RETRACTED: Advanced techniques for Weed and crop identification for site specific Weed management

Weed management plays a major role in the production and economic benefits derived by agricultural industry worldwide. The monitoring of weed pressure, economic threshold, yield loss and environmental impact is critical for sustainable agriculture. Currently research is being carried out relating to weed mapping at field scale and the development of machine vision controlled equipment. Remote sensing and aerial imaging techniques utilised for site-specific weed management have limitations due to the accuracy of satellite imagery, its cost and timing. The advent of optoelectronic sensing and enhanced computing has provided a demand for the development of the real-time assessment and management of weeds in fields. The available technologies that can be used for developing a ground-sensor based system to assist in determination of weed pressure and economise on the application of herbicide under field conditions are reviewed. These technologies include image-based identification and spectroscopic methods for weed identification and threshold determination. The various methods studied and the concepts pursued by various researchers are discussed in the paper.

[1]  R. D. Cousens,et al.  Spatial dynamics of weeds: an overview , 1997 .

[2]  T. Borregaard,et al.  Crop–weed Discrimination by Line Imaging Spectroscopy , 2000 .

[3]  Larry Biehl,et al.  Changes in spectral properties of detached birch leaves , 1985 .

[4]  David A. Mortensen,et al.  Identifying associations among site properties and weed species abundance. II. Hypothesis generation , 2000, Weed Science.

[5]  Francisca López-Granados,et al.  Multi-species weed spatial variability and site-specific management maps in cultivated sunflower , 2003, Weed Science.

[6]  Qamar Uz Zaman,et al.  Detecting Weeds in Wild Blueberry Field Based on Color Images , 2009 .

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

[8]  Jin-Young Jeong,et al.  AE—Automation and Emerging Technologies: Weed–plant Discrimination by Machine Vision and Artificial Neural Network , 2002 .

[9]  H. P. W. Jayasuriya,et al.  Development of a Real-time, Variable Rate Herbicide Applicator Using Machine Vision for Between-row Weeding of Sugarcane Fields , 2006 .

[10]  Ruiliang Pu,et al.  Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data , 2003, IEEE Trans. Geosci. Remote. Sens..

[11]  Luc Van Gool,et al.  Development of a weed activated spraying machine for targeted application of herbicides , 2002 .

[12]  Maria C. Garcia-Alegre,et al.  Development of an image analysis system for estimation of weed pressure. , 2005 .

[13]  E. Franz,et al.  THE USE OF LOCAL SPECTRAL PROPERTIES OF LEAVES AS AN AID FOR IDENTIFYING WEED SEEDLINGS IN DIGITAL IMAGES , 1990 .

[14]  Zhengwei Yang,et al.  IMPACT OF BAND-RATIO ENHANCED AWIFS IMAGE TO CROP CLASSIFICATION ACCURACY , 2008 .

[15]  J. Qi,et al.  Identification of red and NIR spectral regions and vegetative indices for discrimination of cotton nitrogen stress and growth stage , 2005 .

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

[17]  L. Bruce,et al.  Wavelet analysis of hyperspectral reflectance data for detecting pitted morningglory (Ipomoea lacunosa) in soybean (Glycine max) , 2003 .

[18]  Alex Martin,et al.  A simulation of herbicide use based on weed spatial distribution , 1995 .

[19]  David A. Mortensen,et al.  Economic Importance of Managing Spatially Heterogeneous Weed Populations , 1998, Weed Technology.

[20]  H. T. Søgaard,et al.  Determination of crop rows by image analysis without segmentation , 2003 .

[21]  Nancy F. Glenn,et al.  A review of remote sensing of invasive weeds and example of the early detection of spotted knapweed (Centaurea maculosa) and babysbreath (Gypsophila paniculata) with a hyperspectral sensor , 2005, Weed Science.

[22]  C. Wiegand,et al.  Use of spectral vegetation indices to infer leaf area, evapotranspiration and yield. I. Rationale. , 1990 .

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

[24]  R. B. Brown,et al.  Remote Sensing for Identification of Weeds in No-till Corn , 1994 .

