Control de maleza mediante la robótica

Juan Espinoza-Hernández, Carlos Juárez-González, Canek Mota-Delfín, Eugenio Romantchik-Kriuchkova

Resumen

Las malezas representan pérdidas potenciales en el rendimiento de los cultivos, se han logrado avances significativos en el desarrollo de sistemas robóticos para el control de malas hierbas y métodos de detección basados en inteligencia artificial. Por ello, en este trabajo se realizó una revisión de bibliografía de los distintos métodos desarrollados y aplicados en sistemas robóticos para la eliminación de malezas, que se dividen en cuatro principios de funcionamiento general; mecánico, químico, térmico y eléctrico, así como una descripción de sus ventajas, inconvenientes y requerimientos para un buen funcionamiento. Entender el panorama de la investigación actual es importante para el futuro desarrollo de nuevas tecnologías. El uso de la visión por computadora ha permitido el desarrollo de herramientas selectivas y la detección de malezas en ambientes complejos. Los sistemas de guiado con GPS o sensores pueden dar autonomía a robots desmalezadores, los cuales con la incorporación de la inteligencia artificial y en especial el aprendizaje profundo, podrían hacerse más robustos y adaptables a diversos cultivos y terrenos a campo abierto, mejorando así el control de malezas.

Palabras clave

detección; cultivo; inteligencia artificial; GPS; sensores

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Referencias

AGRICULTURERS: Conoce a EcoRobotix, Red de especialistas en agricultura, [en línea], Agriculturers, 2018, Disponible en: https://agriculturers.com/conoce-a-ecorobotix/, [Consulta: 5 de marzo de 2021].

AHMAD, M.T.; TANG, L.; STEWARD, B.L.: “Automated mechanical weeding”, En: Automation: The future of weed control in cropping systems, Ed. Springer Netherlands, Stephen L. Young y Francis J. Pierce, primera ed., New Delhi, India, pp. 125-137, 2014, ISBN: 978-94-007-7512-1.

ARAKERI, M.P.; VIJAYA, K.B.P.; BARSAIYA, S.; SAIRAM, H.V.: “Computer vision based robotic weed control system for precision agriculture”, En: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Ed. IEEE, Udupi, India, pp. 1201-1205, 13 de septiembre de 2017, DOI: https://doi.org/10.1109/ICACCI.2017.8126005.

BAILLE, C.; FILLOLS, E.; MCCARTHY, C.; REES, S.; STAIER, T.: “Evaluating commercially available precision weed spraying technology for detecting weeds in sugarcane farming systems”, 2013, Disponible en: https://elibrary.sugarresearch.com.au/handle/11079/14045, [Consulta: 7 de marzo de 2021].

BAKHSHIPOUR, A.; JAFARI, A.; NASSIRI, S.M.; ZARE, D.: “Weed segmentation using texture features extracted from wavelet sub-images”, Biosystems Engineering, 157: 1-12, 2017, ISSN: 1537-5110, DOI: https://doi.org/10.1016/j.biosystemseng.2017.02.002.

BAWDEN, O.; KULK, J.; RUSSELL, R.; MCCOOL, C.; ENGLISH, A.; AMIRA, F.Y.; LEHNERT, C.; PEREZ, T.: “Robot for weed species plant‐specific management”, Journal of Field Robotics, 34(6): 1179-1199, 2017, ISSN: 1556-4959, DOI: https://doi.org/10.1002/rob.21727.

BECHAR, A.; VIGNEAULT, C.: “Agricultural robots for field operations: Concepts and components”, Biosystems Engineering, 149: 94-111, 2016, ISSN: 1537-5110, DOI: - https://doi.org/10.1016/j.biosystemseng.2016.06.014.

BOGUE, R.: “Robots poised to revolutionise agriculture”, Industrial Robot: An International Journal, 43(5): 450-456, 2016, ISSN: 0143-991X, DOI: https://doi.org/10.1016/j.biosystemseng.2016.06.01410.1108/IR-05-2016-0142.

