Site‐specific weed control technologies

Summary Site-specific weed control technologies are defined as machinery or equipment embedded with technologies that detect weeds growing in a crop and, taking into account predefined factors such as economics, take action to maximise the chances of successfully controlling them. In this study, we describe the basic parts of site-specific weed control technologies, comprising weed sensing systems, weed management models and precision weed control implements. A review of state-of-the-art technologies shows that several weed sensing systems and precision implements have been developed over the last two decades, although barriers prevent their breakthrough. Most important among these is the lack of a truly robust weed recognition method, owing to mutual shading among plants and limitations in the capacity of highly accurate spraying and weeding apparatus. Another barrier is the lack of knowledge about the economic and environmental potential for increasing the resolution of weed control. The integration of site-specific information on weed distribution, weed species composition and density and the effect on crop yield, is decisive for successful site-specific weed management.

[1]  Tomasz Wyszomirski,et al.  Competitive Asymmetry Reduces Spatial Effects on Size-Structure Dynamics in Plant Populations , 1994 .

[2]  G. Meyer,et al.  Color indices for weed identification under various soil, residue, and lighting conditions , 1994 .

[3]  Scott M. Swinton,et al.  A Bioeconomic Model for Weed Management in Corn and Soybean , 1991 .

[4]  Jørgen Schou,et al.  Cutting weeds with a CO2 laser , 2001 .

[5]  César Fernández-Quintanilla,et al.  Simulating the effects of weed spatial pattern and resolution of mapping and spraying on economics of site-specific management , 2004 .

[6]  J. C. Price.  How unique are spectral signatures , 1994 .

[7]  Patrick Wambacq,et al.  Hyperspectral image sensor for weed-selective spraying , 1999, International Symposium on Photonics and Applications.

[8]  Timothy F. Cootes,et al.  Use of active shape models for locating structures in medical images , 1994, Image Vis. Comput..

[9]  Rew,et al.  A stochastic simulation model for evaluating the concept of patch spraying , 1998 .

[10]  H. D. Coble,et al.  HERB : decision model for postemergence weed control in soybean , 1991 .

[11]  Thomas Bak,et al.  The Effect of Laser Treatment as a Weed Control Method , 2006 .

[12]  Scott M. Swinton,et al.  GWM: General Weed Management Model , 1996 .

[13]  E. Oerke.  Crop losses to pests , 2005, The Journal of Agricultural Science.

[14]  Albert-Jan Baerveldt,et al.  An Agricultural Mobile Robot with Vision-Based Perception for Mechanical Weed Control , 2002, Auton. Robots.

[15]  N. D. Tillett,et al.  A mathematical model of the kinematics of a rotating disc for inter-and intra-row hoeing , 2007 .

[16]  Scott D. Noble,et al.  Site-specific weed management: sensing requirements— what do we need to see? , 2005, Weed Science.

[17]  Svend Christensen,et al.  A decision algorithm for patch spraying , 2003 .

[18]  S. Christensen,et al.  Colour and shape analysis techniques for weed detection in cereal fields , 2000 .

[19]  R. Gerhards,et al.  Practical experiences with a system for site‐specific weed control in arable crops using real‐time image analysis and GPS‐controlled patch spraying , 2006 .

[20]  Lucia Stigliani,et al.  SELOMA: Expert System for Weed Management in Herbicide-Intensive Crops , 1993, Weed Technology.

[21]  W. Petry,et al.  Automatisierte Unterscheidung von Unkrautarten nach Formparametern mit Hilfe der quantitativen Bildanalyse , 1989 .

[22]  David Lamb,et al.  PA—Precision Agriculture: Remote-Sensing and Mapping of Weeds in Crops , 2001 .

[23]  Jacob Weiner,et al.  A Neighborhood Model of Annual‐Plant Interference , 1982 .

[24]  W. S. Lee,et al.  Robotic Weed Control System for Tomatoes , 2004, Precision Agriculture.

[25]  H. Søgaard.  Weed Classification by Active Shape Models , 2005 .

[26]  L. Wiles,et al.  Spatial Distribution of Broadleaf Weeds in North Carolina Soybean (Glycine max) Fields , 1992, Weed Science.

[27]  A. R. Frost.  A pesticide injection metering system for use on agricultural spraying machines , 1990 .

[28]  Svend Christensen,et al.  Crop weed competition and herbicide performance in cereal species and varieties , 1994 .

[29]  Mika Keränen,et al.  Automatic Plant Identification with Chlorophyll Fluorescence Fingerprinting , 2003, Precision Agriculture.

[30]  José Blasco,et al.  AE—Automation and Emerging Technologies: Robotic Weed Control using Machine Vision , 2002 .

[31]  T. Heisel,et al.  Annual weed distributions can be mapped with kriging , 1996 .

[32]  N. D. Tillett,et al.  Automated Crop and Weed Monitoring in Widely Spaced Cereals , 2006, Precision Agriculture.

[33]  Giuseppe Zanin,et al.  GESTINF: A decision model for post-emergence weed management in soybean (Glycine max (L) Merr) , 1997 .

[34]  P. D. Ayers,et al.  A Variable Rate, Direct Nozzle Injection Field Sprayer , 1996 .

[35]  Roland Gerhards,et al.  Site Specific Weed Control in Winter Wheat , 1997 .

[36]  L. Tian,et al.  A Review on Remote Sensing of Weeds in Agriculture , 2004, Precision Agriculture.

[37]  H. T. Søgaard,et al.  Application Accuracy of a Machine Vision-controlled Robotic Micro-dosing System , 2007 .

[38]  David Jones,et al.  Individual leaf extractions from young canopy images using Gustafson-Kessel clustering and a genetic algorithm , 2006 .

[39]  A. M. Ellison,et al.  A Null Model for Neighborhood Models of Plant Competitive Interactions , 1994 .

[40]  José Luis González-Andújar,et al.  Spatial distribution of annual grass weed populations in winter cereals , 2003 .

[41]  Roger D. Cousens,et al.  The effect of weed distribution on predictions of yield loss. , 1990 .

[42]  D. K. Giles,et al.  Precision weed control system for cotton , 2002 .

[43]  F. A. Benson,et al.  Electrical methods of killing plants , 1984 .

[44]  Lisa J. Rew,et al.  Spatial distribution of weeds in arable crops: are current sampling and analytical methods appropriate? , 2001 .

[45]  S. Christensen,et al.  Real‐time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley , 2003 .

[46]  Tae-Jin Kwon,et al.  Palweed:Wheat: A Bioeconomic Decision Model for Postemergence Weed Management in Winter Wheat (Triticum aestivum) , 1995 .

[47]  P.C.H. Miller,et al.  An Experimental Sprayer for the Spatially Selective Application of Herbicides , 1995 .

[48]  J. De Baerdemaeker,et al.  Weed Detection Using Canopy Reflection , 2002, Precision Agriculture.