[25]  Ning Wang,et al.  A real-time, embedded, weed-detection system for use in wheat fields , 2007 .

[26]  V. Fontaine And T.G. Crowe,et al.  Development of line-detection algorithms for local positioning in densely seeded crops , 2006 .

[27]  F. E. LaMastus,et al.  Using remote sensing to detect weed infestations in Glycine max , 2000, Weed Science.

[28]  C. Jordan.  Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[29]  J. E. Pinder,et al.  Indications of Relative Drought Stress in Longleaf Pine from Thematic Mapper Data , 1999 .

[30]  A. J. Richardson,et al.  Light Reflectance and Remote Sensing of Weeds in Agronomic and Horticultural Crops , 1985, Weed science.

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

[32]  Richard Aspinall,et al.  Predicting the occurrence of nonindigenous species using environmental and remotely sensed data , 2005, Weed Science.

[33]  Brett Whelan,et al.  Does kriging predict weed distributions accurately enough for site-specific weed control? , 2001 .

[34]  Michael A. Wulder,et al.  Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage , 2003 .

[35]  Frédéric Lebeau,et al.  Improving in-row weed detection in multispectral stereoscopic images , 2009 .

[36]  T. Kataoka,et al.  Unified hyperspectral imaging methodology for agricultural sensing using software framework. , 2009 .

[37]  Hartmut K. Lichtenthaler,et al.  Cell wall bound ferulic acid, the major substance of the blue-green fluorescence emission of plants. , 1998 .

[38]  C. W. Lindwall,et al.  Factors affecting the operation of the weed-sensing Detectspray system , 1998, Weed Science.

[39]  Dallas E. Peterson,et al.  WEED DETECTION USING COLOR MACHINE VISION , 2000 .

[40]  Yud-Ren Chen,et al.  Machine vision technology for agricultural applications , 2002 .

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

[42]  Ning Wang,et al.  DESIGN OF AN OPTICAL WEED SENSOR USINGPLANT SPECTRAL CHARACTERISTICS , 2001 .

[43]  Alberto Tellaeche,et al.  Improving weed pressure assessment using digital images from an experience-based reasoning approach , 2009 .

[44]  Christian Germain,et al.  Row detection in high resolution remote sensing images of vine fields , 2003 .

[45]  David R. Shaw,et al.  Application Timing of Herbicides for the Control of Redvine (Brunnichia ovata) , 1991, Weed Technology.

[46]  Rew,et al.  Evaluating the accuracy of mapping weeds in seedling crops using airborne digital imaging: Avena spp. in seedling triticale , 1999 .

[47]  C. Tucker.  Remote sensing of leaf water content in the near infrared , 1980 .

[48]  Martin Chamberland,et al.  An operational fluorescence system for crop assessment , 2004, SPIE Optics East.

[49]  R. B. Brown,et al.  Site specific weed management with a direct-injection precision sprayer. , 2000 .

[50]  Ismael Moya,et al.  Ultraviolet-induced fluorescence for plant monitoring: present state and prospects , 1999 .

[51]  Frédéric Lebeau,et al.  Selection of the most efficient wavelength bands for discriminating weeds from crop , 2008 .

[52]  L. Lymburner,et al.  Estimation of Canopy-Average Surface-Specific Leaf Area Using Landsat TM Data , 2000 .

[53]  J. V. Stafford,et al.  A hand-held data logger with integral GPS for producing weed maps by field walking , 1996 .

[54]  Shiv O. Prasher,et al.  Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn , 2003 .

[55]  E. Franz,et al.  Shape description of completely-visible and partially-occluded leaves for identifying plants in digital images. , 2016 .

[56]  François J. Tardif,et al.  Evaluation of site-specific weed management using a direct-injection sprayer , 2001, Weed Science.

[57]  M. Susan Moran,et al.  Image-based remote sensing for agricultural management-perspectives of image providers, research scientists and users , 2000 .

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

[59]  T. G. Crowe,et al.  BACKGROUND EFFECTS ON APPARENT LEAF REFLECTANCE , 2001 .

