Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-10T13:41:43.687Z Has data issue: false hasContentIssue false

Intrarow Weed Removal in Broccoli and Transplanted Lettuce with an Intelligent Cultivator

Published online by Cambridge University Press:  20 January 2017

Ran N. Lati*
Affiliation:
University of California, Davis, Department of Plant Sciences, 1636 East Alisal, Salinas, CA 93905
Mark C. Siemens
Affiliation:
Department of Agricultural and Biosystems Engineering, University of Arizona, Yuma Agricultural Center, 6425 8th Street, Yuma, AZ 85364
John S. Rachuy
Affiliation:
University of California, Davis, Department of Plant Sciences, 1636 East Alisal, Salinas, CA 93905
Steven A. Fennimore
Affiliation:
University of California, Davis, Department of Plant Sciences, 1636 East Alisal, Salinas, CA 93905
*
Corresponding author's E-mail: ranlati@gmail.com

Abstract

The performance of the Robovator (F. Poulsen Engineering ApS, Hvals⊘, Denmark), a commercial robotic intrarow cultivator, was evaluated in direct-seeded broccoli and transplanted lettuce during 2014 and 2015 in Salinas, CA, and Yuma, AZ. The main objective was to evaluate the crop stand after cultivation, crop yield, and weed control efficacy of the Robovator compared with a standard cultivator. A second objective was to compare hand weeding time after cultivation within a complete integrated weed management (IWM) system. Herbicides were included as a component of the IWM system. The Robovator did not reduce crop stand or marketable yield compared with the standard cultivator. The Robovator removed 18 to 41% more weeds at moderate to high weed densities and reduced hand-weeding times by 20 to 45% compared with the standard cultivator. At low weed densities there was little difference between the cultivators in terms of weed control and hand-weeding times. The lower-hand weeding time with the Robovator treatments suggest that robotic intrarow cultivators can reduce dependency on hand weeding compared with standard cultivators. Technological advancements and price reductions of these types of machines will likely improve their weed removal efficacy and the long-term viability of IWM programs that will use them.

El desempeño del Robovator (F. Poulsen Engineering ApS, Hvals⊘, Denmark), un cultivador robótico comercial para uso dentro de las hileras de siembra, fue evaluado en brócoli de siembra directa y lechuga trasplantada durante 2014 y 2015 en Salinas, California y Yuma, Arizona. El objetivo principal fue evaluar el cultivo establecido después de la labranza, el rendimiento del cultivo, y la eficacia para el control de malezas del Robovator, al compararse con un cultivador estándar. Un segundo objetivo fue comparar el tiempo de deshierba manual después de la labranza dentro de un sistema de manejo integrado de malezas (IWM) completo. Se incluyó herbicidas como un componente del sistema IWM. El Robovator no redujo el número de plantas del cultivo establecidas ni el rendimiento comercializable al compararse con el cultivador estándar. El Robovator eliminó 18 a 41% más malezas en densidades de moderadas a altas y redujo el tiempo de deshierba manual en 30 a 45% al compararse con el cultivador estándar. A bajas densidades hubo pocas diferencias entre los cultivadores en términos de control de malezas y tiempos de deshierba manual. El mejor tiempo de deshierba manual con los tratamientos con Robovator sugiere que cultivadores robóticos para uso dentro de las hileras de siembra pueden reducir la dependencia en la deshierba manual en comparación con cultivadores estándar. Los avances tecnológicos y las reducciones en precio de este tipo de máquinas probablemente mejorará la eficacia en la remoción de malezas y la viabilidad en el largo plazo de los programas IWM que los usen.

Type
Research Article
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Associate Editor for this paper: Bradley Hanson, University of California, Davis.

