Plant Protect. Sci., 2025, 61(2):95-109 | DOI: 10.17221/76/2024-PPS

Advancements in sensor-based weed management: Navigating the future of weed controlReview

Santhappan Vignesh1, Palanisamy Murali Arthanari ORCID...1, Rengabashyam Kalpana1, Ranganathan Umarani2, Subburamu Karthikeyan3, Ponnusamy Janaki4
1 Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
2 Department of Seed Science and Technology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
3 Department of Agricultural Microbiology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
4 Department of Soil Science & Agricultural Chemistry, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India

Controlling weed populations in agricultural land is challenging due to various factors, such as soil conditions, crop type, and environmental conditions. Substantial experience is needed to develop a strategy for minimising pressure from weed infestation. For a relatively longer period, weed control was taken care of using herbicides and mechanical and manual weeding. While herbicides simplify weed control, they pose issues like residual effects and the development of herbicide resistance in weeds, necessitating the deployment of alternate smart weed-management technologies. Lately, smart weeding robots and sensor-based site-specific spraying systems have been developed. Sensors as varied as hyperspectral imaging cameras, Global Navigation Satellite System (GNSS), Real Time Kinematics-Global Positioning System (RTK-GPS), optoelectronic, fluorescence sensors, laser and ultrasonic systems can help to improve weed control efficacy when combined with mechanical and spraying robotic systems. Camera-steered mechanical weeding robots and unmanned aerial vehicles are now widely available for weed management. This review focuses on the developments in sensor-based mechanical and chemical weeding, identification of herbicide-resistant weeds, and herbicide effect assessment. This is a comprehensive overview of studies of sensor-based weed-management strategies being adopted worldwide. Furthermore, an outlook towards future sensor-based weed control strategies and necessary improvements are given.

Keywords: patch spraying; precision weeding; resistant weeds; robotic weeding; sensor technologies; UAV; weed mapping

Received: May 9, 2024; Revised: September 10, 2024; Accepted: September 16, 2024; Prepublished online: November 19, 2024; Published: April 4, 2025  Show citation

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Vignesh S, Murali Arthanari P, Kalpana R, Umarani R, Karthikeyan S, Janaki P. Advancements in sensor-based weed management: Navigating the future of weed control. Plant Protect. Sci. 2025;61(2):95-109. doi: 10.17221/76/2024-PPS.
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    References

    1. Abdulsalam M., Ahiska K., Aouf N. (2023): A novel UAV-integrated deep network detection and relative position estimation approach for weeds. In: Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. Go to original source...
    2. Andújar D., Escolà A., Dorado J., Fernández-Quintanilla C. (2011): Weed discrimination using ultrasonic sensors. Weed Research, 51: 543-547. Go to original source...
    3. Andújar D., Weis M., Gerhards R. (2012a): An ultrasonic system for weed detection in cereal crops. Sensors, 12: 17343-17357. Go to original source... Go to PubMed...
    4. Andújar D., Weis M., Gerhards R. (2012b): Ultrasonic measurements for weed detection in cereal crops. In: Proceedings of the First RHEA International Conference on Robotics and Associated High Technologies and Equipment for Agriculture, September 19-21, 2012, Pisa, Italy: 287-292.
    5. Andújar D., Escolà A., Rosell-Polo J. R., Fernández-Quintanilla C., Dorado, J. (2013): Potential of a terrestrial LiDAR-based system to characterise weed vegetation in maise crops. Computers and Electronics in Agriculture, 92: 11-15. Go to original source...
    6. Asner G.P., Knox R.G., Green R.O., Ungar S.G. (2005): The Flora mission for ecosystem composition, disturbance and productivity. Technical Report Document 20050180313. Center for Aerospace Information, NASA, Hanover, MD. Available at: https://core.ac.uk/outputs/10513659/?source=oai
    7. Baerveldt A., Åstrand B. (2002): An agricultural mobile robot with vision-based perception for mechanical weed control. Autonomous Robots, 13: 21-35. Go to original source...
