Development and Evaluation of a Combined Cultivator and Band Sprayer with a Row-Centering RTK-GPS Guidance System

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.

[1]  B. Melander.  Optimization of the Adjustment of a Vertical Axis Rotary Brush Weeder for Intra-Row Weed Control in Row Crops , 1997 .

[2]  K. Yuen,et al.  The two-sample trimmed t for unequal population variances , 1974 .

[3]  Paolo Bàrberi,et al.  Integrating physical and cultural methods of weed control— examples from European research , 2005, Weed Science.

[4]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[5]  Charles L. Mohler,et al.  Ecological Management of Agricultural Weeds: Mechanical management of weeds , 2001 .

[6]  Carol V. Brown,et al.  IS Management Handbook , 2003 .

[7]  Riza Kaya,et al.  Integrated Weed Control in Sugar Beet through Combinations of Tractor Hoeing and Reduced Dosages of a Herbicide Mixture , 2006 .

[8]  N. D. Tillett,et al.  Inter-row vision guidance for mechanical weed control in sugar beet , 2002 .

[9]  Francisca López-Granados,et al.  Weed detection for site-specific weed management: mapping and real-time approaches , 2011 .

[10]  Hans W. Griepentrog,et al.  Autonomous Inter-Row Hoeing using GPS-based side-shift Control , 2007 .

[11]  R. Wyse.  Sucrose uptake by sugar beet tap root tissue. , 1979, Plant physiology.

[12]  Hans W. Griepentrog,et al.  Individual plant care in cropping systems , 2003 .

[13]  S. Fountas,et al.  Agricultural robots—system analysis and economic feasibility , 2006, Precision Agriculture.

[14]  David C. Slaughter,et al.  Multispectral Machine Vision Identification of Lettuce and Weed Seedlings for Automated Weed Control , 2008, Weed Technology.

[15]  Gerrit Polder,et al.  A robot to detect and control broad‐leaved dock (Rumex obtusifolius L.) in grassland , 2011, J. Field Robotics.

[16]  Deregulating Genetically Engineered Alfalfa and Sugar Beets: Legal and Administrative Responses [September 10, 2010] , 2010 .

[17]  E. Balfour.  The Living Soil , 1951 .

[18]  Ajit K. Srivastava,et al.  Engineering Principles of Agricultural Machines , 1993 .

[19]  Francisca López Granados.  Weed detection for site-specific weed management: Mapping and real-time approaches , 2011 .

[20]  Rand R. Wilcox,et al.  Comparing non-parametric regression lines via regression depth , 2010 .

[21]  Randal K. Taylor,et al.  DYNAMIC TESTING OF GPS RECEIVERS , 2004 .

[22]  E. Ladewig,et al.  Environmental Situation and Yield Performance of the Sugar Beet Crop in Germany: Heading for Sustainable Development , 2003 .

[23]  Richard B. Langley,et al.  Analyzing GNSS data in precise point positioning software , 2011 .

[24]  D. C. Slaughter,et al.  Automatic weed control system for processing tomatoes , 2010 .

[25]  Davood Karimi,et al.  Torque feedback on the steering wheel of agricultural vehicles , 2009 .

[26]  Claus G. Sørensen,et al.  HortiBot: A System Design of a Robotic Tool Carrier for High-tech Plant Nursing , 2007 .

[27]  Hans W. Griepentrog,et al.  The development and assessment of the accuracy of an autonomous GPS-based system for intra-row mechanical weed control in row crops , 2008 .

[28]  Tony E Grift,et al.  Variable field-of-view machine vision based row guidance of an agricultural robot , 2012 .

[29]  N. D. Tillett,et al.  Increasing Work Rate in Vision Guided Precision Banded Operations , 2006 .

[30]  V. S. Rao.  Principles of Weed Science , 2000 .

[31]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[32]  Carroll E. Goering,et al.  Engine and tractor power , 1985 .

[33]  F. Moreno,et al.  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 , 2004 .

[34]  Steven A. Fennimore,et al.  Evaluation and Economics of a Machine-Vision Guided Cultivation Program in Broccoli and Lettuce , 2010, Weed Technology.

[35]  D. J. Hills,et al.  Autoguidance system operated at high speed causes almost no tomato damage , 2004 .