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Beyond Precision Weed Control: A Model for True Integration

Published online by Cambridge University Press:  20 November 2017

Stephen L. Young
Stephen L. Young*
Affiliation:
Adjunct Assistant Professor, Soil and Crop Science Section, and Director, Northeastern IPM Center, Cornell University, Ithaca, NY, USA.
*
Author for correspondence: S. L. Young, Adjunct Assistant Professor, Soil and Crop Science Section, and Director, Northeastern IPM Center, Cornell University, Ithaca, NY, 14853. (E-mail: sly27@cornell.edu)
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Abstract

Precision means being exact and accurate and is an important management component for cropping systems. However, precision does not mean integration, which encompasses spatial and temporal dimensions and is a necessary practice rivaling precision. True IWM merges precision and integration by incorporating advanced technology that allows for greater flexibility of inputs and enhanced responsiveness to field conditions. Examples of this approach are non-existent due to a lack of suitable technological tools and a need for a paradigm shift. Herein a potential model startup company is offered as a guide to advance beyond precision weed control to true integration. The critical components of such a company include grower connections, investor support, proven and reliable technology, and adaptability and innovation in the agricultural technology market. The company with the vision and incentive to make true IWM a reality will be the first to more fully integrate available tools using technology, thus helping many growers overcome ongoing challenges associated with resistance, soil erosion, drift, and weed seedbanks.

Keywords

Krishna Reddy, United States Department of AgricultureAdvanced technologyautomationdecision supporttrue IWMroboticsUAV
Type
Symposium
Information
Weed Technology , Volume 32 , Issue 1 , February 2018 , pp. 7 - 10
Copyright
© Weed Science Society of America, 2017 

