J 2014 Article ID: 404059. https://doi.org/10.1155/2014/404059

Fahlgren N, Gehan MA, Baxter I, 2015. Lights, camera, action: high- throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol 24: 93-99. https://doi.org/10.1016/j.pbi.2015.02.006

Faiçal BS, Costa FG, Pessin G, Ueyama J, Freitas H, Colombo A, Fini PH, Villas L, Osório FS, Vargas PA, et al., 2014. The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides. J Syst Archit 60: 393-404. https://doi.org/10.1016/j.sysarc.2014.01.004

Gobor Z, Schulze Lammers P, Martinov M, 2013. Development of a mechatronic intra-row weeding system with rotational hoeing tools: Theoretical approach and simulation. Comput Electron Agric 98: 166-174. https://doi.org/10.1016/j.compag.2013.08.008

Griepentrog HW, Dühring Jaeger CL, Paraforos DS, 2013. Robots for field operations with comprehensive multilayer control. Künstl Intell 27: 325-333. https://doi.org/10.1007/s13218-013-0266-z

Grift T, 2007. Robotics in crop production. Encycl Agric Food Biol Eng. DOI: 10.1081/E-EAFE-120043046.

Grimstad L, Phan HNT, Pham CD, Bjugstad N, From PJ, 2015. Initial field-testing of Thorvald, a versatile robotic platform for agricultural applications. Proc of the IROS Workshop on Agri-Food Robotics. October.

Haibo L, Qing L, Yufeng X, Chuijje Y, 2010. Research and development on the key technology of wheat single seed robot. IEEE World Automation Congress; September. pp: 339-343.

Harries GO, Ambler B, 1981. Automatic ploughing: A tractor guidance system using opto-electronic remote sensing techniques and a microprocessor based controller. J Agric Eng Res 26: 33-53. https://doi.org/10.1016/0021-8634(81)90125-6

Haug S, Michaels A, Biber P, Ostermann J, 2014. Plant classification system for crop/weed discrimination without segementation. IEEE Winter Conf on Application of Computer Vision, March. pp: 1142-1149.

Horowitz J, Ebel R, Ueda K, 2010. No-till. Farming is a growing practice. USDA Econ Inform Bull 70. November.

Jensen K, Nielsen SH, Jorgensen RN, Bogild A, Jacobsen NJ, Jorgensen OJ, Hansen CLJ, 2012. A low cost, modular robotics tool carrier for precision agriculture research. Proc Int Conf on Precision Agriculture; July.

Jeon HY, Tian LF, 2009. Direct application end effector for a precise weed control robot. Biosyst Eng 104: 458-464. https://doi.org/10.1016/j.biosystemseng.2009.09.005

Kargar AHB, Shrizadifar AM, 2013. Automatic weed detection system and smart herbicide sprayer robot for corn fields. RSI/ISM Int Conf on Robotics and Mechatronics; February. pp: 13-15.

Kladivko EJ, 2001. Tillage systems and soil ecology. Soil Till Res 61: 61-76. https://doi.org/10.1016/S0167-1987(01)00179-9

Kumar S, 2014. Plant disease management in India: advances and challenges. Afr J Agric Res 9: 1207-1217. https://doi.org/10.5897/AJAR2014.7311

Lee WS, Slaughter DC, Giles DK, 1999. Robotics weed control system for tomatoes. Precis Agric 1: 95-113. https://doi.org/10.1023/A:1009977903204

Li L, Zhang Q, Huang D, 2014. A review of imaging techniques for plant phenotyping. Sensors 14: 20078-20111. https://doi.org/10.3390/s141120078

Libin Z, Qinghua Y, Guanjun B, Yan W, Liyong Q, Feng G, Fang X, 2008. Overview of research on agricultural robots in China. Int J Agric Biol Eng 1: 12-21.

Liu J, Li Z, Li P, Mao H, 2008. Design of a laser stem-cutting device for harvesting robot. IEEE Int Conf on Automation and Logistics; September. pp: 2370-2374.

Lobsey C, Rossel RV, Mcbratney A, 2010. An automated system for rapid in-field soil nutrient testing. 19th World Cong of Soil Science, Soil Solutions for a Change World; August. pp: 9-12.

