Automation of hydroponic installations using a robot with Position Based Visual Feedback.

This paper presents a robot for the automation of hydroponic farms using Position Based Visual Feedback. In recent years advances in agricultural engineering have resulted in higher crop yields and more environmentally friendly practices. To meet the increasing world food demand even more advances in agriculture have to be made. A way to do this is by intensifying agriculture systems. Hydroponics and robotics are proven areas that contribute to intensifying agricultural practices, but automating these hydroponics systems using current automation techniques requires a large capital investments. The designed system is a low cost and simple addition to existing Nutrient Film Technique infrastructures. This robot manipulates seedlings and plants without human intervention and can be used as a monitoring system to give parameters of the crop and environment to the grower. A low cost alternative to stereo cameras, a Microsoft Kinect and a Position Based Visual Feedback algorithm were used to detect the plants and position the robot. It is demonstrated that by using a Microsoft Kinect the positioning is accurate enough to manipulate plants on an hydroponic system.

[1]  N. Kondo,et al.  A review of automation and robotics for the bio-industry , 2008 .

[2]  Jens Garstka View-dependent 3 D Projection using Depth-Image-based Head Tracking , 2011 .

[3]  Norman H. Villaroman,et al.  Teaching natural user interaction using OpenNI and the Microsoft Kinect sensor , 2011, SIGITE '11.

[4]  W. Parton,et al.  Agricultural intensification and ecosystem properties. , 1997, Science.

[5]  Abhinav Valada,et al.  Wireless Sensor Networks and Actionable Modeling for Intelligent Irrigation , 2011 .

[6]  Chris Graves,et al.  The Nutrient Film Technique , 2011 .

[7]  Seth Hutchinson,et al.  Visual Servo Control Part I: Basic Approaches , 2006 .

[8]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[9]  François Chaumette,et al.  Visual servo control. I. Basic approaches , 2006, IEEE Robotics & Automation Magazine.

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

[11]  Víctor González-Pacheco,et al.  Integration of a low-cost RGB-D sensor in a social robot for gesture recognition , 2011, 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[12]  Fan Zhang,et al.  Probabilistic Hough transform for line detection utilizing surround suppression , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[13]  Josef Kittler,et al.  A Comparative Study of Hough Transform Methods for Circle Finding , 1989, Alvey Vision Conference.

[14]  J. Kittler,et al.  Comparative study of Hough Transform methods for circle finding , 1990, Image Vis. Comput..

[15]  Spyros G. Tzafestas,et al.  Robotics for engineers , 1987, Proceedings of the IEEE.