Robotic harvesting of Gerbera Jamesonii based on detection and three-dimensional modeling of cut flower pedicels

Within the present study, a system for the automated harvest of Gerbera jamesonii pedicels with the help of image analytic methods was developed. The study can be divided mainly into two parts: the development of algorithms for the identification of pedicels in digital images and the development of procedures for harvesting these pedicels with a robot. Images of plants were taken with a stereo camera system, which consisted of two high-resolution CCD-cameras with near-infrared filters. The plant was positioned on a rotatable working desk and images of eight different positions were shot. The developed image processing algorithm segmented the potential pedicel regions in the images, removed noise, differentiated overlapping pedicels by using different algorithms and combined the remaining regions to pedicel objects. From the data of both images and eight plant positions three-dimensional models of the pedicels were created by triangulation. The remaining parts of the plants were modeled in a simple fashion. The evaluated 3D model is used to calculate spatial coordinates for the applied robot control. For harvesting the pedicels, an industrial robot with six axes (plus an additional linear axis) was used. A pneumatic harvest grabber was developed, which harvested the pedicels by cutting them off. In order to guarantee the collision free path of the robot, a path planning module was integrated, which includes the three-dimensional model of the plant and the test facility. With the applied techniques it was possible to correctly detect all pedicels on about 72% of the images. Regarding the whole image series of the respective plant, all pedicels could be detected in at least one photographing position in 97% of all cases. In the harvest experiments 80% of all pedicels could be harvested. The harvest rates decreased with increasing numbers of pedicels on a plant. Therefore, 98% of the pedicels could be harvested of plants with one or two pedicels, but only 51% were harvested of plants with five or more pedicels. In horticultural practice, an identification system for evaluating the stage of maturity should be included. An implementation for harvesting pedicels of different species with similar basic characteristics is imaginable.

[1]  Kuan Chong Ting,et al.  Visual feedback guided robotic cherry tomato harvesting , 1996 .

[2]  Ling Wang,et al.  Summary of Pivotal Technique of Cotton-harvest Robot , 2007, CCTA.

[3]  J. Bontsema,et al.  An Autonomous Robot for Harvesting Cucumbers in Greenhouses , 2002, Auton. Robots.

[4]  Naoshi Kondo,et al.  Vision System for Cucumber-Harvesting Robot , 2000 .

[5]  Yael Edan,et al.  Robotic melon harvesting , 2000, IEEE Trans. Robotics Autom..

[6]  J.L. Pons,et al.  A machine vision system using a laser radar applied to robotic fruit harvesting , 1999, Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (CVBVS'99).

[7]  Giorgio Grasso,et al.  Localization of spherical fruits for robotic harvesting , 2001, Machine Vision and Applications.

[8]  David G. Lowe,et al.  Three-Dimensional Object Recognition from Single Two-Dimensional Images , 1987, Artif. Intell..

[9]  R. C. Harrell,et al.  The Florida robotic grove-lab. , 1990 .

[10]  G. Rabatel,et al.  Magali: a self-propelled robot to pick apples , 1987 .

[11]  Shigehiko Hayashi,et al.  Robotic Harvesting System for Eggplants , 2002 .

[12]  Tsuguo Okamoto,et al.  Robotic Transplanting of Orchid Protocorm in Mericlone Culture , 1993 .

[13]  Mitsuji Monta,et al.  Strawberry Harvesting Robot on Table-top Culture , 2004 .

[14]  N.J.B. McFarlane Image-guidance for robotic harvesting of micropropagated plants , 1993 .

[15]  Christoph Zierl,et al.  Robuste Kalibrierung von CCD-Sensoren für autonome, mobile Systeme , 1995, AMS.

[16]  J. C. Rosier,et al.  Automated harvesting of flowers and cuttings , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[17]  P. F. Davis,et al.  Image-guided robotics for the automation of micropropagation , 1990, EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications.

[18]  Naoshi Kondo,et al.  Cucumber Harvesting Robot and Plant Training System , 1999, J. Robotics Mechatronics.

[19]  R. Noble,et al.  AE—Automation and Emerging Technologies , 2001 .

[20]  J. C. Noordam,et al.  Automated rose cutting in greenhouses with 3D vision and robotics: analysis of 3D vision techniques for stem detection , 2005 .

[21]  W. Simonton Automatic Geranium Stock Processing In A Robotic Workcell , 1990 .

[22]  D. L. Peterson,et al.  A SYSTEMS APPROACH TO ROBOTIC BULK HARVESTING OF APPLES , 1999 .

[23]  Carsten Steger Extraction of curved lines from images , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[24]  Mitsuji Monta,et al.  VISUAL SENSING ALGORITHM FOR CHRYSANTHEMUM CUTTING STICKING ROBOT SYSTEM , 1996 .

[25]  Thomas B. Moeslund,et al.  3D Pose Estimation of Cactus Leaves using an Active Shape Model , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[26]  I Dewa Made Subrata,et al.  3-D Vision Sensor for Cherry Tomato Harvesting Robot , 1997 .

[27]  Gang Xu,et al.  Epipolar Geometry in Stereo, Motion and Object Recognition , 1996, Computational Imaging and Vision.

[28]  P. J. Sobey,et al.  Automated micro-propagation of plant material , 1997, Proceedings Fourth Annual Conference on Mechatronics and Machine Vision in Practice.