Nived Chebrolu

PhD Student
Contact:
Email: nived.chebrolu@nulligg.uni-bonn.de
Tel: +49 – 228 – 73 – 27 03
Fax: +49 – 228 – 73 – 27 12
Office: Nussallee 15, 1.0G, room 1.XXX
Address:
University of Bonn
Photogrammetry, IGG
Nussallee 15
53115 Bonn

Research Interests

  • Robotics
  • Photogrammetry

Publications

2018

  • N. Chebrolu, T. Läbe, and C. Stachniss, “Robust long-term registration of uav images of crop fields for precision agriculture,” Ieee robotics and automation letters, vol. 3, iss. 4, pp. 3097-3104, 2018. doi:10.1109/LRA.2018.2849603
    [BibTeX] [PDF]
    @Article{chebrolu2018ral,
    author={N. Chebrolu and T. L\"abe and C. Stachniss},
    journal={IEEE Robotics and Automation Letters},
    title={Robust Long-Term Registration of UAV Images of Crop Fields for Precision Agriculture},
    year={2018},
    volume={3},
    number={4},
    pages={3097-3104},
    keywords={Agriculture;Cameras;Geometry;Monitoring;Robustness;Three-dimensional displays;Visualization;Robotics in agriculture and forestry;SLAM},
    doi={10.1109/LRA.2018.2849603},
    url={http://www.ipb.uni-bonn.de/pdfs/chebrolu2018ral.pdf}
    }

  • P. Lottes, J. Behley, N. Chebrolu, A. Milioto, and C. Stachniss, “Joint stem detection and crop-weed classification for plant-specific treatment in precision farming,” in Proceedings of the ieee/rsj int. conf. on intelligent robots and systems (iros) , 2018.
    [BibTeX] [PDF] [Video]

    Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat every plant in the field individually and thus can reduce the required use of such chemicals. To achieve that, robots need the ability to identify crops and weeds in the field and must additionally select effective treatments. While certain types of weed can be treated mechanically, other types need to be treated by (selective) spraying. In this paper, we present an approach that provides the necessary information for effective plant-specific treatment. It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying. Our approach uses an end-to- end trainable fully convolutional network that simultaneously estimates stem positions as well as the covered area of crops and weeds. It jointly learns the class-wise stem detection and the pixel-wise semantic segmentation. Experimental evaluations on different real-world datasets show that our approach is able to reliably solve this problem. Compared to state-of-the-art approaches, our approach not only substantially improves the stem detection accuracy, i.e., distinguishing crop and weed stems, but also provides an improvement in the semantic segmentation performance.

    @InProceedings{lottes2018iros,
    author = {P. Lottes and J. Behley and N. Chebrolu and A. Milioto and C. Stachniss},
    title = {Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming},
    booktitle = {Proceedings of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS)},
    year = 2018,
    url = {http://www.ipb.uni-bonn.de/pdfs/lottes18iros.pdf},
    videourl = {https://www.youtube.com/watch?v=C9mjZxE_Sxg},
    abstract = {Applying agrochemicals is the default procedure for conventional weed control in crop production, but has negative impacts on the environment. Robots have the potential to treat every plant in the field individually and thus can reduce the required use of such chemicals. To achieve that, robots need the ability to identify crops and weeds in the field and must additionally select effective treatments. While certain types of weed can be treated mechanically, other types need to be treated by (selective) spraying. In this paper, we present an approach that provides the necessary information for effective plant-specific treatment. It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying. Our approach uses an end-to- end trainable fully convolutional network that simultaneously estimates stem positions as well as the covered area of crops and weeds. It jointly learns the class-wise stem detection and the pixel-wise semantic segmentation. Experimental evaluations on different real-world datasets show that our approach is able to reliably solve this problem. Compared to state-of-the-art approaches, our approach not only substantially improves the stem detection accuracy, i.e., distinguishing crop and weed stems, but also provides an improvement in the semantic segmentation performance.}
    }

2017

  • N. Chebrolu, P. Lottes, A. Schaefer, W. Winterhalter, W. Burgard, and C. Stachniss, “Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields,” The international journal of robotics research, 2017. doi:10.1177/0278364917720510
    [BibTeX] [PDF]
    @Article{chebrolu2017ijrr,
    title = {Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields},
    author = {N. Chebrolu and P. Lottes and A. Schaefer and W. Winterhalter and W. Burgard and C. Stachniss},
    journal = ijrr,
    year = {2017},
    doi = {10.1177/0278364917720510},
    url = {http://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/chebrolu2017ijrr.pdf},
    }

igg
Institute of Geodesy
and Geoinformation
lwf
Faculty of Agriculture
ubn
University of Bonn