Typically, low-pressure sprayers are used to uniformly apply pre- and post-emergent herbicides to control weeds in crop rows. An innovative machine for weed control in inter-row and intra-row areas, with a unique combination of inter-row cultivation tooling and intra-row band spraying for six rows and an electro-hydraulic side-shift frame controlled by a GPS system, was developed and evaluated. Two weed management strategies were tested in the field trials: broadcast spraying (the conventional method) and band spraying with mechanical weed control using RTK-GPS (the experimental method). This approach enabled the comparison between treatments from the perspective of cost savings and efficacy in weed control for a sugar beet crop. During the 2010–2011 season, the herbicide application rate (112 L ha ？ 1) of the experimental method was approximately 50% of the conventional method, and thus a significant reduction in the operating costs of weed management was achieved. A comparison of the 0.2-trimmed means of weed population post-treatment showed that the treatments achieved similar weed control rates at each weed survey date. Sugar beet yields were similar with both methods ( p = 0.92). The use of the experimental equipment is cost-effective on ≥20 ha of crops. These initial results show good potential for reducing herbicide application in the Spanish beet industry.
References
[1]
Slaughter, D.C.; Giles, D.K.; Fennimore, S.A.; Smith, R.F. Multispectral machine vision identification of lettuce and weed seedlings for automated weed control. Weed Technol. 2008, 22, 378–384.
[2]
Brandes, A. Ertrag und Qualit？t von Zuckerrüben in Abh？ngigkeit von Restverunkrautung und Standort. Ph.D. Theis, Universit？t G？ttingen, G？ttingen, Germany, 2000.
J？rgensen, R.N.; S？rensen, C.G.; Maagaard, J.; Havn, I.; Jensen, J.; S？gaard, H.T.; S？rensen, L.B. HortiBot: A system design of a robotic tool carrier for high-tech plant nursing. CIGR J. Sci. Res. Dev. 2007, IX, 13–21.
[5]
N？rremark, M.; Griepentrog, H.W.; Nielsen, J.; S？gaard, H.T. The development and assessment of the accuracy of an autonomous GPS-based system for intra-row mechanical weed control in row crops. Biosyst. Eng. 2008, 101, 396–410.
[6]
Slaughter, D.C.; Pérez-Ruiz, M.; Gliever, C.; Upadhayaya, S.; Sun, H. Automatic Weed Control System for Processing Tomatoes. Proceedings of the XVIIth World Congress of the International Commission for Agricultural Engineering (CIGR), Québec City, Canada, 13–17 June 2010; pp. 131–132.
[7]
Van Evert, F.K.; Samson, J.; Polder, G.; Vijn, M.; van Dooren, H.; Lamaker, A.; van Der Heijden, G.W.A.M.; van der Zalm, T.; Lotz, L.A. A robot to detect and control broad-leaved dock (Rumex obtusifolius L.) in grassland. J. Field Rob. 2011, 28, 264–277.
[8]
Karimi, D.; Mann, D. Torque feedback on the steering wheel of agricultural vehicles. Comput. Electr. Agric. 2009, 65, 77–84.
[9]
López-Granados, F. Weed detection for site-specific weed management: Mapping and real-time approaches. Weed Res. 2011, 51, 1–11.
[10]
Xue, J.; Zhang, L.; Grift, T.E. Variable field-of-view machine vision based row guidance of an agricultural robot. Comup. Electr. Agric. 2012, 84, 85–91.
[11]
Griepentrog, H.W.; N？rremark, M.; Nielsen, H.; Blackmore, B.S. Individual Plant Care in Cropping Systems. Proceedings of the 4th European Conference on Precision Agriculture, Berlin, Germany, 16–18 June 2003; pp. 247–251.
[12]
Lampkin, N. The Living Soil; Farming Press: Ipswich, UK, 1994. Chapter 2; pp. 13–51.
[13]
Mohler, C.L. Mechanical Management of Weeds. In Ecological Management of Agricultural Weeds; Liebman, M., Mohler, C.L., Staver, C.P., Eds.; Cambridge University Press: Cambridge, UK, 2001; pp. 139–209.
[14]
Melander, B. Optimization of the adjustment of a vertical axis rotary brush weeder for intra-row weed control in row crops. J. Agric. Eng. Res. 1997, 68, 39–50.
[15]
Tillett, N.D.; Hague, T.; Miles, S.J. Inter-row vision guidance for mechanical weed control in sugar beet. Comput. Electr. Agr. 2002, 33, 163–177.
[16]
Kaya, R.; Buzluk, S. Integrated weed control in sugar beet through combinations of tractor hoeing and reduced dosages of a herbicide mixture. Turkish J. Agr. For. 2006, 30, 137–144.
[17]
Tillett, N.D.; Hague, T. Increasing work rate in vision guided precision banded operations. Biosyst. Eng. 2006, 94, 487–494.
