UAV image based crop and weed distribution estimation on embedded GPU boards

Mulham Fawakherji; Ciro Potena; Domenico D. Bloisi; Marco Imperoli; Alberto Pretto; Daniele Nardi

doi:10.1007/978-3-030-29930-9_10

UAV image based crop and weed distribution estimation on embedded GPU boards

Mulham Fawakherji
, Ciro Potena
, Domenico D. Bloisi
, Marco Imperoli
, Alberto Pretto
, Daniele Nardi

Computer Systems Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Unknown book
Publisher	Springer Verlag
DOIs	https://doi.org/10.1007/978-3-030-29930-9_10
State	Published - 2019

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10.1007/978-3-030-29930-9_10

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@inproceedings{7852c108395e4876a5f6eb38e617ac60,

title = "UAV image based crop and weed distribution estimation on embedded GPU boards",

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.",

author = "Mulham Fawakherji and Ciro Potena and Bloisi, \{Domenico D.\} and Marco Imperoli and Alberto Pretto and Daniele Nardi",

year = "2019",

doi = "10.1007/978-3-030-29930-9\_10",

language = "English",

booktitle = "Unknown book",

publisher = "Springer Verlag",

}

TY - GEN

T1 - UAV image based crop and weed distribution estimation on embedded GPU boards

AU - Fawakherji, Mulham

AU - Potena, Ciro

AU - Bloisi, Domenico D.

AU - Imperoli, Marco

AU - Pretto, Alberto

AU - Nardi, Daniele

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

UR - https://dx.doi.org/10.1007/978-3-030-29930-9_10

U2 - 10.1007/978-3-030-29930-9_10

DO - 10.1007/978-3-030-29930-9_10

M3 - Conference contribution

BT - Unknown book

PB - Springer Verlag

ER -

UAV image based crop and weed distribution estimation on embedded GPU boards

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