Cattle counting in the wild with geolocated aerial images in large pasture areas

V. H.A. Soares*, M. A. Ponti, R. A. Gonçalves, R. J.G.B. Campello

*Kontaktforfatter

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Abstract

Among the production areas with largest impact on global economy, agriculture and livestock play a prominent role. Technologies have been developed in order to automate and increase the efficiency of these fields. The use of Unmanned Aerial Vehicles (UAVs) has been extensively investigated to improve the efficiency of agricultural production and in the monitoring of animals. One of the most important and challenging tasks in animal monitoring is cattle counting. In this paper, we propose a method for detecting and counting cattle in aerial images obtained by UAVs, based on Convolutional Neural Networks (CNNs) and a graph-based optimization to remove duplicated animals detected in overlapped images. We show that maximizing the degree of matching between animals is a suitable strategy to reduce duplicate counting. We also offer a dataset of real images, obtained from large pasture areas, both for training as well as for testing/benchmarking of cattle counting techniques. Our results show that the proposed method is very competitive, outperforming the state of the art in detecting duplicated animals, while significantly reducing the computational cost of the overall counting task.

OriginalsprogEngelsk
Artikelnummer106354
TidsskriftComputers and Electronics in Agriculture
Vol/bind189
ISSN0168-1699
DOI
StatusUdgivet - okt. 2021
Udgivet eksterntJa

Bibliografisk note

Funding Information:
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Moacir A. Ponti was funded by FAPESP (18/22482-0 and 2019/07316-0) and CNPq (National Council of Technological and Scientific Development) (304266/2020-5). Ricardo J. G. B. Campello was funded by CNPq (302161/2017-1). We thank Nitryx Consulting who supported this work financially, with equipment and access to the farms.

Funding Information:
This study was financed in part by the Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - Brasil (CAPES) - Finance Code 001. Moacir A. Ponti was funded by FAPESP (18/22482-0 and 2019/07316-0) and CNPq (National Council of Technological and Scientific Development) (304266/2020-5). Ricardo J. G. B. Campello was funded by CNPq (302161/2017-1). We thank Nitryx Consulting who supported this work financially, with equipment and access to the farms.

Publisher Copyright:
© 2021 Elsevier B.V.

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