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Home / Equine Research Bank / Sensors (Basel) / Improving Animal Monitoring Using Small Unmanned Aircraft Sy...
Sensors (Basel, Switzerland)2021; 21(17); 5697; doi: 10.3390/s21175697

Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks.

Abstract: In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (), horses (), Canada Geese (), and white-tailed deer (). We chose these animals because they were readily accessible and white-tailed deer and Canada Geese are considered aviation hazards, as well as being easily identifiable within aerial imagery. A four-class classification problem involving these species was developed from the acquired data using deep learning neural networks. We studied the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet). Results indicate that the ResNet model with 18 layers, ResNet 18, may be an effective algorithm at classifying between animals while using a relatively small number of training samples. The best ResNet architecture produced a 99.18% overall accuracy (OA) in animal identification and a Kappa statistic of 0.98. The highest OA and Kappa produced by CNN were 84.55% and 0.79 respectively. These findings suggest that ResNet is effective at distinguishing among the four species tested and shows promise for classifying larger datasets of more diverse animals.
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Publication Date: 2021-08-24 PubMed ID: 34502588PubMed Central: PMC8433839DOI: 10.3390/s21175697Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research paper discusses the integration of small unmanned aircraft systems (sUAS) with deep learning networks to improve animal monitoring. The study demonstrates the automated identification of four different animal species through images gathered by these systems. The results suggest that the ResNet deep learning model may be particularly effective for such classifications.

Use of Small Unmanned Aircraft Systems (sUAS

  • The researchers utilized small unmanned aircraft systems (sUAS) to monitor animals. This technology was chosen due to it being customizable, easy to operate, capable of accessing hard-to-navigate areas, and having potential to disturb animals less.
  • The sUAS were equipped with visible spectrum cameras to acquire images of four different animal species: cattle, horses, Canada geese, and white-tailed deer. These animals were chosen due to their availability and because Canada geese and white-tailed deer are known aviation hazards easily identifiable from the sky.

Using Deep Learning Networks for Animal Identification

  • The researchers used the captured images to train deep learning models for automated animal identification. The goal included preventing animal-vehicle collisions, such as those between animals and aircraft on airports, by improving large-area animal monitoring capabilities.
  • A four-class classification system with these four species was developed using deep learning neural networks.

Comparison of Deep Neural Network Models

  • The researchers compared the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet).
  • The results showed that the ResNet model with 18 layers (ResNet 18) effectively classified animals, even with a relatively small number of training samples.
  • The ResNet model produced a 99.18% overall accuracy (OA) in animal identification and a Kappas statistic of 0.98, while the CNN model’s highest OA and Kappa were 84.55% and 0.79 respectively.

Conclusions and Potential Future Applications

  • The study concluded that the ResNet deep learning model shows promising potential for classifying a wide range of animal species due to its high performance in this research.
  • This technology could be developed further to identify and track a wider variety of animals, further improving large-area animal monitoring and potentially reducing dangerous collisions.

Cite This Article

APA
Zhou M, Elmore JA, Samiappan S, Evans KO, Pfeiffer MB, Blackwell BF, Iglay RB. (2021). Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks. Sensors (Basel), 21(17), 5697. https://doi.org/10.3390/s21175697

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 21
Issue: 17
PII: 5697

Researcher Affiliations

Zhou, Meilun
  • Geosystems Research Institute, Mississippi State University, Oxford, MS 39762, USA.
Elmore, Jared A
  • Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Box 9690, Oxford, MS 39762, USA.
Samiappan, Sathishkumar
  • Geosystems Research Institute, Mississippi State University, Oxford, MS 39762, USA.
Evans, Kristine O
  • Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Box 9690, Oxford, MS 39762, USA.
Pfeiffer, Morgan B
  • U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Ohio Field Station, Sandusky, OH 44870, USA.
Blackwell, Bradley F
  • U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Ohio Field Station, Sandusky, OH 44870, USA.
Iglay, Raymond B
  • Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Box 9690, Oxford, MS 39762, USA.

MeSH Terms

  • Aircraft
  • Algorithms
  • Animals
  • Cattle
  • Deep Learning
  • Deer
  • Horses
  • Neural Networks, Computer

Grant Funding

  • AP20WSNWRC00C010 and AP20WSNWRC00C026 / U.S. Department of Agriculture

Conflict of Interest Statement

The authors declare no conflict of interest.

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Citations

This article has been cited 8 times.
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