    8. Bakker T., Van Asselt K., Bontsema J., Müller J., Straten G. (2010a): Systematic design of an autonomous platform for robotic weeding. Journal of Terramechanics, 47: 63-73. Go to original source...
    9. Bakker T., Van Asselt K., Bontsema J., Van Henten E.J. (2010b): Robotic weeding of a maise field based on navigation data of the tractor that performed the seeding. IFAC Proceedings Volumes, 43: 157-159. Go to original source...
    10. Bakker T., Van Asselt K., Bontsema J., Müller J., Van Straten G. (2010c): A path following algorithm for mobile robots. Autonomous Robots, 29: 85-97. Go to original source...
    11. Barba P., Sawicka B., Marczak B.K., Pszczółkowski P. (2020): Effect of mechanical and herbicide treatments on weed densities and biomass in two potato cultivars. Agriculture, 10: 455. Go to original source...
    12. Bawden O., Kulk J., Russell R., McCool C., English A., Dayoub F. (2017): Robot for weed species plant-specific management. Journal of Field Robotics, 34: 1179-1199. Go to original source...
    13. Burks T.F., Shearer S.A., Green J.D., Heath J.R. (2002): Influence of weed maturity levels on species classification using machine vision. Weed Science, 50: 802-811. Go to original source...
    14. Busi R., Vila-Aiub M.M., Beckie H.J., Gaines T.A., Goggin D.E., Kaundun S.S., Lacoste M., Neve P., et al. (2013): Herbicide-resistant weeds: From research and knowledge to future needs. Evolutionary Applications, 6: 1218-1221. Go to original source... Go to PubMed...
    15. Carré-Made for Agriculture (2020): Available at: https://www.carre.fr/en/carre-produit/anatis/ (accessed February 21, 2024).
    16. Cerovic Z.G., Samson G., Morales F., Tremblay N., Moya I. (1999): Ultraviolet-induced fluorescence for plant monitoring: present state and prospects. Agronomie, 19: 543-578. Go to original source...
    17. Chandel A.K., Kumar S.P., Tewari V.K., Mehta1 C.R., Nare B., Chethan C.R. (2019): Sonar sensor based intra row weeding prototype for row crops. World Journal of Agriculture & Soil Science, 3: 1-6.
    18. Conesa-Muñoz J., Gonzalez-de-Soto M., Gonzalez-de-Santos P., Ribeiro A. (2015): Distributed multi-level supervision to effectively monitor the operations of a fleet of autonomous vehicles in agricultural tasks. Sensors, 15: 5402-5428. Go to original source... Go to PubMed...
    19. Dammer K., Wartenberg G. (2007): Sensor-based 20. Crop Protection, 26: 270-277. Go to original source...
    20. Dille J.A., Mortensen D.A., Young L.J. (2002): Predicting weed species occurrence based on site properties and previous year's weed presence. Precision Agriculture, 3: 193-207. Go to original source...
    21. Ehlert D., Adame K.R., Horn H.J. (2009): Laser range finder-based measuring of crop biomass under field conditions. Precision Agriculture, 10: 395-408. Go to original source...
    22. Eide A., Koparan C., Zhang Y., Ostlie M., Howatt K., Sun X. (2021): UAV-assisted thermal infrared and multispectral imaging of weed canopies for glyphosate resistance detection. Remote Sensing, 13: 4606. Go to original source...
    23. 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. Scientific World Journal, 2014: 404059. Go to original source... Go to PubMed...
    24. Energreen (2024): Available at: https://en.energreen.it/green-maintenance-machines-energreen/robo-remote-controlled-tool-carriers/ (accessed February 27, 2024) Go to original source...
    25. FarmDroid FD20. (2020): Available at: https://www.farmdroid.co.uk/products/farmdroid-fd20/ (accessed February 21, 2024).
