Curved and straight crop row detection by accumulation of green pixels from images in maize fields

This study proposes a new method for detecting curved and straight crop rows in images captured in maize fields during the initial growth stages of crop and weed plants. The images were obtained under perspective projection with a camera installed onboard and conveniently arranged at the front of a tractor. The final goal was the identification of the crop rows which are crucial for precise autonomous guidance and site-specific treatments, including weed removal based on the identification of plants outside the crop rows. Image quality is affected by uncontrolled lighting conditions in outdoor agricultural environments and by gaps in the crop rows (due to lack of germination or defects during planting). Also, different plants heights and volumes occur due to different growth stages affecting the crop row detection process. The proposed method was designed with the required robustness to cope with the above undesirable situations and it consists of three sequentially linked phases: (i) image segmentation, (ii) identification of starting points and (iii) crop row detection. The main contribution is the ability of the method to detect curved crop rows as well as straights rows even with irregular inter-row spaces. The method performance has been tested in terms of accuracy and time processing.

[1]  Guoquan Jiang,et al.  Wheat rows detection at the early growth stage based on Hough transform and vanishing point , 2016, Comput. Electron. Agric..

[2]  Farzin Deravi,et al.  Efficient algorithm for the detection of parabolic curves , 1995, Other Conferences.

[3]  Qin Zhang,et al.  A Stereovision-based Crop Row Detection Method for Tractor-automated Guidance , 2005 .

[4]  Frédéric Truchetet,et al.  Spatial and Spectral Methods for Weed Detection and Localization , 2002, EURASIP J. Adv. Signal Process..

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

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

[7]  Frederic Truchetet,et al.  Wavelet transform to discriminate between crop and weed in perspective agronomic images , 2009 .

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

[9]  H. J. Olsen.  Determination of row position in small-grain crops by analysis of video images , 1995 .

[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]  Y. H. Han,et al.  Navigation line detection based on support vector machine for automatic agriculture vehicle , 2012 .

[13]  T. Hague,et al.  A bandpass filter-based approach to crop row location and tracking , 2001 .

[14]  Lijun Qi,et al.  Crop-row detection algorithm based on Random Hough Transformation , 2011, Math. Comput. Model..

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

[16]  Gonzalo Pajares,et al.  Integrating Sensory/Actuation Systems in Agricultural Vehicles , 2014, Sensors.

[17]  T. Bakker,et al.  Autonomous navigation using a robot platform in a sugar beet field , 2011 .

[18]  Xue Jinlin,et al.  Vision-Based Guidance Line Detection in Row Crop Fields , 2010, 2010 International Conference on Intelligent Computation Technology and Automation.

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

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

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

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

[23]  G. van Straten,et al.  A vision based row detection system for sugar beet , 2005 .

[24]  John F. Reid,et al.  Machine Vision Based Automated Tractor Guidance , 2003 .

[25]  Qin Zhang,et al.  Development of a stereovision sensing system for 3D crop row structure mapping and tractor guidance , 2008 .

[26]  Gonzalo Pajares,et al.  Automatic expert system based on images for accuracy crop row detection in maize fields , 2013, Expert Syst. Appl..

[27]  Rudolf Scitovski,et al.  Center-based clustering for line detection and application to crop rows detection , 2014 .

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

[29]  Vincent Leemans,et al.  Line cluster detection using a variant of the Hough transform for culture row localisation , 2006, Image Vis. Comput..

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

[31]  Xavier P. Burgos-Artizzu,et al.  utomatic segmentation of relevant textures in agricultural images , 2010 .

[32]  N. D. Tillett,et al.  Computer-Vision-based Hoe Guidance for Cereals — an Initial Trial , 1999 .

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

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

[35]  John F. Reid,et al.  Stereo vision three-dimensional terrain maps for precision agriculture , 2008 .

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

[37]  G. Pajares,et al.  Crop Row Detection in Maize Fields Inspired on the Human Visual Perception , 2012, TheScientificWorldJournal.

[38]  Gonzalo Pajares,et al.  Machine-Vision Systems Selection for Agricultural Vehicles: A Guide , 2016, J. Imaging.

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

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

[41]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[42]  Ivan Vidovic,et al.  Crop row detection by global energy minimization , 2016, Pattern Recognit..

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

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

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