Merge Fuzzy Visual Servoing and GPS-Based Planning to Obtain a Proper Navigation Behavior for a Small Crop-Inspection Robot

The concept of precision agriculture, which proposes farming management adapted to crop variability, has emerged in recent years. To effectively implement precision agriculture, data must be gathered from the field in an automated manner at minimal cost. In this study, a small autonomous field inspection vehicle was developed to minimise the impact of the scouting on the crop and soil compaction. The proposed approach integrates a camera with a GPS receiver to obtain a set of basic behaviours required of an autonomous mobile robot to inspect a crop field with full coverage. A path planner considered the field contour and the crop type to determine the best inspection route. An image-processing method capable of extracting the central crop row under uncontrolled lighting conditions in real time from images acquired with a reflex camera positioned on the front of the robot was developed. Two fuzzy controllers were also designed and developed to achieve vision-guided navigation. A method for detecting the end of a crop row using camera-acquired images was developed. In addition, manoeuvres necessary for the robot to change rows were established. These manoeuvres enabled the robot to autonomously cover the entire crop by following a previously established plan and without stepping on the crop row, which is an essential behaviour for covering crops such as maize without damaging them.

[1]  Albert Stoll,et al.  Guidance of a Forage Harvester with GPS , 2000, Precision Agriculture.

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

[3]  Thomas Bak,et al.  Agricultural Robotic Platform with Four Wheel Steering for Weed Detection , 2004 .

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

[5]  Angela Ribeiro,et al.  Efficient Distribution of a Fleet of Heterogeneous Vehicles in Agriculture: A Practical Approach to Multi-path Planning , 2015, 2015 IEEE International Conference on Autonomous Robot Systems and Competitions.

[6]  Gianluca Antonelli,et al.  A Fuzzy-Logic-Based Approach for Mobile Robot Path Tracking , 2007, IEEE Transactions on Fuzzy Systems.

[7]  Angela Ribeiro,et al.  Distributed Multi-Level Supervision to Effectively Monitor the Operations of a Fleet of Autonomous Vehicles in Agricultural Tasks , 2015, Sensors.

[8]  Ming Li,et al.  Review of research on agricultural vehicle autonomous guidance , 2009 .

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

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

[11]  J. V. Stafford,et al.  Implementing precision agriculture in the 21st century. , 2000 .

[12]  Yoshisada Nagasaka,et al.  Autonomous guidance for rice transplanting using global positioning and gyroscopes , 2004 .

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

[14]  Jaime Gómez Gil,et al.  A Simple Method to Improve Autonomous GPS Positioning for Tractors , 2011, Sensors.

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

[16]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

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

[18]  Michio Sugeno,et al.  On stability of fuzzy systems expressed by fuzzy rules with singleton consequents , 1999, IEEE Trans. Fuzzy Syst..

[19]  John F. Reid,et al.  Machine vision-based guidance system for agricultural grain harvesters using cut-edge detection , 2003 .

[20]  Sue E. Nokes,et al.  MANIPULATION OF HIGH SPATIAL RESOLUTION AIRCRAFT REMOTE SENSING DATA FOR USE IN SITE-SPECIFIC FARMING , 1998 .

[21]  D. Bochtis,et al.  AE—Automation and Emerging TechnologiesThe vehicle routing problem in field logistics part I , 2009 .

[22]  E. Marshall.  Field‐scale estimates of grass weed populations in arable land , 1988 .

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

[24]  Xavier P. Burgos-Artizzu,et al.  Real-time Image Processing for the Guidance of a Small Agricultural Field Inspection Vehicle , 2008, 2008 15th International Conference on Mechatronics and Machine Vision in Practice.

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

[26]  D. C. Slaughter,et al.  Vision Guided Precision Cultivation , 1999, Precision Agriculture.

[27]  J. Christian Gerdes,et al.  Performance Guarantees for Hazard Based Lateral Vehicle Control , 2002 .

[28]  Kazunobu Ishii,et al.  Field Automation Using Robot Tractor , 2002 .

[29]  Mats Rudemo,et al.  Assessment of weed density at an early stage by use of image processing , 1997 .

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

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

[32]  R. Eaton,et al.  Autonomous Farming: Modeling and Control of Agricultural Machinery in a Unified Framework , 2008, 2008 15th International Conference on Mechatronics and Machine Vision in Practice.

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

[34]  Alberto Tellaeche,et al.  Analysis of natural images processing for the extraction of agricultural elements , 2010, Image Vis. Comput..

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

[36]  Kazunobu Ishii,et al.  The Development of the Autonomous Tractor with Steering Controller Applied by Optimal Control , 2002 .

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

[38]  Eam Khwang Teoh,et al.  Fuzzy speed and steering control of an AGV , 2002, IEEE Trans. Control. Syst. Technol..

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

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

[41]  N. D. Tillett,et al.  Row-following accuracy of an autonomous vision-guided agricultural vehicle , 1997 .

[42]  Dean Pomerleau,et al.  ALVINN, an autonomous land vehicle in a neural network , 2015 .

[43]  Miguel Ángel Sotelo,et al.  Using Fuzzy Logic in Automated Vehicle Control , 2006 .

[44]  Anup Kumar Panda,et al.  Fuzzy logic techniques for navigation of several mobile robots , 2009, Appl. Soft Comput..

[45]  Thierry Fraichard,et al.  Fuzzy control to drive car-like vehicles , 2001, Robotics Auton. Syst..

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

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

[48]  Jaime Gomez-Gil,et al.  Evaluation of the use of low-cost GPS receivers in the autonomous guidance of agricultural tractors. , 2011 .

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

[50]  Alberto Tellaeche,et al.  A computer vision approach for weeds identification through Support Vector Machines , 2011, Appl. Soft Comput..

[51]  S. Sheikholeslam,et al.  Design of decentralized adaptive controllers for a class of interconnected nonlinear dynamical systems , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

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

[53]  Ancha Srinivasan,et al.  Handbook of Precision Agriculture: Principles and Applications , 2006 .

[54]  A. J. S. McDonald,et al.  Weed and crop discrimination using image analysis and artificial intelligence methods , 2003 .

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

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

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

[58]  Robin Gebbers,et al.  Precision Agriculture and Food Security , 2010, Science.