BOND, W.; TURNER, R.; GRUNDY, A.: A review of non-chemical weed management, [en línea], HDRA, the organic Organisation, UK, 81 p., 2003, Disponible en: http://www.organicweeds.org.uk, [Consulta: 8 de marzo de 2021].

BRODIE, G.: “Chapter 3 - The use of physics in weed control”, En: Non-Chemical Weed Control, Ed. Elsevier Academic Press, Khawar Jabran y Bhagirath S. Chauhan, ed., USA, pp. 33-59, 2018, ISBN: 978-0-12-809881-3.

CHRISTENSEN, S.; SØGAARD, H.T.; KUDSK, P.; NØRREMARK, M.; LUND, I.; NADIMI, E.S.; JØRGENSEN, R.: “Site‐specific weed control technologies”, Weed Research, 49(3): 233-241, 2009, ISSN: 0043-1737, DOI: https://doi.org/10.1111/j.1365-3180.2009.00696.x.

CORDIS EUROPA: Sustainable weed management in agriculture with laser-based autonomous tools, [en línea], CORDIS, 2020, Disponible en: https://cordis.europa.eu/project/id/101000256/es, [Consulta: 5 de marzo de 2021].

CSIC COMUNICACIÓN: Un proyecto del CSIC utilizará el láser para eliminar malas hierbas de los cultivos sin necesidad de pesticidas, [en línea], CSIC, 2020, Disponible en: https://www.csic.es/sites/default/files/30diciembre2020_laser_malas_hierbas.pdf, [Consulta: 5 de marzo de 2021].

CUTULLE, M.A.; ARMEL, G.R.; BROSNAN, J.T.; KOPSELL, D.A.; HART, W.E.; VARGAS, J.J.; GIBSON, L.A.; MESSER, R.E.; GONZÁLEZ, A.J.A.; DUNCAN, H.A.: “Evaluation of a cryogenic sprayer using liquid nitrogen and a ballasted roller for weed control”, Journal of Testing and Evaluation, 41(6): 869-874, 2013, ISSN: 0090-3973, DOI: https://doi.org/10.1520/JTE20120296.

DAVENPORT, T.: The future of work now: Digital weeder, [en línea], Forbes, 2020, Disponible en: https://www.forbes.com/sites/tomdavenport/2020/03/21/the-future-of-work-now-digital- weeder/?sh=14b940cc2203, [Consulta: 6 de marzo de 2021].

ECOINVENTOS: El robot capaz de arrancar malas hierbas amenaza de los herbicidas, [en línea], Ecoinventos, 2020, Disponible en: https://ecoinventos.com/odd-bot/, [Consulta: 5 de marzo de 2021].

ECOROBOTIX: The autonomous robot weeder from Ecorobotix, AVO - The autonomous robot weeder from Ecorobotix, [en línea], Ecorobotix, 2019, Disponible en: https://www.ecorobotix.com/en/avo-autonomous- robot-weeder/, [Consulta: 5 de marzo de 2021].

ENGLISH, A.; ROSS, P.; BALL, D.; UPCROFT, B.; CORKE, P.: “Learning crop models for vision-based guidance of agricultural robots”, En: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Ed. IEEE, pp. 1158-1163, 2015, DOI: https://doi.org/10.1109/IROS.2015.7353516, ISBN: 1-4799-9994-6.

FARMDROID: Product | Farmdroid, [en línea], Farmdroid, 2020, Disponible en: https://farmdroid.dk/en/product/, [Consulta: 5 de marzo de 2021].

FARMING-REVOLUTION: Farming revolution GmbH, [en línea], Farming-Revolution, 2020, Disponible en: https://www.farming-revolution.com/, [Consulta: 5 de marzo de 2021].

FARMWISE LABS, I.: FarmWise | Feeding Our World and Our Future, Farmwise.io, [en línea], Farmwise, 2020, Disponible en: https://farmwise.io/, [Consulta: 5 de marzo de 2021].

GAY, P.; ALABANDA, P.; AIMONINO, D.R.; TORTIA, C.: “A high efficacy steam soil disinfestation system, part II: Design and testing”, Biosystems engineering, 107(3): 194-201, 2010, ISSN: 1537-5110, DOI: https://doi.org/10.1016/j.biosystemseng.2010.07.008.

