Precision farming techniques for sustainable weed management

Authors

  • Nihal Dwivedi Division of Crop Production and Protection, CSIR- Central Institute of Medicinal and Aromatic Plants, Lucknow-226 015, India
  • Dipender Kumar CSIR-Central Institute of Medicinal and Aromatic Plants, Research Centre, Pantnagar, Uttarakhand, 263149, India
  • Priyanka Suryavanshi Division of Crop Production and Protection, CSIR- Central Institute of Medicinal and Aromatic Plants, Lucknow-226 015, India

DOI:

https://doi.org/10.31783/elsr.2022.82142149

Keywords:

aerial sensors, crop yield loss, ground-based sensors, precision agriculture, weed management

Abstract

Weed management in modern agriculture is crucial to avoid yield losses and ensure food security. Climate change, intensive agricultural practices, and natural disasters change weed dynamics, requiring changes in weed management strategies. In addition to labor shortages, manual and chemical control options are no longer viable because of weed resistance to herbicides and the effects of eco-degradation and health hazards. As a result, weed management strategies that boost agricultural productivity are urgently needed. Precision agriculture has become one alternative for managing weeds, using tools and technologies to boost farm productivity. Recent innovations in precision application technology have made it possible to make smaller treatment units that can be applied to meet site-specific demands. These systems combine ground-based and aerial weed sensing systems (that are site-specific, need-specific, and cost-effective) with integrated weed management. Despite the viability of all of these strategies in today's agriculture, site-specific selections and the appropriate combination of these eco-friendly strategies can efficiently reduce herbicide use, and ensure environmental protection while enhancing weed control efficiency and crop yield.

References

[1] J. D. Smith, T. Dubois, R. Mallogo, E. F. Njau, S. Tua and R. Srinivasan (2019). Host range of the invasive tomato pesttutaabsolutameyrick (Lepidoptera: Gelechiidae) on solanaceous crops and weeds in Tanzania. Fla. Entomol., 101: 573-579.

[2] P. Roshan, A. Kulshreshtha and V. Hallan (2019). Global Weed-Infecting Geminiviruses. In: Kumar, R. (eds) Geminiviruses. Springer, Cham. doi: 10.1007/978-3-030-18248-9_6.

[3] R. Srinivasan, F. A. Cervantes and J. M. Alvarez (2013). Aphid-borne virus dynamics in the potato-weed pathosystem. Insect Pests Potato.

[4] B. S. Chauhan, G. S. Gill and C. Preston (2006). Tillage system effects on weed ecology, herbicide activity and persistence: a review. Aust. J. Exp. Agric., 46: 1557-1570.

[5] B. S. Chauhan and G. S. Gill (2014). Ecologically based weed management strategies. In Recent Advances in Weed Management, eds B. S. Chauhan and G. Mahajan. New York: Springer, pp1-11. doi: 10.1007/978-1-4939-1019-9_1.

[6] S. Christensen, H.T. Søgaard, P. Kudsk, M. Nørremark, I. Lund, E.S. Nadimi and R. Jørgensen (2009). Site-specific weed control technologies. Weed Res., 49: 233-241.

[7] G. Guccione and G. Schifani (2001). Technological innovation, agricultural mechanization and the impact on the environment: Sod seeding and minimum tillage. Prospettive e propostemediterranee. J. Econ. Agric. Environ., 3: 29-36.

[8] P. Ribas and A. Matsumura (2009). A química dos agrotóxicos: Impactosobre a saúde e meio ambiente. The chemistry of pesticides: Impact on health and the environment. Rev. Lib., 10: 149-158.

[9] S. L. Young, F. J. Pierce (2014). Automation: The Future of Weed Control in Cropping Systems; Springer: Berlin/Heidelberg, Germany; pp249-259.

[10] E. J. P. Marshall (1988). Field-scale estimates of grass populations in arable land. Weed Res., 28: 191-198.

[11] L. J. Wiles, G. W. Oliver, A. C. York, H. J. Gold and G. G. Wilkerson (1992). Spatial distribution of broadleaf weeds in North Carolina soybean (Glycine max) fields. Weed Sci., 40: 554-557.

[12] T. W. Berge, S. Goldberg, K. Kaspersen and J. Netland (2012). Towards machine vision based site-specific weed management in cereals. Comput. Electron. Agric., 81: 79-86.

[13] R. Gerhards and H. Oebel (2006). Practical experiences with a system for site specific weed control in arable crops using real-time image analysis and GPS-controlled patch spraying. Weed Res., 46: 185-193.

[14] L. J. Wiles (2009). Beyond patch spraying: site-specific weed management with several herbicides. Precision Agric., 10: 277-290.

[15] D. Reiser, E. S. Sehsah, O. Bumann, J. Morhard and H. W. Griepentrog (2019). Development of an autonomous electric robot implement for intra-row weeding in vineyards. Agriculture, 9: 18. doi: 10.3390/agriculture9010018.

[16] R. Mink, A. Dutta, G. Peteinatos, M. Sökefeld, J. Engels, M. Hahn and R. Gerhards (2018). Multi-temporal site-specific weed control of Cirsium arvense (L.) Scop and Rumex crispus L. in maize and sugar beet using unmanned aerial vehicle based mapping. Agriculture, 8: 65. doi: 10.3390/agriculture8050065.

