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This study introduces an automated method using Unmanned Aerial Vehicles (UAVs) and deep learning for accurate cattle counting in extensive production systems. The novel approach achieves over 90% accuracy, improving livestock management efficiency.

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Animal Science

Background:

  • Livestock management in extensive systems presents challenges due to large areas.
  • Unmanned Aerial Vehicles (UAVs) offer a promising solution for data collection in these environments.
  • Automated image analysis algorithms for livestock counting are currently limited.

Purpose of the Study:

  • To develop and validate an automated method for counting cattle using UAV imagery.
  • To address the scarcity of effective algorithms for extracting livestock data from aerial images.
  • To enhance the efficiency and accuracy of livestock management in extensive systems.

Main Methods:

  • A deep learning model was employed for initial animal detection.
  • Color space manipulation and mathematical morphology techniques were used to refine animal isolation.
  • Image matching was incorporated to handle overlapping image data.
  • The method was tested on Nelore and Canchim cattle breeds.

Main Results:

  • The proposed method demonstrated high accuracy, exceeding 90% in cattle counting.
  • The approach proved effective across diverse environmental conditions and backgrounds.
  • The combination of deep learning and image processing techniques successfully identified and counted animals, even in clustered groups.

Conclusions:

  • The developed UAV-based system provides an accurate and efficient solution for automated cattle counting.
  • This technology has the potential to significantly improve livestock monitoring and management in extensive farming.
  • Further research can explore broader applications in precision livestock farming.