    26. FarmWise (2020): Available at: https://farmwise.io/ (accessed February 20, 2024).
    27. Fennimore S.A., Smith R.F., Tourte L., LeStrange M. (2014): Evaluation and economics of a rotating cultivator in bok choy, celery, lettuce, and radicchio. Weed Technology, 28: 176-188. Go to original source...
    28. Fernández-Quintanilla C., Peña J. M., Andújar D., Dorado J., Ribeiro A., López-Granados F., Smith R. (2018): Is the current state of the art of weed monitoring suitable for site-specific weed management in arable crops? Weed Research, 58: 259-272. Go to original source...
    29. Franz E., Gebhardt E., Unklesbay K.B. (1991): Shape description of completely-visible and partially-occluded leaves for identifying plants in digital images. Transactions of the American Society of Agricultural Engineers, 34: 673-681. Go to original source...
    30. Gerhards R., Risser P., Spaeth M., Saile M., Peteinatos G. (2023): A comparison of seven innovative robotic weeding systems and reference herbicide strategies in sugar beet (Beta vulgaris subsp. vulgaris L.) and rapeseed (Brassica napus L.). Weed Research, 1-12. Go to original source...
    31. Gitelson A.A., Merzlyk M.N. (1997): Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18: 2691-2697. Go to original source...
    32. Gonzalez-de-Santos P., Ribeiro A., Fernández-Quintanilla C., Lopez-Granados F., Brandstoetter M., Tomic S., Pedrazzi S. (2017): Fleets of robots for environmentally-safe pest control in agriculture. Precision Agriculture, 18: 574-614. Go to original source...
    33. Granitto P.M., Verdes P.F., Ceccatto H.A. (2005): Large-scale investigation of weed seed identification by machine vision. Computers and Electronics in Agriculture, 47:15-24. Go to original source...
    34. Hasan M., Ahmad-Hamdani M.S., Rosli A.M., Hamdan H. (2021): Bioherbicides: An eco-friendly tool for sustainable weed management. Plants, 10: 1212. Go to original source... Go to PubMed...
    35. Heap I.M. (2024): The international survey of herbicide resistant weeds. Available at: www.weedscience.org (accessed February 14, 2024).
    36. Home M.C.W., Tillett N.D., Hague T., Godwin R.J. (2002): An experimental study of lateral positional accuracy achieved during inter-row cultivation. In: Proceedings of the 5th EWRS Workshop on Physical and Cultural Weed Control, March 11-13, 2002, Pisa, Italy: 101-110.
    37. Huang Y., Reddy K.N., Fletcher R.S., Pennington D. (2017): UAV low-altitude remote sensing for precision weed management. Weed Technology, 32: 2-6. Go to original source...
    38. Huang Y., Zhao X., Pan Z., Reddy K., Zhang J. (2022): Hyperspectral plant sensing for differentiating glyphosate-resistant and glyphosate-susceptible johnsongrass through machine learning algorithms. Pest Management Science. Go to original source... Go to PubMed...
    39. Krause G.H., Weis E. (1991): Chlorophyll fluorescence and photosynthesis: the basics. Annual Review Plant Physiology and Plant Molecular Biology, 42: 313-349. Go to original source...
    40. Kunz C., Weber J., Gerhards R. (2015): Benefits of precision farming technologies for mechanical weed control in soybean and sugar beet - Comparison of precision hoeing with conventional mechanical weed control. Agronomy, 5: 130-142. Go to original source...
    41. Kunz C., Weber J.F., Gerhards R. (2016): Comparison of different mechanical weed control strategies in sugar beets. Julius-Kühn-Archiv, 446.
    42. Kunz C., Weber J.F., Peteinatos G.G., Sökefeld M., Gerhards R. (2018): Camera-steered mechanical weed control in sugar beet, maise and soybean. Precision Agriculture, 19: 708-720. Go to original source...
    43. Lati R.N., Siemens M.C., Rachuy J.S., Fennimore S.A. (2016): Intra-row weed removal in broccoli and transplanted lettuce with an intelligent cultivator. Weed Technology, 30: 655-663. Go to original source...