[17] J. Rasmussen, J. Nielsen, F. Garcia-Ruiz, S. Christensen and J. C. Streibig (2013). Potential uses of small unmanned aircraft systems (UAS) in weed research. Weed Res., 53: 242-248.

[18] J. P. Pohl, D. Rautmann, H. Nordmeyer, D. Hoersten (2019). Direkteinspeisungim Präzisionspflanzenschutz Teilflächenspezifische application von Pflanzenschutzmitteln (Direct Injection for site-specific application of pesticides). Gesunde Pflanzen, 71: S51-S55.

[19] T. Ruigrok, E. van Henten, J. Booij, K. van Boheemen, and G. Kootstra (2020). Application-specific evaluation of a weed-detection algorithm for plant-specific spraying. Sensors, 20: 7262. doi: 10.3390/s20247262.

[20] D. C. Slaughter, W. T. Lanini and D. K. Giles (2004). Discriminating weeds from processing tomato plants using visible and near-infrared spectroscopy. Trans. ASAE., 47: 1907-1911.

[21] T. Borregaard, H. Nielsen, L. Norgaard and H. Have (2000). Crop–weed discrimination by line imaging spectroscopy. J. Agric. Eng. Res., 75: 389-400.

[22] F. Feyaerts and L. van Gool (2001). Multi-spectral vision system for weed detection. Pattern Recognit. Lett., 22: 667-674.

[23] C. Koger, D. Shaw, C. Watson and K. Reddy (2003). Detecting Late Season Weed infestations in Soybean (Glycine max). Weed Technol., 17: 696-704. doi: 10.1614/WT02-122.

[24] W. S. Lee (1998). Robotic weed control system for tomatoes. Ph.D. dissertation, University of California, Davis.

[25] M. Pflanz, H. Nordmeyer and M. Schirrmann (2018). Weed mapping with UAS imagery and a bag of visual words based image classifier. Remote Sens., 10: 1530. doi: 10.3390/rs10101530.

[26] L. Tian, D. C. Slaughter and R. F. Norris (1997). Outdoor field machine vision identification of tomato seedlings for automated weed control. Trans. ASAE, 40: 1761-1768.

[27] W. S. Lee, D. C. Slaughter and D. K. Giles (1999). Robotic weed control system for tomatoes. Precision Agric. 1: 95-113.

[28] B. Astrand and A. J. Baerveldt (2002). An agricultural mobile robot with vision-based perception for mechanical weed control. Auton. Robots, 13: 21-35.

[29] R. Raja, T. T. Nguyen, D. C. Slaughter and S. A. Fennimore (2020). Real-time weed-crop classification and localisation technique for robotic weed control in lettuce, Biosyst. Eng., 192: 257-274.

[30] S. Christensen, T. Heisel, A. Walter and E. Graglia (2003). A decision algorithm for patch spraying.Weed Res., 43: 276-284.

[31] P. Hamouz, K. Hamouzová, J. Holec and L. Tyšer (2013). Impact of site-specific weed management on herbicide savings and winter wheat yield. Plant, Soil and Environ., 59: 101-107.

[32] C. Ritter, D. Dicke, M. Weis, H. Oebel, H.P. Piepho, A. Büchse and R. Gerhards (2008). An on-farm approach to quantify yield variation and to derive decision rules for site-specific weed management. Precision Agric., 9: 133-146.

[33] D.K., Giles, D. Downey, D.C. Slaughter, J.C. Brevis-Acuna, W.T. Lanini (2004). Herbicide micro-dosing for weed control in field grown processing tomatoes. Appl. Eng. Agric., 20: 735-743.

[34] J. Machleb, G.G. Peteinatos, M. Sökefeld and R. Gerhards (2021). Sensor-based intrarow mechanical weed control in sugar beets with motorized finger weeders. Agronomy, 11: 1517. doi: 10.3390/agronomy11081517.

[35] J. E. Hunter, T. W. Gannon, R. J. Richardson, F. H. Yelverton and R. G. Leon (2020). Integration of remote‐weed mapping and an autonomous spraying unmanned aerial vehicle for site‐specific weed management. Pest Manag. Sci., 76: 1386-1392.

[36] R. D. Lamm, D. C. Slaughter and D. K. Giles (2002). Precision weed control system for cotton. Trans. ASAE., 45: 231-238.

[37] B. Astrand and A. J. Baerveldt (2002). An agricultural mobile robot with vision-based perception for mechanical weed control. Auton. Robots, 13: 21-35.

[38] B. Astrand and A. J. Baerveldt (2004). Plant recognition and localization using context information and individual plant features. In: Proc. of the IEEE Conf. Mechatronics and Robotics 2004-special session Autonomous Machines in Agriculture, Aachen, Germany, September 13-15, pp1191-1196.

[39] B. Astrand and A.J. Baerveldt (2005). Paper IV-Plant recognition and localization using context information and individual plant features. In: Vision Based Perception for Mechatronic Weed Control. Astrand B. Doctor of Philosophy Thesis, Chalmers University of Technology and Halmstad University, Sweden.

[40] J. Blasco, N. Aleixos, J. M. Roger, G. Rabatel and E. Molto (2002). Robotic weed control using machine vision. Biosyst. Eng., 83: 149-157.

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Published

2022-10-28

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Articles

How to Cite

Precision farming techniques for sustainable weed management. (2022). Emergent Life Sciences Research, 142-149. https://doi.org/10.31783/elsr.2022.82142149