The use of unmanned aerial vehicles (UAVs) in precision agriculture is gaining more and more interest. In this paper, we present a deep learning based method for estimating the crop and weed distribution from images captured by a UAV. The proposed approach runs on an embedded board equipped with a GPU. Quantitative experimental results have been obtained using real images from two different public datasets. The results demonstrate the effectiveness of the proposed approach. © Springer Nature Switzerland AG 2019.
UAV image based crop and weed distribution estimation on embedded GPU boards / Fawakherji, Mulham; Potena, C.; Bloisi, D. D.; Imperoli, M.; Pretto, A.; Nardi, D.. - 1089:(2019), pp. 100-108. (Intervento presentato al convegno 1st Workshop on Deep-learning based Computer Vision for UAV, DL-UAV 2019, and 1st Workshop on Visual Computing and Machine Learning for Biomedical Applications, ViMaBi 2019 held at the 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 tenutosi a Salerno; Italy) [10.1007/978-3-030-29930-9_10].
UAV image based crop and weed distribution estimation on embedded GPU boards
FAWAKHERJI, MULHAM
;Potena C.;Bloisi D. D.;Imperoli M.;Pretto A.;Nardi D.
2019
Abstract
The use of unmanned aerial vehicles (UAVs) in precision agriculture is gaining more and more interest. In this paper, we present a deep learning based method for estimating the crop and weed distribution from images captured by a UAV. The proposed approach runs on an embedded board equipped with a GPU. Quantitative experimental results have been obtained using real images from two different public datasets. The results demonstrate the effectiveness of the proposed approach. © Springer Nature Switzerland AG 2019.| File | Dimensione | Formato | |
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