[18]
M？rl？nder, B.; Hoffman, C.; Koch, H.-J.; Ladewig, E.; Merkes, R.; Petersen, J.; Stockfisch, N. Environmental situation and yield performance of the sugar beet crop in germany: Heading for sustainable development. J. Agron. Crop Sci. 2003, 189, 201–226.
[19]
Dietsch, A. Rentabilit？t und Umweltvertr？glichkeit der Unkrautregulierung in gentechnisch ver？nderten herbizidtoleranten Zuckerrüben—ein Beitrag zur nachhaltigen Entwicklung des Zuckerrübenanbaus. Ph.D. Thesis, Universit？t G？ttingen, Kinzel, G？ttingen, Germany, 2002.
[20]
Cowan, T.; Alexander, K. Deregulating Genetically Engineered Alfalfa and Sugar Beets: Legal and Administrative Responses. Congressional Research Service, 2012. Available online: http://www.fas.org/sgp/crs/misc/R41395.pdf (accessed on 27 December 2012).
[21]
Wevers, J.D.A. Reduced Environmental Contamination by New Herbicide Formulations. Proceedings of the 60th IIRB Congress, Cambridge, UK, 1–3 July 1997; pp. 169–176.
[22]
Melander, B.; Rasmussen, I.A.; Barberi, P. Integrating physical and cultural methods of weed control: Examples from European research. Weed Sci. 2005, 53, 369–381.
[23]
Griepentrog, H.W.; Norremark, M.; Nielsen, J.; Soriano Ibarra, J. Autonomous Inter-row Hoeing using GPS Based Side-shift Control. Proceedings of Automation Technology for Off-Road, Bonn, Germany, 1–2 September 2006; pp. 117–124.
[24]
Leer, S.; Lowenberg-DeBoer, J. Purdue Study Drives Home Benefits of GPS Auto Guidance. 2004. Available online: http://news.uns.purdue.edu/UNS/html4ever/2004/040413.Lowenberg.gps.html (accessed on 16 May 2011).
[25]
Abidine, A.Z.; Heidman, B.C.; Upadhyaya, S.K.; Hills, D.J. Autoguidance system operated at high speed causes almost no tomato damage. California Agric. 2004, 58, 44–47.
[26]
Leandro, R.F.; Santos, M.C.; Langley, R.B. Analyzing GNSS data in precise point positioning software. GPS Solut. 2011, 30, 1–13.
[27]
Moreno, F.L.; Vaz, R.P.; Fernández, E.B.; Cabrera, F.C. Simulating the composition of the in situ soil solution by the model Expreso: Application to a Reclaimed Marsh Soil of SW Spain irrigated with saline water. Agr. Water Manag. 2004, 66, 113–124.
[28]
Wyse, R. Sucrose uptake by sugar beet tap root tissue. Plant Physiol. 1979, 64, 837–841.
[29]
Taylor, R.K.; Schrock, M.D. Dynamic Testing of GPS Receivers. Paper No. 03–1013; ASABE: St. Joseph, MI, USA, 2003.
[30]
R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria. Available online: http://www.R-project.org/ (accessed on 18 June 2011).
[31]
Rao, V.S. Principles of Weed Science; Science Publisher, Inc.: Endfield, NH, USA, 2000.
[32]
Wilcox, R. Comparing robust nonparametric regression lines via regression depth. J. Statist. Comput. Simul. 2010, 80, 379–387.
[33]
Yuen, K.K. The two-sample trimmed t for unequal population variances. Biometrika 1974, 61, 165–170.
[34]
Romero, C. Evaluación Financiera de Inversiones Agrarias; Mundi-Prensa: Madrid, Spain, 1998.
[35]
Goering, C.E.; Hansen, A.C. Engine and Tractor Power, 4th ed. ed.; ASABE: St. Joseph, MI, USA, 2008.
[36]
Srivastava, A.K.; Goering, C.E.; Rohrbach, R.P.; Buckmaster, D.R. Engineering Principles of Agricultural Machines, 2nd ed. ed.; ASABE: St. Joseph, MI, USA, 2006.
[37]
Agricultural Machinery Management Data. ASABE Standards: ASABE D497.7MAR2011; ASABE: St. Joseph, MI, USA, 2011.
[38]
Ballestero, E. Contabilidad Agraria; Mundi-Prensa: Madrid, Spain, 1996.
[39]
Hembree, K.J. Sugarbeet Integrated Weed Management. In UC Pest Management Guidelines. UC ANR Publication 3469; UC Cooperative Extension: Fresno, CA, USA, 2010.
[40]
Wanternberg, G.; Dammer, K.H. Experiences in developing technologies in site-specific herbicide spraying in real time. J. Plant Diseases Protect. 2002, 18, 443–450.
[41]
Fennimore, S.A.; Tourte, L.J.; Rachuy, J.S.; Smith, R.F.; George, C.A. Evaluation and economics of a machine-vision guided cultivation program in broccoli and lettuce. Weed Technol. 2010, 24, 33–38.