[60]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[61]  B. Rock,et al.  Detection of changes in leaf water content using Near- and Middle-Infrared reflectances , 1989 .

[62]  Leonid P. Yaroslavsky,et al.  Weed detection in multi-spectral images of cotton fields , 2005 .

[63]  Chun-Chieh Yang,et al.  Development of an Image Processing System and a Fuzzy Algorithm for Site-Specific Herbicide Applications , 2003, Precision Agriculture.

[64]  Hiroshi Okamoto,et al.  Plant classification for weed detection using hyperspectral imaging with wavelet analysis , 2007 .

[65]  Yubin Lan,et al.  Development of an airborne remote sensing system for crop pest management: system integration and verification. , 2009 .

[66]  Luc Van Gool,et al.  Multi-spectral vision system for weed detection , 2001, Pattern Recognit. Lett..

[67]  Case R. Medlin,et al.  Detection of Weed Species in Soybean Using Multispectral Digital Images1 , 2004, Weed Technology.

[68]  Reyer Zwiggelaar,et al.  A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops , 1998 .

[69]  J. Hemming,et al.  PA—Precision Agriculture: Computer-Vision-based Weed Identification under Field Conditions using Controlled Lighting , 2001 .

[70]  Edward C. Luschei,et al.  Implementing and conducting on-farm weed research with the use of GPS , 2001 .

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

[72]  Yves Goulas,et al.  Dualex: a new instrument for field measurements of epidermal ultraviolet absorbance by chlorophyll fluorescence. , 2004, Applied optics.

[73]  Louis Longchamps,et al.  Discrimination of corn, grasses and dicot weeds by their UV-induced fluorescence spectral signature , 2010, Precision Agriculture.

[74]  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 .

[75]  A. J. M. Timmermans,et al.  Weed-It: a new selective weed control system , 1996, Other Conferences.

[76]  P. Thenkabail,et al.  Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .

[77]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[78]  Helmut Schmidt,et al.  Fotooptische Sensoren – Eine Alternative für die Unkrauterkennung , 1999 .

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

[80]  D. Ess,et al.  Precision farming and precision pest management: the power of new crop production technologies. , 1998, Journal of nematology.

[81]  Tsuyoshi Akiyama,et al.  Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements , 1991 .

[82]  L. Bruce,et al.  Remote Sensing to Distinguish Soybean from Weeds After Herbicide Application1 , 2004, Weed Technology.

[83]  F. Truchetet,et al.  Crop/weed discrimination in perspective agronomic images , 2008 .

[84]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[85]  M. S. Moran,et al.  Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .

[86]  Jerry L. Hatfield,et al.  Integrated Weed and Soil Management , 1997 .

[87]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[88]  Prasad S. Thenkabail,et al.  Landsat-5 Thematic Mapper models of soybean and corn crop characteristics , 1994 .

[89]  Yubin Lan,et al.  Analysis of vegetation indices derived from aerial multispectral and ground hyperspectral data , 2009 .

[90]  Alberto Tellaeche,et al.  A new vision-based approach to differential spraying in precision agriculture , 2008 .

[91]  V. Palanisamy,et al.  Texture-based medical image classification of computed tomography images using MRCSF , 2009, Int. J. Medical Eng. Informatics.

[92]  Gaines E. Miles,et al.  Application of machine vision to shape analysis in leaf and plant identification , 1993 .

[93]  G. W. Cussans,et al.  A technique for mapping the spatial distribution of Elymus repots, with estimates of the potential reduction in herbicide usage from patch spraying , 1996 .

[94]  Youngwook Kim,et al.  2-band enhanced vegetation index without a blue band and its application to AVHRR data , 2007, SPIE Optical Engineering + Applications.

[95]  J. V. Stafford,et al.  Spatially variable treatment of weed patches , 1996 .

[96]  J. McMurtrey,et al.  Laser-induced fluorescence of green plants. 3: LIF spectral signatures of five major plant types. , 1985, Applied optics.