Lund ED, Adamchuk VI, Collings KL, Drummond PE, Christy CD, 2005. Development of soil pH and lime requirement maps using on-the-go soil sensors. 5th Eur Conf on Precis Agr; July. pp: 457-464.

Mandal S, Maity A, 2013. Precision farming for small agricultural farm: Indian scenario. Am J Exp Agric 3: 200-217. https://doi.org/10.9734/AJEA/2013/2326

Matsuo Y, Yukumoto O, Noguchi N, 2012. Enhanced adaptability of tilling robot (initial report). JARQ 46: 295-303. https://doi.org/10.6090/jarq.46.295

Micheal AM, Ojha TP, 2008. Principles of agricultural engineering, vol. I. Jain, Brothers. 638 pp.

Nagasaka Y, Tamaki K, Nishiwaki K, Saito M, Motobayashi K, Kikuchi Y, Hosokawa H, 2011. Autonomous rice field operation project in NARO. IEEE Int Conf on Mechatronics and Automation; August. pp: 870-874.

Nakai S, Yamada Y, 2014. Development of a weed suppression robot for rice cultivation: weed suppression and posture control. Int J Electr Comput Electron Commun Eng 8: 1736-1740.

Nørremark M, Griepentrog HW, Nielsen J, Søgaard HT, 2008. The development and assessment of the accuracy of an autonomous GPS-based system for intra-row mechanical weed control in row crops. Biosyst Eng 101: 396-410. https://doi.org/10.1016/j.biosystemseng.2008.09.007

Ocampo JA, 2014. Concise report on the world population situation in 2014. Dept. of Economic and Social Affairs Population Division. United Nations, NY. http://www.un.org/en/development/desa/population/publications/trends/concise-report2014.shtml.

Oksanen T, 2013. Accuracy and performance experiences of four wheel steered autonomous agricultural tractor in sowing operation. Int Conf on Field Service Robotics; December. pp: 425-438.

Parker C, Fryer JD, 1975. Weed control problems causing major reduction in world food supplies. FAO Plant Prot Bull 23: 83-95.

Patnaik A, Narayanamoorthi R, 2015. Weed removal in cultivated field by autonomous robot using LabVIEW. IEEE Int Conf on Innovations in Information Embedded and Communication Systems; March. pp: 1-5.

Pavan W, Fraisse CW, Peres NA, 2011. Development of a web-based disease forecasting system for strawberries. Comput Electron Agric 75: 169-175. https://doi.org/10.1016/j.compag.2010.10.013

Pedersen SM, Fountas S, Have H, Blackmore BS, 2006. Agricultural robots—System analysis and economic feasibility. Precis Agric 7: 295-308. https://doi.org/10.1007/s11119-006-9014-9

Pedersen SM, Fountas S, Blackmore S, 2008. Agricultural robots-Applications and economic perspectives, service robot applications. http://www.intechopen.com/books/service_robot_applications/agricultural_robots_-_applications_and_economic_perspectives.

Pérez-Ruiz M, Gonzalez-de-Santos P, Ribeiro A, Fernandez-Quintanilla C, Peruzzi A, Vieri M, Tomic S, Agüera J, 2015. Highlights and preliminary results for autonomous crop protection. Comput Electron Agric 110: 150-161. https://doi.org/10.1016/j.compag.2014.11.010

Phillips AJ, Newlands NK, Liang SHL, Ellert BH, 2014. Integrated sensing of soil mositure at the field scale: measuring, modeling and sharing for improved agricultural decision support. Comput Electron Agric 8: 13-17.

Pilli SK, Nallathambi B, George SJ, Diwanji V, 2014. eAGROBOT - A robot for early crop disease detection using image processing. IEEE Int Conf on Electronics and Communication Systems; February. pp: 1-6.

Pobkrut T, Kerdcharoen T, 2014. Soil sensing survey robots based on electronic nose. Int Conf on Control, Automation and System. pp: 1604-1609. https://doi.org/10.1109/iccas.2014.6987829

Polder G, van der Heijden GWAM, van Doorn J, Baltissen TAHMC, 2014. Automatic detection of tulip breaking virus (TBV) in tulip fields using machine vision. Biosyst Eng 117: 35-42. https://doi.org/10.1016/j.biosystemseng.2013.05.010

Priyadarshini M, Sheela L, 2015. Command based self guided digging and seed sowing rover. Int Conf on Engineering Trends and Science & Humanities; March. pp: 5-9.

