1st Workshop BOOK - project RHEA

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A Five-Step Approach for Planning a Robotic Site-Specific Weed Management Program for Winter Wheat Fig. 5. Distribution of three ground units to conduct a spraying operation following a predefined prescription map. Path plan for each individual unit marked with a different colour. Green cells indicate the presence of weeds. Circles indicate the starting position 10 Unit 1 Unit 2 Online spraying Prescription map Genetic algorithms Fitness functions Prescription map Online detection Fig. 6. Online detection and spraying by a fast-response controller
Acknowledgement Robotics and associated High technologies and Equipment for Agriculture The research leading to these results has received funding from the European Union’s Seventh Framework Programme [FP7/2007-2013] under Grant Agreement nº 245986. References Andújar, D., A. Ribeiro, C. Fernández-Quintanilla and J. Dorado (2011). Accuracy and feasibility of optoelectronic sensors for weed mapping in row crops. Sensors 11, 2304-2318. Burgos-Artizzu, X.P., A. Ribeiro, M. Guijarro and G. Pajares (2011). Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronic in Agriculture, 75, 337-346. Christensen, S., H.T. Sogaard, P. Kudsk, M. Norremark, I. Lund and E.S. Nadimi (2009). Site-specific weed control technologies. Weed Research, 49, 233-241. Clarke, J. (2002). Weed management strategies for winter cereals. In: Weed Management Handbook (R.E.L. Naylor, Ed.), pp. 354-358. Blackwell Science Ltd. for British Crop Protection Council, Oxford, UK. Conesa-Muñoz, J. and A. Ribeiro (2011). An evolutionary approach to obtain the optimal distribution of a robot fleet for weed control in arable crops. In: EFITA/WCCA '11: 8th European Federation for Information Technology in Agriculture, Food and the Environment Congress/Word Congress on Computers in Agriculture (E. Gelb and K. Charvát, Eds.), pp. 141-155. Czech Centre for Science and Society, Prague, Czech Republic. Jin, J. and L. Tang (2010). Optimal coverage path planning for arable farming on 2D surfaces. Transactions of the ASABE, 53, 283-295. López-Granados, F. (2011). Weed detection for site-specific weed management: mapping and real-time approaches. Weed Research 51, 1-11. Newman, J.R. (2002). Management of aquatic weeds. In: Weed Management Handbook (R.E.L. Naylor, Ed.). Pp. 399-414. Blackwell Science Ltd. for British Crop Protection Council, Oxford, UK. Parsons, D.J., L.R. Benjamin, J. Clarke, D. Ginsburg, A. Mayes, A.E. Milne and D.J. Wilkinson (2009). Weed Manager-A model-based decision support system for weed management in arable crops. Computers and Electronics in Agriculture, 65, 155-167. Sheley, R.L., J.J. James, B.S. Smith and E.A. Vasquez (2010). Applying ecologically based invasive-plant management. Rangeland Ecology and Management, 63, 605-613. Slaughter, D.C., D.K. Giles and D. Downey (2008). Autonomous robotic weed control systems: A review. Computers and Electronics in Agriculture, 61, 63-78. 11
Page 1: RHEA-2011 ROBOTIICS AND ASSOCIIATED
Page 5: The research leading to these resul
Page 9 and 10: FOREWORD These proceedings are the
Page 11 and 12: CONTENTS Weed management A Five-Ste
Page 13: A Five-Step Approach for Planning a
Page 16 and 17: A Five-Step Approach for Planning a
Page 26 and 27: Effect of flaming with different LP
Page 37 and 38: Robotics and associated High techno
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Page 48 and 49: Hyperspectral imagery to discrimina
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Camera System geometry for site spe
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Robotics and associated High techno
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4. Methods Robotics and associated
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Camera System geometry for site spe
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3. Experiments Robotics and associa
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Techniques for Area Discretization
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2.3 Temperature sensor cards Roboti
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2.5 Principal points of interest Ro
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Temperature (ºC) Temperature (ºC)
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4. Conclusions Robotics and associa
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3. Results and Discussion Robotics
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4. Conclusions Robotics and associa
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1.3. Time management in Webots Robo
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3.2 Risk assessment Robotics and as
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References Robotics and associated
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6. Conclusions and Future Work Robo
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1st Workshop BOOK - project RHEA

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