The work “UAV-Based Crop and Weed Classification for Smart Farming” by Philipp Lottes, Raghav Khanna, Johannes Pfeifer, Roland Siegwart and Cyrill Stachniss received the Best Paper Award in Automation of the IEEE Robotics and Automation Society at ICRA 2017.

Abstract: Unmanned aerial vehicles (UAVs) and other robots in smart farming applications offer the potential to monitor farm land on a per-plant basis, which in turn can reduce the amount of herbicides and pesticides that must be applied. A central information for the farmer as well as for autonomous agriculture robots is the knowledge about the type and distribution of the weeds in the field. In this regard, UAVs offer excellent survey capabilities at low cost. In this paper, we address the problem of detecting value crops such as sugar beets as well as typical weeds using a camera installed on a light-weight UAV. We propose a system that performs vegetation detection, plant-tailored feature extraction, and classification to obtain an estimate of the distribution of crops and weeds in the field. We implemented and evaluated our system using UAVs on two farms, one in Germany and one in Switzerland and demonstrate that our approach allows for analyzing the field and classifying individual plants.