Wheat rows detection at the early growth stage based on Hough transform and vanishing point

A simple and effective wheat row detection method is presented.A moving window and multiple interlaced scanning strategies were constructed to extract feature points.The Hough transform is used to estimate all possible candidate wheat rows.K-means clustering was performed to look for a clustering center representing the vanishing point.The real wheat rows were detected based on the vanishing point. A simple and effective wheat rows detection method is presented in this paper. It includes five steps: image segmentation, feature points extraction, candidate wheat rows estimation, vanishing point detection and real wheat rows detection. Firstly, a color image was converted into gray-level image and segmented in two parts, the foreground and background. Secondly, to extract feature points indicating centers of wheat rows, a moving window and multiple interlaced scanning strategies were constructed. Thirdly, the Hough transform method was employed to extract straight lines for estimating all possible candidate wheat rows. Fourthly, k-means clustering was performed to look for a clustering center representing the vanishing point. And lastly the real wheat rows were extracted based on the vanishing point. Test results indicate that the proposed method can effectively detect wheat rows at the early growth stage, and the detected rate is up to 90%.

[1]  Tsuguo Okamoto,et al.  Image Analysis of Crop Row Used for Agricultural Mobile Robot (Part 1) , 1995 .

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

[3]  Pulse polishing of die steels in neutral solutions , 1985 .

[4]  R Keicher,et al.  Automatic guidance for agricultural vehicles in Europe , 2000 .

[5]  Vincent Leemans,et al.  Application of the hough transform for seed row localisation using machine vision , 2006 .

[6]  Yun Zhang,et al.  Hyperspectral Vision-Based Machine Learning for Robust Plant Recognition in Autonomous Weed Control. , 2011 .

[7]  J. A. Marchant,et al.  Tracking of row structure in three crops using image analysis , 1996 .

[8]  S. Gan-Mor,et al.  DGPS-Based Automatic Guidance – Implementation and Economical Analysis , 2001 .

[9]  John F. Reid,et al.  Hough-transform-based vision algorithm for crop row detection of an automated agricultural vehicle , 2005 .

[10]  J. Marchant,et al.  Segmentation of row crop plants from weeds using colour and morphology , 2003 .

[11]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[12]  John F. Reid,et al.  DEVELOPMENT OF A PRECISION SPRAYER FOR SITE-SPECIFIC WEED MANAGEMENT , 1999 .

[13]  F. Truchetet,et al.  Modelling agronomic images for weed detection and comparison of crop/weed discrimination algorithm performance , 2009, Precision Agriculture.

[14]  Xavier P. Burgos-Artizzu,et al.  Real-time image processing for crop / weed discrimination in maize fields , 2012 .

[15]  Albert-Jan Baerveldt,et al.  A vision based row-following system for agricultural field machinery , 2005 .

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

[17]  Gonzalo Pajares,et al.  Automatic expert system for weeds/crops identification in images from maize fields , 2013, Expert Syst. Appl..

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

[19]  Gonzalo Pajares,et al.  Support Vector Machines for crop/weeds identification in maize fields , 2012, Expert Syst. Appl..

[20]  Manuel Menezes de Oliveira Neto,et al.  Real-time line detection through an improved Hough transform voting scheme , 2008, Pattern Recognit..

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

[22]  Frederic Truchetet,et al.  Original paper: Assessment of an inter-row weed infestation rate on simulated agronomic images , 2009 .

[23]  Opas Chutatape,et al.  A modified Hough transform for line detection and its performance , 1999, Pattern Recognit..

[24]  John Billingsley,et al.  The successful development of a vision guidance system for agriculture , 1997 .

[25]  John F. Reid,et al.  A guidance directrix approach to vision-based vehicle guidance systems , 2004 .

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

[27]  David C. Slaughter,et al.  Autonomous robotic weed control systems: A review , 2008 .

[28]  Filiberto Pla,et al.  Building perspective models to guide a row crop navigation vehicle , 1997, Image Vis. Comput..

[29]  G. Meyer,et al.  Verification of color vegetation indices for automated crop imaging applications , 2008 .

[30]  Jaime Gomez-Gil,et al.  Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA) , 2009 .

[31]  Tsuguo Okamoto,et al.  Image Analysis of Crop Row Used for Agricultural Mobile Robot (Part 2) , 1995 .

[32]  Guoquan Jiang,et al.  A vision system based crop rows for agricultural mobile robot , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[33]  John A. Marchant,et al.  Real-Time Tracking of Plant Rows Using a Hough Transform , 1995, Real Time Imaging.

[34]  Alberto Tellaeche,et al.  A vision-based method for weeds identification through the Bayesian decision theory , 2008, Pattern Recognit..

[35]  Hellen Adams,et al.  Patent and Trademark Office , 2017 .

[36]  Filiberto Pla,et al.  Colour segmentation based on a light reflection model to locate citrus fruits for robotic harvesting , 1993 .

[37]  G. Polder,et al.  A Mobile Field Robot with Vision-Based Detection of Volunteer Potato Plants in a Corn Crop1 , 2006, Weed Technology.

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

[39]  L. J. Wiles,et al.  Software to quantify and map vegetative cover in fallow fields for weed management decisions , 2011 .

[40]  B. Chen,et al.  Detection Algorithm for Crop Multi-Centerlines Based on Machine Vision , 2008 .

[41]  Guoquan Jiang,et al.  Automatic detection of crop rows based on multi-ROIs , 2015, Expert Syst. Appl..

[42]  Xavier P. Burgos-Artizzu,et al.  Mapping Wide Row Crops with Video Sequences Acquired from a Tractor Moving at Treatment Speed , 2011, Sensors.

[43]  Björn Åstrand,et al.  A vision-guided mobile robot for precision agriculture , 2009 .

[44]  Gonzalo Pajares,et al.  Automatic detection of crop rows in maize fields with high weeds pressure , 2012, Expert Syst. Appl..