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Open AccessArticle

Counting Cattle in UAV Images—Dealing with Clustered Animals and Animal/Background Contrast Changes

1
Embrapa Agricultural Informatics, Campinas 13083-886, Brazil
2
Embrapa Southeast Livestock, São Carlos 13560-970, Brazil
3
Universidade Santo Amaro, UNISA, UNIP, São Paulo 04743-030, Brazil
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 2126; https://doi.org/10.3390/s20072126
Received: 2 March 2020 / Revised: 6 April 2020 / Accepted: 7 April 2020 / Published: 10 April 2020
(This article belongs to the Section Remote Sensors)
The management of livestock in extensive production systems may be challenging, especially in large areas. Using Unmanned Aerial Vehicles (UAVs) to collect images from the area of interest is quickly becoming a viable alternative, but suitable algorithms for extraction of relevant information from the images are still rare. This article proposes a method for counting cattle which combines a deep learning model for rough animal location, color space manipulation to increase contrast between animals and background, mathematical morphology to isolate the animals and infer the number of individuals in clustered groups, and image matching to take into account image overlap. Using Nelore and Canchim breeds as a case study, the proposed approach yields accuracies over 90% under a wide variety of conditions and backgrounds. View Full-Text
Keywords: unmanned aerial vehicles; Canchim breed; Nelore breed; convolutional neural networks; mathematical morphology unmanned aerial vehicles; Canchim breed; Nelore breed; convolutional neural networks; mathematical morphology
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MDPI and ACS Style

Barbedo, J.G.A.; Koenigkan, L.V.; Santos, P.M.; Ribeiro, A.R.B. Counting Cattle in UAV Images—Dealing with Clustered Animals and Animal/Background Contrast Changes. Sensors 2020, 20, 2126. https://doi.org/10.3390/s20072126

AMA Style

Barbedo JGA, Koenigkan LV, Santos PM, Ribeiro ARB. Counting Cattle in UAV Images—Dealing with Clustered Animals and Animal/Background Contrast Changes. Sensors. 2020; 20(7):2126. https://doi.org/10.3390/s20072126

Chicago/Turabian Style

Barbedo, Jayme G.A.; Koenigkan, Luciano V.; Santos, Patrícia M.; Ribeiro, Andrea R.B. 2020. "Counting Cattle in UAV Images—Dealing with Clustered Animals and Animal/Background Contrast Changes" Sensors 20, no. 7: 2126. https://doi.org/10.3390/s20072126

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