GBOT.AG: Robótica aplicada en la agricultura, GBOT | Fenotipo de alto rendimiento, [en línea], Gbot.AG, 2020, Disponible en: https://gbot.ag/, [Consulta: 5 de marzo de 2021].

GOMES, F.S.J.; LETA, R.F.: “Applications of computer vision techniques in the agriculture and food industry: a review”, European Food Research and Technology, 235(6): 989-1000, 2012, ISSN: 1438-2385, DOI: https://doi.org/10.1007/s00217-012-1844-2.

GUYONNEAU, R.; BELIN, É.; MERCIER, F.; AHMAD, A.; MALAVAZI, F.B.: Autonomous robot for weeding, Inst. System engineering research laboratory of angers University of Angers, Francia, 2 p., 2017.

GUZMÁN, S.L.E.; ACEVEDO, R.M.; GUEVARA, R.A.: “Weed-removal system based on artificial vision and movement planning by A* and RRT techniques”, En: Acta Scientiarum. Agronomy, Agricultural Engineering International: the CIGR, vol. 41, 2019, DOI: https://doi.org/10.4025/actasciagron.v41i1.42687, ISBN: 1682-1130, 9.

HASAN, A.S.M.M.; SOHEL, F.; DIEPEVEEN, D.; LAGA, H.; JONES, M.G.K.: “A survey of deep learning techniques for weed detection from images”, Computers and Electronics in Agriculture, 184, 2021, ISSN: 0168-1699, DOI: https://doi.org/10.1016/j.compag.2021.106067.

IRÍAS, T.A.J.; CASTRO, C.R.: “Algorithm of weed detection in crops by computational vision”, En: 2019 International Conference on Electronics, Communications and Computers (CONIELECOMP), Ed. IEEE, Cholula, Mexico, pp. 124-128, 27 de marzo de 2019, DOI: https://doi.org/10.1109/CONIELECOMP.2019.8673182.

JIANG, G.; ZHAO, C.; SI, Y.: “A machine vision based crop rows detection for agricultural robots”, En: 2010 International Conference on Wavelet Analysis and Pattern Recognition, Ed. IEEE, pp. 114-118, 2010, ISBN: 1-4244-6531-1.

JIMÉNEZ, L.A.F.; CAMARGO, P.D.A.; GARCÍA, R.D.Y.: “Intelligent system for weeds management in pineapple crop with precision agriculture concepts.”, Revista Ciencia y Agricultura, 17(3): 122-136, 2020, ISSN: 0122-8420, DOI: https://doi.org/10.19053/01228420.v17.n3.2020.10830.

JØRGENSEN, R.N.; SØRENSEN, C.G.; PEDERSEN, J.M.; HAVN, I.; OLSEN, H.; SØGAARD, H.: “HORTIBOT: A System Design of a Robotic Tool Carrier for High-Tech Plant Nursing Automation Technology for Off-road Equipment 2006”, En: Proceedings of the 1-2 September 2006 International Conference, Bonn, Germany, Bonn, Germany, 2006.

KANAGASINGHAM, S.; EKPANYAPONG, M.; CHAIHAN, R.: “Integrating machine vision-based row guidance with GPS and compass-based routing to achieve autonomous navigation for a rice field weeding robot”, Precision Agriculture, 21(4): 831-855, 2020, ISSN: 1573-1618, DOI: https://doi.org/10.1007/s11119-019-09697-z.

KORRES, N.E.; BURGOS, N.R.; TRAVLOS, I.; VURRO, M.; GITSOPOULOS, T.K.; VARANASI, V.K.; DUKE, S.O.; KUDSK, P.; BRABHAM, C.; ROUSE, C.E.: “New directions for integrated weed management: Modern technologies, tools and knowledge discovery”, Advances in Agronomy, 155: 243-319, 2019, ISSN: 0065-2113.