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References

Alsever, J (2016). Is there an agtech bubble? Fortune, May 25. http://fortune.com/2016/07/25/agriculture-farming-tech-startup-bubble/. Accessed May 12, 2017.Google Scholar
Beckie, HJ Harker, KN (2017) Our top 10 herbicide-resistant weed management practices. Pest Manag Sci 73, 1045–1052.Google Scholar
Bogue, R (2016) Robots poised to revolutionise agriculture. Industrial Robot: An International Journal 43, 450–456, doi: 10.1108/IR-05-2016-0142 http://dx.doi.org/10.1108/IR-05-2016-0142. Accessed May 12, 2017.CrossRefGoogle Scholar
Chesbrough, H (2003) The era of open innovation. MIT Sloan Management Review 44, 35–41.Google Scholar
Christensen, S, Sogaard, HT, Kudsk, P, Norremark, M, Lund, I, Nadimi, ES Jorgensen, R (2009) Site-specific weed control technologies. Weed Res 49, 233–241.CrossRefGoogle Scholar
Dammer, KH (2016) Real-time variable-rate herbicide application for weed control in carrots. Weed Res 56, 237–246.CrossRefGoogle Scholar
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, 404059, doi: 10.1155/2014/404059 Google Scholar
Etzkowitz, H, Webster, A, Gebhardt, C Terra, BRC (2000) The future of the university and the university of the future: evolution of ivory tower to entrepreneurial paradigm. Research Policy 29, 313–330.Google Scholar
Fennimore, SA, Slaughter, DC, Siemens, MC, Leon, RG Saber, MN (2016) Technology for automation of weed control in specialty crops. Weed Technol 30, 823–837.Google Scholar
Fennimore, SA, Tourte, LJ, Rachuy, JS George, CA (2010) Evaluation and economics of a machine-vision guided cultivation program in broccoli and lettuce. Weed Technol 24, 33–38.CrossRefGoogle Scholar
Gerhards, R Christensen, S (2003) Real-time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley. Weed Res 43, 385–392.Google Scholar
Hamer, J (2016). A vision of new science and technology applications for agriculture: Monsanto growth ventures. National Academy of Sciences Board on Agriculture and Natural Resources (BANR) Meeting on New Technologies for Agriculture Production and Research. http://dels.nas.edu/Upcoming-Event/BANR-Meeting-Technologies/AUTO-5-91-79-R. Accessed May 11, 2017.Google Scholar
Lamm, RD, Slaughter, DC Giles, DK (2002) Precision weed control system for cotton. Trans ASAE 45, 231–238.Google Scholar
Liebman, M Gallandt, ER (1997) Many little hammers: ecological management of crop-weed interactions. In Jackson LE (ed.). Ecology in Agriculture. San Diego: Academic Press. Pp 291–343.Google Scholar
Melander, B, Lattanzi, B Pannacci, E (2015) Intelligent versus non-intelligent mechanical intra-row weed control in transplanted onion and cabbage. Crop Prot 71, 1–8.CrossRefGoogle Scholar
Nieuwenhuizen, AT, Tang, L, Hofstee, JW, Müller, J van Henten, EJ (2007) Colour based detection of volunteer potatoes as weeds in sugar beet fields using machine vision. Precision Ag 8, 267–278.CrossRefGoogle Scholar
Nordmeyer, H (2006) Patchy weed distribution and site-specific weed control in winter cereals. Precision Ag 7, 219–231.Google Scholar
Norsworthy, JK, Ward, SM, Shaw, DR, Llewellyn, RS, Nichols, RL, Webster, TM, Bradley, KW, Frisvold, G, Powles, SB, Burgos, NR, Witt, WW Barrett, M (2012) Reducing the risks of herbicide resistance: best management practices and recommendations. Weed Sci 60, 31–62.Google Scholar
Owen, MK, Beckie, HJ, Leeson, JY, Norsworthy, JK Steckel, LE (2015) Integrated pest management and weed management in the United States and Canada. Pest Manag Sci 71, 357–376.Google Scholar
Patel, N (2015). 90% of startups fail: here’s what you need to know about the 10%. Forbes, January 16. https://www.forbes.com/sites/neilpatel/2015/01/16/90-of-startups-will-fail-heres-what-you-need-to-know-about-the-10/#3f6dc5cf6679. Accessed July 17, 2017.Google Scholar
Singh, K, Agrawal, KN Bora, GC (2011) Advanced techniques for weed and crop identification for site specific weed management. Biosyst Eng 109, 52–64.Google Scholar
Slaughter, DC, Giles, DK Downey, D (2008) Autonomous robotic weed control systems: a review. Computers Elect Ag 61, 63–78.Google Scholar
Tobe, F (2017). 2016 Best Year Ever for Funding Robotics Startup Companies. The Robot Report. https://www.therobotreport.com/news/2016-was-best-year-ever-for-funding-robotics-startup-companies. Accessed May 11, 2017.Google Scholar
Van Evert, FK, Polder, G, Van der Heijden, GA, Kempenaar, C Lotz, LA (2009) Real-time vision-based detection of Rumex obtusifolius in grassland. Weed Res 49, 164–174.CrossRefGoogle Scholar
Vrindts, E Ramon, H (2002) Weed detection using canopy reflection. Precision Ag 3, 63–80.Google Scholar
Young, SL (2012) True integrated weed management. Weed Res 52, 107–111.Google Scholar
Young, SL, Meyer, GE Woldt, WE (2014) Future directions for automated weed management in precision agriculture. In Young SL and FJ Pierce (eds.). Automation: The Future of Weed Control in Cropping Systems. New York: Springer. Pp 249–259.Google Scholar
Young, SL, Pitla, SK, Van Evert, FK, Schueller, JK Pierce, FJ (2017) Moving integrated weed management from low level to a truly integrated and highly specific weed management system using advanced technologies. Weed Res 57, 1–5.Google Scholar
Zhang, Y, Staab, ES, Slaughter, DC, Giles, DK Downey, D (2012) Automated weed control in organic row crops using hyperspectral species identification and thermal micro-dosing. Crop Prot 41, 96–105.CrossRefGoogle Scholar