Robot for weed species plant‐specific management

The rapid evolution of herbicide-resistant weed species has revitalized research in nonchemical methods for weed destruction. Robots with vision-based capabilities for online weed detection and classification are a key enabling factor for the specialized treatment of individual weed species. This paper describes the design, development, and testing of a modular robotic platform with a heterogeneous weeding array for agriculture. Starting from requirements derived from farmer insights, technical specifications are put forward. A design of a robotic platform is conducted based on the required technical specifications, and a prototype is manufactured and tested. The second part of the paper focuses on the weeding mechanism attached to the robotic platform. This includes aspects of vision for weed detection and classification, as well as the design of a weeding array that combines chemical and mechanical methods for weed destruction. Field trials of the weed detection and classification system show an accuracy of 92.3% across a range of weed species, while the heterogeneous weed management system is able to selectively apply a mechanical or chemical control method based on the species of weed. Together, the robotic platform and weeding array demonstrate the potential for robotic plant-species–specific weed management enabled by the vision-based online detection and classification algorithms.

[1]  Brian C. Lovell,et al.  Spatio-temporal covariance descriptors for action and gesture recognition , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[2]  R. Y. van der Weide,et al.  Practical weed control in arable farming and outdoor vegetable cultivation without chemicals , 2006 .

[3]  C. Justice,et al.  Analysis of the dynamics of African vegetation using the normalized difference vegetation index , 1986 .

[4]  Tristan Perez,et al.  Visual detection of occluded crop: For automated harvesting , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[5]  L. Tian,et al.  Direct application end effector for a precise weed control robot , 2009 .

[6]  M. G. Bekker,et al.  Off-the-Road Locomotion: Research and Development in Terramechanics , 1960 .

[7]  A. Dedousis An investigation into the design of precision weeding mechanisms for inter and intra-row weed control , 2007 .

[8]  G. W. Cussans,et al.  Integrated weed management. , 1995 .

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

[10]  E. J. van Henten,et al.  Shadow-resistant segmentation based on illumination invariant image transformation , 2014 .

[11]  Jo Yung Wong,et al.  Terramechanics and Off-Road Vehicle Engineering: Terrain Behaviour, Off-Road Vehicle Performance and Design , 2009 .

[12]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Margot Brereton,et al.  Bringing the Farmer Perspective to Agricultural Robots , 2015, CHI Extended Abstracts.

[14]  Claes Lund Dühring A Low Cost, Modular Robotics Tool Carrier For Precision Agriculture Research , 2016 .

[15]  W. W. Brixius,et al.  TIRES AND TRACKS IN AGRICULTURE , 1976 .

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

[17]  Guoping Zeng,et al.  Two common properties of the erlang-B function, erlang-C function, and Engset blocking function , 2003 .

[18]  Fatih Murat Porikli,et al.  Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.

[19]  Jan Gulliksen,et al.  Key principles for user-centred systems design , 2003, Behav. Inf. Technol..

[20]  L. Alakukku,et al.  Prevention strategies for field traffic-induced subsoil compaction : a review. Part 1. Machine/soil interactions , 2003 .

[21]  G. van Straten,et al.  Systematic design of an autonomous platform for robotic weeding , 2010 .

[22]  Garry R. Griffith,et al.  The economic impact of weeds in Australia. , 2004 .

[23]  Mehrtash Tafazzoli Harandi,et al.  Approximate infinite-dimensional Region Covariance Descriptors for image classification , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[24]  L. I. Leviticus,et al.  PERFORMANCE AND USE OF TRACKS IN AGRICULTURE - A REVIEW , 1995 .

[25]  Ole Green,et al.  Commercial autonomous agricultural platform: Kongskilde Robotti , 2014 .

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

[27]  Gaines E. Miles,et al.  MACHINE VISION AND IMAGE PROCESSING FOR PLANT IDENTIFICATION. , 1986 .

[28]  Matthew. Home An investigation into the design of cultivation systems for inter- and intra-row weed control , 2003 .

[29]  Michael Kassler,et al.  Agricultural Automation in the new Millennium , 2001 .

[30]  Owen Bawden Design of a lightweight, modular robotic vehicle for the sustainable intensification of broadacre agriculture , 2015 .

[31]  Xavier Maldague,et al.  Bayesian classification and unsupervised learning for isolating weeds in row crops , 2014, Pattern Analysis and Applications.

[32]  N. D. Tillett,et al.  Mechanical within-row weed control for transplanted crops using computer vision , 2008 .

[33]  Hideyasu Sumiya,et al.  Plant Identification From Leaves Using Quasi-Sensor Fusion , 1996 .

[34]  Arno Ruckelshausen,et al.  Tube Stamp for mechanical intra-row individual Plant Weed Control , 2014 .

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

[36]  R. Y. van der Weide,et al.  Innovation in mechanical weed control in crop rows , 2008 .

[37]  Sajad Kiani Discriminating The Corn Plants From The Weeds By Using Artificial Neural Networks , 2012 .

[38]  Arno Ruckelshausen,et al.  RemoteFarming.1: Human-machine interaction for a field- robot-based weed control application in organic farming , 2014 .

[39]  Robert Fitch,et al.  Real-time target detection and steerable spray for vegetable crops , 2015 .

[40]  Toon Goedemé,et al.  Neural networks and low-cost optical filters for plant segmentation , 2010, CISIM 2011.

[41]  Keiichi Abe,et al.  Topological structural analysis of digitized binary images by border following , 1985, Comput. Vis. Graph. Image Process..

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

[43]  William Whittaker,et al.  Analytical configuration of wheeled robotic locomotion , 2001 .

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

[45]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[46]  M. G. Bekker,et al.  Theory of Land Locomotion: The Mechanics of Vehicle Mobility , 1962 .

[47]  Thomas Rath,et al.  Improving plant discrimination in image processing by use of different colour space transformations , 2002 .

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

[49]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Chufan Lin,et al.  A support vector machine embedded weed identification system , 2010 .

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

[52]  W. Day Engineering advances for input reduction and systems management to meet the challenges of global food and farming futures , 2010 .

[53]  David Ball,et al.  A lightweight, modular robotic vehicle for the sustainable intensification of agriculture , 2014, ICRA 2014.

[54]  David Ball,et al.  Robotics for Sustainable Broad-Acre Agriculture , 2013, FSR.

[55]  Randy L. Raper,et al.  Agricultural traffic impacts on soil , 2005 .