KORRESA, N.E.; BURGOSA, N.; TROVLOS, I.; VURRO, M.; GITSOPOULOS, T.; VARANASI, V.; DUKE, S.; ALABANDA, P.; BRABHAM, C.; ROUSE, C.: Chapter Six-New directions for integrated weed management: Modern technologies, tools and knowledge discovery, Ed. Academic Press, Donald L. Sparks, Advances in Agronomy ed., vol. 155, 243-319 p., 2019.

KUSHWAHA, H.L.; SINHA, J.; KHURA, T.; ROGET, D.K.; EKKA, U.; PURUSHOTTAM, M.; SINGH, N.: “Status and scope of robotics in agriculture”, [en línea], En: International Conference on Emerging Technologies in Agricultural and Food Engineering, vol. 12, p. 163, 2016, Disponible en: https://www.researchgate.net/publication/312589560_Status_and_Scope_of_Robotics_in_Agriculture, [Consulta: 8 de marzo de 2021].

LEE, W.S.; SLAUGHTER, D.C.; GILES, D.K.: “Robotic weed control system for tomatoes”, Precision Agriculture, 1(1): 95-113, 1999, ISSN: 1573-1618, DOI: https://doi.org/10.1023/A:1009977903204.

LIU, H.; SUN, H.; LI, M.; IIDA, M.: “Application of color featuring and deep learning in maize plant detection”, Remote Sensing, 12(14): 2229, 2020, ISSN: 2072-4292, DOI: https://doi.org/10.3390/rs12142229.

MARX, C.; BARCIKOWSKI, S.; HUSTEDT, M.; HAFERKAMP, H.; RHAT, T.: “Design and application of a weed damage model for laser-based weed control”, Biosystems Engineering, 113(2): 148-157, 2012, ISSN: 1537-5110, DOI: https://doi.org/10.1016/j.biosystemseng.2012.07.002.

MILIOTO, A.; LOTTES, P.; STACHNISS, C.: “Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNs”, En: 2018 IEEE International Conference on Robotics and Automation (ICRA), Ed. IEEE, Brisbane, QLD, Australia, pp. 2229-2235, 21 de mayo de 2018, DOI: https://doi.org/10.1109/ICRA.2018.8460962.

MISSE, M.P.T.E.; ALMOND, P.; WERNER, A.: Developing automated and autonomous weed control methods on pipfruit orchards in New Zealand, [en línea], 2019, Disponible en: https://www.researchgate.net/publication/333812494_Developing_Automated_and_Autonomous_W eed_Control_Methods_On_Pipfruit_Orchards_In_New_Zealand, [Consulta: 5 de marzo de 2021].

NAÏO TECHNOLOGIES: Multifuncional Vineyard weeding robot – TED, TED, the vineyard weeding robot, [en línea], Naïo Technologies, 2019, Disponible en: https://www.naio-technologies.com/en/agricultural- equipment/vineyard-weeding-robot/, [Consulta: 5 de marzo de 2021].

NØRREMARK, M.; GRIEPENTROG, H.W.; NIELSEN, J.; SØGAARD, H.T.: “The development and assessment of the accuracy of an autonomous GPS-based system for intra-row mechanical weed control in row crops”, Biosystems Engineering, 101(4): 396-410, 2008, ISSN: 1537-5110, DOI: https://doi.org/10.1016/j.biosystemseng.2008.09.007.

OERKE, E.C.: “Crop losses to pests”, The Journal of Agricultural Science, 144(1): 31-43, 2006, ISSN: 0021-8596, DOI: https://doi.org/10.1017/S0021859605005708.

OSORIO, K.; PUERTO, A.; PEDRAZA, C.; JAMAICA, D.; RODRÍGUEZ, L.: “A deep learning approach for weed detection in lettuce crops using multispectral images”, AgriEngineering, 2(3): 471-488, 2020, ISSN: 2624-7402, DOI: https://doi.org/10.3390/agriengineering2030032.

OSTEN, V.; CROOK, N.: A review of technologies that can be enabled by robotics to improve weed control, [en línea], Inst. Cotton Research and Development Corporation, Australian Cotton Farming Systems, Gindie Queensland, Australia, 30 p., 2016, Disponible en: http://www.visionweeding.com/thermal-weeding/, [Consulta: 15 de marzo de 2021].

