Crop Row Detection in Maize for Developing Navigation Algorithms Under Changing Plant Growth Stages

To develop robust algorithms for agricultural navigation, different growth stages of the plants have to be considered. For fast validation and repeatable testing of algorithms, a dataset was recorded by a 4 wheeled robot, equipped with a frame of different sensors and was guided through maize rows. The robot position was simultaneously tracked by a total station, to get precise reference of the sensor data. The plant position and parameters were measured for comparing the sensor values. A horizontal laser scanner and corresponding total station data was recorded for 7 times over a period of 6 weeks. It was used to check the performance of a common RANSAC row algorithm. Results showed the best heading detection at a mean growth height of 0.268 m.

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

[2]  Marcel Bergerman,et al.  Mapping Orchards for Autonomous Navigation , 2014 .

[3]  K. Zhou,et al.  Route planning for orchard operations , 2015, Comput. Electron. Agric..

[4]  David Ball,et al.  Vision based guidance for robot navigation in agriculture , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[5]  Mark Whitty,et al.  GPS-free Localisation and Navigation of an Unmanned Ground Vehicle for Yield Forecasting in a Vineyard , 2014 .

[6]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[7]  Nicolai Petkov,et al.  Edge and line oriented contour detection: State of the art , 2011, Image Vis. Comput..

[8]  Sunglok Choi,et al.  Robust ground plane detection from 3D point clouds , 2014, International Conference on Control, Automation and Systems.

[9]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[10]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[11]  S. Hiremath,et al.  Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter , 2014 .

[12]  Kazunobu Ishii,et al.  Development of an Autonomous Navigation System using a Two-dimensional Laser Scanner in an Orchard Application , 2007 .

[13]  Niels Kjølstad Poulsen,et al.  Derivative free Kalman filtering used for orchard navigation , 2010, 2010 13th International Conference on Information Fusion.

[14]  Andreas Zell,et al.  Plant Species Classification Using a 3D LIDAR Sensor and Machine Learning , 2010, 2010 Ninth International Conference on Machine Learning and Applications.

[15]  Peter Biber,et al.  Plant detection and mapping for agricultural robots using a 3D LIDAR sensor , 2011, Robotics Auton. Syst..

[16]  Cui-Jun Zhao,et al.  A machine vision based crop rows detection for agricultural robots , 2010, 2010 International Conference on Wavelet Analysis and Pattern Recognition.