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Article

Cost-Performance Evaluation of a Recognition Service of Livestock Activity Using Aerial Images

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Department of Computer Science and Engineering, Campus de Viesques, University of Oviedo, 33204 Gijón, Asturias, Spain
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Department of Spatial Data, Seresco S.A., Matemático Pedrayes 23, 33005 Oviedo, Asturias, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Magaly Koch
Remote Sens. 2021, 13(12), 2318; https://doi.org/10.3390/rs13122318
Received: 30 April 2021 / Revised: 3 June 2021 / Accepted: 5 June 2021 / Published: 13 June 2021
(This article belongs to the Special Issue Data Mining in Multi-Platform Remote Sensing)
The recognition of livestock activity is essential to be eligible for subsides, to automatically supervise critical activities and to locate stray animals. In recent decades, research has been carried out into animal detection, but this paper also analyzes the detection of other key elements that can be used to verify the presence of livestock activity in a given terrain: manure piles, feeders, silage balls, silage storage areas, and slurry pits. In recent years, the trend is to apply Convolutional Neuronal Networks (CNN) as they offer significantly better results than those obtained by traditional techniques. To implement a livestock activity detection service, the following object detection algorithms have been evaluated: YOLOv2, YOLOv4, YOLOv5, SSD, and Azure Custom Vision. Since YOLOv5 offers the best results, producing a mean average precision (mAP) of 0.94, this detector is selected for the creation of a livestock activity recognition service. In order to deploy the service in the best infrastructure, the performance/cost ratio of various Azure cloud infrastructures are analyzed and compared with a local solution. The result is an efficient and accurate service that can help to identify the presence of livestock activity in a specified terrain. View Full-Text
Keywords: livestock activity recognition; Azure; cloud service deployment and cost-performance evaluation; aerial images; CNNs; YOLOv2; YOLOv4; YOLOv5; SSD; Azure Custom Vision livestock activity recognition; Azure; cloud service deployment and cost-performance evaluation; aerial images; CNNs; YOLOv2; YOLOv4; YOLOv5; SSD; Azure Custom Vision
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MDPI and ACS Style

Lema, D.G.; Pedrayes, O.D.; Usamentiaga, R.; García, D.F.; Alonso, Á. Cost-Performance Evaluation of a Recognition Service of Livestock Activity Using Aerial Images. Remote Sens. 2021, 13, 2318. https://doi.org/10.3390/rs13122318

AMA Style

Lema DG, Pedrayes OD, Usamentiaga R, García DF, Alonso Á. Cost-Performance Evaluation of a Recognition Service of Livestock Activity Using Aerial Images. Remote Sensing. 2021; 13(12):2318. https://doi.org/10.3390/rs13122318

Chicago/Turabian Style

Lema, Darío G., Oscar D. Pedrayes, Rubén Usamentiaga, Daniel F. García, and Ángela Alonso. 2021. "Cost-Performance Evaluation of a Recognition Service of Livestock Activity Using Aerial Images" Remote Sensing 13, no. 12: 2318. https://doi.org/10.3390/rs13122318

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