Rumex and Urtica detection in grassland by UAV

Binch, Adam, Cooke, Nigel and Fox, Charles (2018) Rumex and Urtica detection in grassland by UAV. In: 14th International Conference on Precision Agriculture, 24 - 27 June 2018, Montreal, Canada.

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. Previous work (Binch & Fox, 2017) used autonomous ground robotic platforms to successfully detect Urtica (nettle) and Rumex (dock) weeds in grassland, to improve farm productivity and the environment through precision herbicide spraying. It assumed that ground robots swathe entire fields to both detect and spray weeds, but this is a slow process as the slow ground platform must drive over every square meter of the field even where there are no weeds. The present study examines a complimentary approach, using unmanned aerial vehicles (UAVs) to perform faster detections, in order to inform slower ground robots of weed location and direct them to spray them from the ground. In a controlled study, it finds that the existing state-of-the-art (Binch & Fox, 2017) ground detection algorithm based on local binary patterns and support vector machines is easily re-usable from a UAV with 4K camera despite large differences in camera type, distance, perspective and motion, without retraining. The algorithm achieves 83-95% accuracy on ground platform data with 1-3 independent views, and improves to 90% from single views on aerial data. However this is only attainable at low altitudes up to 8 feet, speeds below 0.3m/s, and a vertical view angle, suggesting that autonomous or manual UAV swathing is required to cover fields, rather than use of a single high-altitude photograph. This demonstrates for the first time that combined aerial detection with ground spraying system is feasible for Rumex and Urtica in grassland, using UAVs to replace the swathing and detection of weeds then dispatching ground platforms to spray them at the detection sites (as spraying by UAV is illegal in EU countries). This reduces total time requires to spray as the UAV performs the survey stage faster than a ground platform.

Keywords:agriculture, robotics, UAV, vision, weeds
Subjects:D Veterinary Sciences, Agriculture and related subjects > D415 Crop Production
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:31362
Deposited On:16 Mar 2018 13:09

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