Rieder R, Pavan W, Maciel JMC, Fernandes JMC, Pinho MS, 2014. A virtual reality system to monitor and control diseases in strawberry with drones: A project. Proc 7th Int Cong on Environ Model & Software; June. pp: 919-926.

Riesen S, Rohrer L, 2011. Master Thesis on Gardening robotics - Design of a seed planting robot for the creation of large scale growing flower images. Swiss Federal Institute of Technology Zurich.

Rossel RAV, McBratney AB, 1998. Soil chemical analytical accuracy and costs: implications from precision agriculture. Aust J Exp Agric 38: 765-775. https://doi.org/10.1071/EA97158

Ruangurai P, Ekpanyapong M, Pruetong C, Watewai T, 2015. Automated three-wheel rice seeding robot operating in dry paddy fields. Maejo Int J Sci Tech 9: 403-412.

Ruckelshausen A, Biber P, Dorna M, Gremmes H, Klose R, Linz A, Rahe F, Resch R, Thiel M, Trautz D, et al., 2009. Bonirob — An autonomous field robot platform for individual plant phenotyping. Joint Int Agr Conf: July. pp: 841-847.

Sahay J, 2006. Elements of agricultural engineering. Standard Publ & Distrib, New Delhi. 462 pp.

Sankaran S, Mishra A, Ehsani R, Davis C, 2010. A review of advanced techniques for detecting plant diseases. Comput Electron Agric 72: 1-13. https://doi.org/10.1016/j.compag.2010.02.007

Schirrmann M, Gebbers R, Kramer E, Seidel J, 2011. Soil pH mapping with an on-the-go sensor. Sensors 11: 573-598. https://doi.org/10.3390/s110100573

Scholz C, Moeller K, Ruckelshausen A, Hinck S, Goettinger M, 2014. Automatic soil penetrometer measurements and GIS based documentation with the autonomous field robot platform bonirob. Int Conf on Precis Agr; July.

Sheng P, 2014. An intelligent robot system for spraying pesticides. Open Electr Electron Eng J 8: 435-444. https://doi.org/10.2174/1874129001408010435

Shibusawa S, 2003. On line real time sensing. IEEE Int Conf on Advanced Intelligent Mechatronics; July. pp: 1006-1066.

Slaughter DC, Giles DK, Downey D, 2008. Autonomous robotic weed control systems: a review. Comput Electron Agric 61: 63-78. https://doi.org/10.1016/j.compag.2007.05.008

Søgaard HT, Lund I, 2007. Application accuracy of a machine vision-controlled robotic micro-dosing system. Biosyst Eng 96: 315-322. https://doi.org/10.1016/j.biosystemseng.2006.11.009

Tarannum N, Rhaman MK, Khan SA, Shakil SR, 2015. A brief overview and systematic approach for using agricultural robot in developing countries. J Mod Sci Tech 3: 88-101.

Torii T, 2000. Research in autonomous agriculture vehicles in Japan. Comput Electron Agric 25: 133-153. https://doi.org/10.1016/S0168-1699(99)00060-5

Weiss U, Biber P, 2011. Plant detection and mapping for agricultural robots using a 3D LIDAR sensor. Robot Auton Syst 59: 265-273. https://doi.org/10.1016/j.robot.2011.02.011

Yaghoubi S, Akbarzadeh NA, Bazargani SS, Bazargani SS, Bamizan M, Asl MI, 2013. Autonomous robots for agricultural tasks and farm assignment and future trends in agro robots. Int J Mech Mechatron Eng 13: 1-6.

Yoon B, Kim S, 2013. Design of Paddy weeding robot. IEEE Int Symp on Robotics; October: 1-2.

Zecha CW, Link J, Claupein W, 2013. Mobile sensor platforms: categorisation and research applications in precision farming. J Sens Sens Syst 2: 51-72. https://doi.org/10.5194/jsss-2-51-2013

Zhang Q, Pierce FJ, 2013. Agricultural automation: fundamentals and practices. CRC Press, London. 411 pp.

Zhang Z, Noguchi N, Ishii K, Yang L, Zhang C, 2013. Development of a robot combine harvester for wheat and paddy harvesting. IFAC Proc 46: 45-48. https://doi.org/10.3182/20130327-3-jp-3017.00013




DOI: 10.5424/sjar/2017151-9573