Volume IV-2/W3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W3, 41-48, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W3-41-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W3, 41-48, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W3-41-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Aug 2017

18 Aug 2017

REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS

A. Milioto, P. Lottes, and C. Stachniss A. Milioto et al.
  • Institute of Geodesy and GeoInformation, University of Bonn, Germany

Keywords: Agriculture Robotics, Convolutional Neural Networks, Deep Learning, Computer Vision, Unmanned Aerial Vehicles

Abstract. UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field.