    44. Loghavi M., Mackvandi B. (2008): Development of a target oriented weed control system. Computers and Electronics in Agriculture, 63: 112-118. Go to original source...
    45. López-Granados F., Torres-Sánchez J., Serrano-Pérez A., de Castro A.I., Mesas-Carrascosa F.J., Pena J.M. (2016): Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds. Precision Agriculture, 17: 183-199. Go to original source...
    46. Machleb J., Peteinatos G., Kollenda B., Andújar D., Gerhards R. (2020): Sensor-based mechanical weed control: Present state and prospects. Computers and Electronics in Agriculture, 176: 105638. Go to original source...
    47. Mahé I., Cordeau S., Bohan D.A., Derrouch D., Dessaint F., Millot D., Chauvel B. (2020): Soil seedbank: Old methods for new challenges in agroecology? Annals of Applied Biology, 178: 23-38. Go to original source...
    48. Martin D., Singh V., Latheef M.A., Bagavathiannan M. (2020): Spray deposition on weeds (Palmer Amaranth and Morningglory) from a remotely piloted aerial application system and backpack sprayer. Drones, 4: 59. Go to original source...
    49. Melander B., Rasmussen I.A., Bàrberi P. (2006): Integrating physical and cultural methods of weed control - Examples from European research. Weed Science, 53: 369-381. Go to original source...
    50. Melander B., Lattanzi B., Pannacci E. (2015): Intelligent versus non-intelligent mechanical intra-row weed control in transplanted onion and cabbage. Crop Protection, 72: 1-8. Go to original source...
    51. Moshou D., Ramon H., De Baerdemaeker J. (2002): A weed species spectral detector based on neural networks. Precision Agriculture, 3: 209-223. Go to original source...
    52. Naïo Technologies. (2024): Autonomous weeding, agricultural robots. Available at: https://www.naio-technologies.com/en/home/ (accessed on February 20, 2024).
    53. Noble S.D., Brown R.B., Crowe T.G. (2012): Design and evaluation of an imaging spectrophotometer incorporating a uniform light source. Review of Scientific Instruments, 83: 9. Go to original source... Go to PubMed...
    54. Noguchi N., Will J., Reid J., Zhang Q. (2004): Development of a master - slave robot system for farm operations. Computers and Electronics in Agriculture, 44: 1-19. Go to original source...
    55. Nørremark M., Griepentrog H.W., Nielsen J., Søgaard H.T. (2008): The development and assessment of an autonomous GPS-based system for intra-row mechanical weed control in row crops. Biosystems Engineering, 101: 396-410. Go to original source...
    56. Nørremark M., Griepentrog H.W., Nielsen J., Søgaard H.T. (2012): Evaluation of an autonomous GPS-based system for intra-row weed control by assessing the tilled area. Precision Agriculture, 13: 149-162. Go to original source...
    57. Oerke E.C. (2006): Crop losses to pests. Journal of Agricultural Science, 144: 31-43. Go to original source...
    58. ONE SMART SPRAY (2023): Available at: https://www.onesmartspray.com/ (accessed on April 20, 2024).
    59. Pastrana J. (2012). Active shape models with focus on overlapping problems applied to plant detection and soil pore analysis [PhD thesis]. Leibniz Universität, Hannover, Germany. Available at: http://www.bgthannover.de/homepagethesis/pastrana_phd_2012.pdf (accessed on February 9, 2024)
    60. Paul R.A.I., Arthanari P.M., Pazhanivelan S., Kavitha R., Djanaguiraman M. (2023a): Drone-based herbicide application for energy saving, higher weed control and economics in direct-seeded rice (Oryza sativa). The Indian Journal of Agricultural Sciences, 93: 704-709. Go to original source...
    61. Paul R.A.I., Arthanari P.M., Pazhanivelan S., Kavitha R., Djanaguiraman M. (2023b): UAV-based herbicide application for efficient weed management in direct-seeded rice. Agricultural Science Digest, 44: 295-300. Go to original source...