PAICE, M.E.R.; MILLER, P.C.H.; DAY, W.: “Control requirements for spatially selective herbicide sprayers”, Computers and Electronics in Agriculture, 14(2): 163-177, 1996, ISSN: 0168-1699, DOI: https://doi.org/10.1016/0168-1699(95)00046-1.

PANDEY, P.; NARAYAN, D.H.; YOUNG, S.: A literature review of non-herbicide, robotic weeding: A decade of progress, Inst. Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh NC, USA, 26 p., 2020.

PELTZER, S.C.; HASHEM, A.; OSTEN, V.A.; GUPTA, M.L.; DIGGLE, A.J.; RIETHMULLER, G.P.; DOUGLAS, A.; MOORE, J.M.; KOETZ, E.A.: “Weed management in widerow cropping systems: a review of current practices and risks for Australian farming systems”, Crop and Pasture Science, 60(5): 395-406, 2009, ISSN: 1836-5795, DOI: https://doi.org/10.1071/CP08130.

PÉREZ, R.M.; SLAUGHTER, D.C.; FATHALLAH, F.A.; GLIEVER, C.J.; MILLER, B.J.: “Co- robotic intra-row weed control system¨”, Biosystems Engineering, 126: 45-55, 2014, ISSN: 1537-5110, DOI: https://doi.org/10.1016/j.biosystemseng.2014.07.009.

PINO, O.R.S.E.: “Efecto de la competencia de malezas y la densidad de siembra en el rendimiento del cultivo de algodón (Gossypium hirsutum L.) var. coodetec 405”, Investigación Agraria, 10(2): 21-28, 2013, ISSN: 2305- 0683.

POTATOPRO: Simon’s Steam’r or Steam’r, [en línea], PotatoPro, 2018, Disponible en: https://www.potatopro.com/es/node/98044, [Consulta: 19 de abril de 2021].

POULSEN, F.: Deshierbe térmico, [en línea], VISIONWEEDING, 2021, Disponible en: http://www.visionweeding.com/thermal-weeding/, [Consulta: 5 de marzo de 2021].

RAJAA, R.; SLAUGHTERA, D.C.; FENNIMOREB, S.; SIEMENSC, M.: Precision weed control robot for vegetable fields with high crop and weed densities, Inst. 2019 ASABE Annual International Meeting, Boston, Massachussetss, USA, 4 p., 2019.

RASTI, P.; AHMAD, A.; SAMIEI, S.; BELIN, E.; ROUSSEAU, D.: “Supervised image classification by scattering transform with application to weed detection in culture crops of high density”, Remote Sensing, 11(3), 2019, ISSN: 2072-4292, DOI: https://doi.org/10.3390/rs11030249.

REIMONDO, G.: Robot desmalezador con rayo láser y vapor de agua, ¡Sin agrotóxicos! Tecnología humanizada, [en línea], 2020, Disponible en: https://humanizationoftechnology.com/robot- desmalezador-con-rayo-laser-y-vapor-de-agua-sin-agrotoxicos/revista/2020/volumen-1- 2020/03/2020/, [Consulta: 5 de marzo de 2021].

REISER, D.; SEHSAH, E.S.; BUMANN, O.; MORHARD, J.; GRIEPENTROG, H.W.: “Development of an autonomous electric robot implement for intra-row weeding in vineyards”, Agriculture, 9(1), 2019, ISSN: 2077-0472, DOI: https://doi.org/10.3390/agriculture9010018.

RIZZARDI, K.; PROSTKO, E.; RAINS, G.; VELLIDIS, G.; MORARI, F.: “Selective spraying of weeds in peanut”, En: Sixth European Conference on Precision Agriculture (6ECPA), vol. Poster, Skiathos, Greece, p. 6, 2007.

ROBOVATOR: Automated Mechanical Weeding, [en línea], Robovator, 2018, Disponible en: https://www.robovator.com/, [Consulta: 19 de abril de 2021].