References

Literature Cited

Anonymous (2015a) Dacthal W-75 label. Amvac 11365-4. Los Angeles, CA: Amvac. http://www.cdms.net/ldat/ld4HM005.pdf. Accessed February 2, 2016Google Scholar
Anonymous (2015b) Kerb 50WP label. Dow AgroSciences D02-166-005. Indianapolis, IN: Dow AgroSciences. https://s3-us-west-1.amazonaws.com/www.agrian.com/pdfs/Kerb_50_WP_Label4a.pdf. Accessed February 2, 2016Google Scholar
Bell, CE (1995) Broccoli (Brassica oleracea var. botrytis) yield loss from Italian ryegrass (Lolium perenne) interference. Weed Sci 43: 117120 CrossRefGoogle Scholar
Bruggeman, AC, Mostaghimi, S, Holtzman, GI, Shanholtz, VO, Shukla, S, Ross, BB (1995) Monitoring pesticides and nitrate in Virginia's groundwater—a pilot study. Trans ASAE (Am Soc Agric Eng) 38: 797807 CrossRefGoogle Scholar
Cloutier, DC, Van der Weide, RY, Peruzzi, A, Leblanc, M (2007) Mechanical weed management. Pages 111134 in Upadhyaya, MK, Blackshaw, RE, eds. Non-Chemical Weed Management: Principles, Concepts and Technology. Oxon, UK: CABI CrossRefGoogle Scholar
Fennimore, SA, Doohan, DJ (2008) The challenges of specialty crop weed control, future directions. Weed Technol 22: 364372 Google Scholar
Fennimore, SA, Hanson, BD, Sosnoskie, LM, Samtani, JB, Datta, A, Knezevic, SZ, Siemens, MC (2013) Field applications of automated weed control: Western Hemisphere. Pages 151169 in Young, SL, Pierce, FJ, eds. Automation: The Future of Weed Control in Cropping Systems. Dordrect, The Netherlands: Springer Google Scholar
Fennimore, SA, Smith, RF, Tourte, L, LeStrange, M, Rachuy, JS (2014) Evaluation and economics of a rotating cultivator in bok choy, celery, lettuce and radicchio. Weed Technol 28: 176188 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: 3338 CrossRefGoogle Scholar
Gast, R (2008) Industry views of minor crop weed control. Weed Technol 22: 385388 CrossRefGoogle Scholar
Goodhue, RE, Martin, P (2014) Labor, water and California agriculture in 2014. Agric Resour Econ Update 17 (4): 58. http://giannini.ucop.edu/media/are-update/files/issues/V17N4_1.pdf. Accessed January 22, 2016Google Scholar
Hofstee, JW, Nieuwenhuizen, AT (2013) Field applications of automated weed control: northwest Europe. Pages 171188 in Young, SL, Pierce, FJ, eds. Automation: The Future of Weed Control in Cropping Systems. The Netherlands: Springer Google Scholar
[MDCH] Michigan Department of Community Health (2003) Health Consultation. Dacthal® Groundwater Contamination: Additional Toxicological Data, Coloma Township, Berrien County, Michigan. http://www.michigan.gov/documents/DacthalDiAcidHealthConsult_71729_7.pdf. Accessed January 19, 2016.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: 18 Google Scholar
Mou, B (2011) Mutations in lettuce improvement. Int J Plant Genomics 2011: 17 Google Scholar
[NASS] National Agricultural Statistic Service (2014) Vegetables: 2012 Summary. http://usda.mannlib.cornell.edu.80/usda/.Google Scholar
Perez-Ruiz, M, Slaughter, DC, Fathallah, FA, Gliever, CJ, Miller, BJ (2014) Co-robotic intra-row weed control system. Biosyst Eng 126: 4555 CrossRefGoogle Scholar
Perez-Ruiz, M, Slaughter, DC, Gliever, CJ, Upadhyaya, SK (2012) Automatic GPS based intra-row weed knife control system for transplanted row crops. Comput Electron Agric 80: 4149 CrossRefGoogle Scholar
Rasmussen, J, Griepentrog, HW, Nielsen, J, Henriksen, CB (2012) Automated intelligent rotor tine cultivation and punch planting to improve the selectivity of mechanical intra-row weed control. Weed Res 52: 327337 Google Scholar
Roberts, HA, Hewson, RT, Ricketts, ME (1977) Weed competition in drilled summer lettuce. Hortic Res 17: 3945 Google Scholar
Shem-Tov, S, Fennimore, SA, Lanini, WT (2006) Weed management in lettuce (Lactuca sativa) with pre-plant irrigation. Weed Technol 20: 10581065 CrossRefGoogle Scholar
Siemens, MC (2014) Robotic weed control. Pages 7680 in Proceedings of the 66th Annual California Weed Science Society. Salinas, CA: California Weed Science Society Google Scholar
Slaughter, DC, Giles, DK, Fennimore, SA, Smith, RF (2008) Multispectral machine vision identification of lettuce and weed seedlings for automated weed control. Weed Technol 22: 378384 CrossRefGoogle Scholar
Smith, R, Klonsky, KM, De Moura, RL (2007) Sample Costs to Produce Iceberg Lettuce. Davis, CA: University of California Cooperative Extension publication LC-CC-09-2. http://coststudyfiles.ucdavis.edu/uploads/cs_public/92/af/92af15bd-a003-4e2e-a796-fc33e253edb8/lettuceicecc09.pdf. Accessed January 20, 2016Google Scholar
Smith, RF (2015) Impact of automated thinners on weeds and lettuce production. Page 44 in Proceedings of the 67th Annual California Weed Science Society. Salinas, CA: California Weed Science Society Google Scholar
Smith, RF, Fennimore, SA, LeStrange, M (2007) Lettuce: Integrated Weed Management. http://www.ipm.ucdavis.edu/PMG/r441700111.html. Accessed June 12, 2015Google Scholar
Taylor, JE, Charlton, D, Yunez-Naude, A (2012) The end of farm labor abundance. Appl Econ Perspect Policy 34: 587598 Google Scholar
Tillett, ND, Hague, T, Grundy, AC, Dedousis, AP (2008) Mechanical within-row weed control for transplanted crops using computer vision. Biosyst Eng 99: 171178 Google Scholar
Van der Weide, RY, Bleeker, PO, Achten, VTJM, Lotz, LAP, Fogelberg, F, Melander, B (2008) Innovation in mechanical weed control in crop rows. Weed Res 48: 215224 Google Scholar