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RoboWeedSupport-Semi-Automated Unmanned Aerial System for Cost Efficient High Resolution in Sub-Millimeter Scale Acquisition of Weed Images
Recent advances in the Unmanned Aerial System (UAS) safety and perception systems enable safe low altitude autonomous terrain following flights recently demonstrated by the consumer DJI Mavic PRO and Phamtom 4 Pro drones. This paper presents the first prototype system utilizing this functionality in form of semi-automated UAS based collection of crop/weed images where the embedded perception system ensures a significantly safer and faster gathering of weed images with sub-millimeter resolution. The system is to be used when the weeds are at cotyledon stage and prior to the harvest recognizing the grass weed species, which cannot be discriminated at the cotyledon stage.
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[1] J. M. Peña, J. Torres-Sánchez, A. I. de Castro, M. Kelly, and F. López-Granados, “Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images,” PLoS One, vol. 8, no. 10, p. e77151, 2013.
[2] D. Gómez-Candón, A. I. De Castro, and F. López-Granados, “Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat,” Precis. Agric., vol. 15, no. 1, pp. 44–56, 2013.
[3] J. M. Peña, J. Torres-Sánchez, A. Serrano-Pérez, A. I. de Castro, and F. López-Granados, “Quantifying Efficacy and Limits of Unmanned Aerial Vehicle (UAV) Technology for Weed Seedling Detection as Affected by Sensor Resolution,” Sensors, vol. 15, no. 3, pp. 5609–5626, 2015.
[4] M. Dyrmann and R. N. Jørgensen, “RoboWeedSupport: weed recognition for reduction of herbicide consumption,” in Precision agriculture ’15, J. V Stafford, Ed. Wageningen Academic Publishers Books, 2015, pp. 571–578.
[5] M. Dyrmann, H. Karstoft, and H. S. Midtiby, “Plant species classification using deep convolutional neural network,” Biosyst. Eng., vol. 151, pp. 72–80, Nov. 2016.
[6] M. Laursen, R. Jørgensen, H. Midtiby, K. Jensen, M. Christiansen, T. Giselsson, A. Mortensen, and P. Jensen, “Dicotyledon Weed Quantification Algorithm for Selective Herbicide Application in Maize Crops,” Sensors, vol. 16, no. 11, p. 1848, 2016.
[7] R. B. Brown and S. D. Noble, “Site-specific weed management: sensing requirements--- what do we need to see?,” Weed Sci., vol. 53, no. 2, pp. 252–258, 2005.
[8] D. E. Guyer, G. E. Miles, M. M. Schreiber, O. R. Mitchell, and V. C. Vanderbilt, “Machine vision and image processing for plant identification,” Trans. ASAE, vol. 29, no. 6, pp. 1500–1507, 1986.
[9] R. N. Jørgensen, M. S. Laursen, M. Dymann, and R. N. Poulsen, “RoboWeedSupport - Weed Mapping with drones using a DJI Phantom 4,” 2016. (Online). Available: https://www.youtube.com/watch?v=yegTo2mw6GA. (Accessed: 07-Feb-2017).
[10] J. Rasmussen, J Nielsen, F Garcia-Ruiz, and J. C. Streibig, “Potential uses of small unmanned aircraft systems (UAS) in weed research,” Weed Res., vol. 53(4), no. 242–248, pp. 242–248, 2013.
[11] S. L. Madsen, M. S. Larsen, R. N. Poulsen, and R. N. Jørgensen, “RoboWeedSupport - Semi-automated UAS system for cost efficient high resolution in sub-millimeter scale acquisition of weed images,” in ECPA 2017 - 11th European Conference on Precision Agriculture, 2017.
[12] L. DJI Technology CO., “Creating a MapView and Waypoint Application,” DJI Mobile SDK Documentation, 2016. (Online). Available: https://developer.dji.com/mobile-sdk/documentation/ios-tutorials/GSDemo.html. (Accessed: 07-Feb-2017).
[13] M. Dyrmann, R. N. Jørgensen, and H. S. Midtiby, “RoboWeedSupport - Detection of Weed Locations in Leaf Occluded Cereal Crops using a Fully Convolutional Neural Network,” in ECPA 2017 - 11th European Conference on Precision Agriculture, 2017.
[14] M. S. Laursen, R. N. Jørgensen, M. Dyrmann, and R. N. Poulsen, “RoboWeedSupport - Sub millimeter weed image acquisition in cereal crops with speeds up till 50 km/h,” in ICPA 2017 - 19th International Conference on Precision Agriculture, 2017.
[15] L. DJI Technology CO., “Phantom 4 Pro Specs,” 2017. (Online). Available: https://www.dji.com/phantom-4-pro/info. (Accessed: 07-Feb-2017).
[16] P. Rydahl, N.-P. Jensen, M. Dyrmann, P. H. Nielsen, and R. N. Jørgensen, “RoboWeedSupport - Presentation of a cloud based system bridging the gap between in-field weed inspections and decision support systems,” in ECPA 2017 - 11th European Conference on Precision Agriculture, 2017.
[17] J. H. Jeppesen, R. H. Jacobsen, R. N. Jørgensen, R. Gislum, A. Halberg, and S. T. Toftegaard, “Improving Profitability in Precision Agriculture by Identification of High-Variation Fields based on Open Satellite Imagery,” in ECPA 2017 - 11th European Conference on Precision Agriculture, 2017.
[18] J. H. Jeppesen, R. H. Jacobsen, R. Nyholm Jørgensen, and T. S. Toftegaard, “Towards Data-Driven Precision Agriculture using Open Data and Open Source Software,” in International Conference on Agricultural Engineering 2016, 2016.
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