SABANCI, K.; AYDIN, C.: “Smart robotic weed control system for sugar beet”, Journal of Agricultural Science and Technology, 19(1): 73-83, 2017, ISSN: 1680-7073, e-ISSN: 2345-3737.

SLAUGHTER, D.C.; GILES, D.K.; DOWNEY, D.: “Autonomous robotic weed control systems: A review”, Computers and Electronics in Agriculture, 61(1): 63-78, 2008, ISSN: 0168-1699, DOI: https://doi.org/10.1016/j.compag.2007.05.008.

SOHAIL, R.; NAWAZ, Q.; HAMID, I.; GILANI, S.M.M.; MUMTAZ, I.; MATEEN, A.; NAWAZ, J.: “A review on machine vision and image processing techniques for weed detection in agricultural crops”, Pakistan Journal of Agricultural Sciences, 58(1): 187-204, 2021, ISSN: 0552-9034, DOI: https://doi.org/10.21162/PAKJAS/21.305.

SOIL STEAM INTERNATIONAL: Products, SoilSteam International, [en línea], Soil Steam International, 2020, Disponible en: https://soilsteam.com/products/, [Consulta: 5 de marzo de 2021].

STEWARD, B.L.; GAI, J.; TANG, L.: The use of agricultural robots in weed management and control, Ed. Burleigh Dodds, J. Billingsley (Ed.), Robotics and automation for improving agriculture ed., Cambridge, UK, 25 p., 2019, ISBN: 978-1-78676-272-6.

SUTTONAG: Steketee, Steketee ic weeder, [en línea], Suttonag, 2015, Disponible en: https://www.suttonag.com/steketee_ic_weeder.html, [Consulta: 12 de abril de 2021].

TIAN, L.: “Development of a sensor-based precision herbicide application system”, Computers and Electronics in Agriculture, 36(2): 133-149, 2002, ISSN: 0168-1699, DOI: https://doi.org/10.1016/S0168-1699(02)00097-2.

TILLETT, N.D.; HAGUE, T.; DEDOUSIS, A.C.: “Mechanical within-row weed control for transplanted crops using computer vision”, Biosystems Engineering, 99(2): 171-178, 2008, ISSN: 1537-5110, DOI: https://doi.org/10.1016/j.biosystemseng.2007.09.026.

TRIMBLE INC: Sistema automático de pulverización WeedSeeker 2, Trimble Agriculture, [en línea], Trimble Inc, 2019, Disponible en: https://agriculture.trimble.com/product/sistema-automtico-de-pulverizacin- weedseeker-2/?lang=es, [Consulta: 16 de marzo de 2021].

VAN DER WEIDE, R.Y.; BLEEKER, P.O.; ACHTEN, V.T.J.M.; LOTZ, L.A.P.; FOGELBERG, F.; MELANDER, B.: “Innovation in mechanical weed control in crop rows”, Weed Research, 48(3): 215-224, 2008, ISSN: 0043-1737, DOI: https://doi.org/10.1111/j.1365-3180.2008.00629.x.

VOUGIOUKAS, S.G.: “Agricultural robotics. Annual Review of Control”, Robotics, and Autonomous Systems, 2(1): 365–392, 2019, ISSN: 0921-8890, DOI: https://doi.org/10.1146/annurev-control-053018-0236170.

WEED-IT: Precision spraying - weed sprayer, WEED-IT Precision Spraying, [en línea], Weed-It, 2019, Disponible en: https://www.weed-it.com/, [Consulta: 16 de marzo de 2021].

ZASSO: Agriculture–Zasso, Zasso.com, [en línea], Zasso, 2020, Disponible en: https://zasso.com/home/what/agriculture/#, [Consulta: 12 de abril de 2021].

ZHANG, Y.; SSTAAB, E.; SLAUGHTER, C.D.; GILES, K.D.; DOWNEY, C.: “Precision Automated Weed Control Using Hyperspectral Vision Identification and Heated Oil”, En: 2009 ASABE Annual International Meeting, Reno, Nevada, USA, p. 17, 2009.

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