    62. Pérez-Ruiz M., Upadhyaya S. (2012): GNSS in Precision Agricultural Operations. In New Approach of Indoor and Outdoor Localization Systems. España: Intech. Go to original source...
    63. Pérez-Ruiz M., Gonzalez-de-Santos P., Ribeiro A., Fernández-Quintanilla C., Peruzzi A., Vieri M., Agüera J. (2015): Highlights and preliminary results for autonomous crop protection. Computers and Electronics in Agriculture, 110: 150-161. Go to original source...
    64. Persson M., Åstrand B. (2008): Classification of crops and weeds extracted by active shape models. Biosystems Engineering, 100: 484-497. Go to original source...
    65. Peteinatos G., Weis M., Andújar D., Ayala V., Gerhards R. (2013): Potential use of ground-based sensor technologies for weed detection. Pest Management Science, 70: 190-199. Go to original source... Go to PubMed...
    66. Platform PUMAgri - SITIA (2020): Available at: http://www.sitia.fr/en/solution-innovation-en/systems-innovative/platform-pumagri/ (accessed February 20, 2024).
    67. Pusphavalli M., Chandraleka R. (2016): Automatic weed removal system using machine vision. International Journal of advanced Research in Electronics and Communication Engineering, 5: 503-506.
    68. Raja R., Nguyen T., Slaughter D., Fennimore S. (2020): Real-time weed-crop classification and localisation technique for robotic weed control in lettuce. Biosystems Engineering, 192: 257-274. Go to original source...
    69. Reiser D., Vázquez-Arellano M., Paraforos D.S., Marrido-Izard M., Griepentrog H.W. (2018): Iterative individual plant clustering in maise with assembled 2D LiDAR data. Computers in Industry, 99: 42-52. Go to original source...
    70. Rosell J.R., Sanz R. (2012): A review of methods and applications of the geometric characterisation of tree crops in agricultural activities. Computers and Electronics in Agriculture, 81: 124-141. Go to original source...
    71. Rueda-Ayala V., Peteinatos G., Gerhards R., Andújar D. (2015): A non-chemical system for online weed control. Sensors, 15: 7691-7707. Go to original source... Go to PubMed...
    72. Rumpf T., Römer C., Weis M., Sökefeld M., Gerhards R., Plümer L. (2012): Sequential support vector machine classification for small-grain weed species discrimination with special regard to Cirsium arvense and Galium aparine. Computers and Electronics in Agriculture, 80: 89-96. Go to original source...
    73. Saile M., Spaeth M., Gerhards R. (2022): Evaluating sensor based mechanical weeding combined with pre- and post-emergence herbicides for integrated weed management in cereals. Agronomy, 12: 1465. Go to original source...
    74. Slaughter D., Giles D. (2008): Autonomous robotic weed control systems: A review. Computers and Electronics in Agriculture, 61: 63-78. Go to original source...
    75. Sodaeizadeh H., Hosseini Z. (2012): Allelopathy an environmentally friendly method for weed control. In: Proceedings of International Conference on Applied Life Sciences (Rijeka, Croatia: In Tech open).
    76. Sökefeld M., Gerhards R., Oebel H., Therburg R.D. (2007): Image acquisition for weed detection and identification by digital image analysis. In: 6th European Conference on Precision Agriculture (ECPA) [Stafford J.V. (ed.)], Wageningen Academic Publishers, Wageningen: 523-529. Go to original source...
    77. Srivastava S.K., Singh J., Kumar N.R., Singh N.P., Ahmad N. (2020): Changing agricultural labour market and its effects on farm economy in India. Indian Journal of Agricultural Economics, 75: 469-480.
    78. Staab E., Slaughter D., Zhang Y., Giles D. (2009): Hyperspectral imaging system for precision weed control in processing tomato. In: Conference: June 21-24, 2009, Reno, Nevada. 1. St. Joseph, Michigan: American Society of Agricultural and Biological Engineers. Go to original source...
