A Stereovision-based Crop Row Detection Method for Tractor-automated Guidance

Steering agricultural machinery within rowed crop fields is a tedious task for producers. Automated guidance of the machinery will not only reduce operator fatigue but also increase both the productivity and safety of the operation. An essential aspect of automatic guidance is the ability to identify the pathway between the crop rows. This research was to develop an implementable row-detection algorithm for a stereovision-based agricultural machinery guidance system. The algorithm consists of functions for stereo-image processing, elevation map creation and navigation point determination. The method developed first reconstructed a three-dimensional crop elevation map from a stereovision image of crop rows and then searched for optimal navigation points from the map. The developed stereovision-based crop row detection system was tested in a soya bean field to follow both straight and curved soya bean rows at typical operating speeds. Field validation tests indicated that the stereovision-based guidance system could localise crop rows accurately and reliably in a weedy field with missing sections of soya beans. Based on crop row localisation information, an automated navigation system could guide an autonomous agricultural tractor following both straight and curved rows accurately at normal field operation speeds.

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

[2]  T. Kozai,et al.  A Binocular Stereovision System for Transplant Growth Variables Analysis , 2003 .

[3]  N. D. Tillett,et al.  Inter-row vision guidance for mechanical weed control in sugar beet , 2002 .

[4]  S. W. Searcy,et al.  Vision-based guidance of an agriculture tractor , 1987, IEEE Control Systems Magazine.

[5]  Kazunobu Ishii,et al.  Development of the Agricultural Autonomous Tractor with an RTK-GPS and a Fog , 2001 .

[6]  Thomas Bell.  Automatic tractor guidance using carrier-phase differential GPS. , 2000 .

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

[8]  Du-Ming Tsai,et al.  The evaluation of normalized cross correlations for defect detection , 2003, Pattern Recognit. Lett..

[9]  Daniel Scharstein,et al.  View Synthesis Using Stereo Vision , 2001, Lecture Notes in Computer Science.

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

[11]  H Qiu,et al.  Feedforward-plus-proportional-integral-derivative controller for an off-road vehicle electrohydraulic steering system , 2003 .

[12]  John F. Reid,et al.  Vehicle Guidance Parameter Determination from Crop Row Images using Principal Component Analysis , 2000 .

[13]  Jiahua Wu,et al.  Extracting the three-dimensional shape of live pigs using stereo photogrammetry , 2004 .

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

[15]  J. Lines,et al.  An automatic image-based system for estimating the mass of free-swimming fish , 2001 .

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

[17]  John F. Reid,et al.  FUZZY CONTROL OF ELECTROHYDRAULIC STEERING SYSTEMS FOR AGRICULTURAL VEHICLES , 2001 .