    79. Streibig J., Rasmussen J., Andújar D., Andreasen C., Berge T., Chachalis D., Dittmann T., Gerhards R., et al. (2014): Sensor-based assessment of herbicide effects. Weed Research, 54: 223-233. Go to original source...
    80. Sui R., Thomasson J.A., Hanks J., Wooten J. (2008): Ground-based sensing system for weed mapping in cotton. Computers and Electronics in Agriculture, 60: 31-38. Go to original source...
    81. Swain K.C., Nørremark M., Jørgensen R.N., Midtiby H.S., Green O. (2011): Weed identification using an automated active shape matching (AASM) technique. Biosystems in Engineering, 110: 450-457. Go to original source...
    82. Tertill (2020): Available at: https://tertill.com/ (accessed February 22, 2024).
    83. Tillett N. (1991): Automatic guidance sensors for agricultural field machines: A review. Journal of Agricultural Engineering Research, 50: 167-187. Go to original source...
    84. Tillett N., Hague T., Grundy A. (2008): Mechanical within-row weed control for transplanted crops using computer vision. Biosystems in Engineering, 99: 171-178. Go to original source...
    85. Trimble Agriculture (2024): WeedSeeker 2 automatic spot spray system. Available at: https://agriculture.trimble.com/en/products/hardware/flow-application-control/weedseek)er-2-spot-spray-system (accessed February 27, 2024).
    86. Ustin S.L., DiPietro D., Olmstead K., Underwood E., Scheer G.J. (2002): Hyperspectral remote sensing for invasive species detection and mapping. In: IEEE International Geoscience and Remote Sensing Symposium June 24-28, 2002, Toronto, Canada. Newyork: Institute of Electrical and Electronics Engineers: 1658-1660. Go to original source...
    87. Ustin S.L., Roberts D.A., Gamon J.A., Asner G.P., Green R.O. (2004): Using imaging spectroscopy to study ecosystem processes and properties. BioScience, 54: 523-534. Go to original source...
    88. Vaishnavi P., Manisankar G. (2022): Labour scarcity in agriculture: A review. The Pharma Innovation Journal, 11: 2087-2090.
    89. Van Evert F.K., Samsom J., Polder G., Vijn M., Van Dooren H.J., Lamaker A., Van Der Heijden G.W.A.M., Kempenaar C., et al. (2011): A robot to detect and control broad-leaved dock (Rumex obtusifolius L.) in grassland. Journal of Field Robotics, 28: 264-277. Go to original source...
    90. Wang P., Peteinatos G., Li H., Gerhards R. (2016): Rapid in-season detection of herbicide resistant Alopecurus myosuroides using a mobile fluorescence imaging sensor. Crop Protection, 89: 170-177. Go to original source...
    91. Wang P., Li H., Weber J.F., Gerhards R. (2018): Early identification of herbicide stress in soybean [Glycine max (L.) Merr.] using chlorophyll fluorescence imaging technology. Sensors. 18: 21. Go to original source... Go to PubMed...
    92. Weis M., Gerhards R. (2007): Feature extraction for the identification of weed species in digital images for the purpose of site-specific weed control. In: 6th European Conference on Precision Agriculture (ECPA) [Stafford J.V. (ed.)], Wageningen Academic Publishers: Wageningen: 537-545. Go to original source...
    93. Westwood J.H., Charudattan R., Duke S.O., Fennimore S.A., Marrone P., Slaughter D.C., Swanton C., Zollinger R. (2018): Weed management in 2050: Perspectives on the future of weed science. Weed Science, 66: 275-285. Go to original source...
    94. Zhu D.S., Pan J.Z., He Y. (2008): Identification methods of crop and weeds based on Vis/NIR spectroscopy and RBF-NN model. Guang Pu Xue Yu Guang Pu Fen Xi, 28: 1102-1106.

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