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Mid-Infrared Sheep Segmentation in Highland Pastures Using Multi-Level Region Fusion OTSU Algorithm

Author

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  • Mengmeng Wang

    (Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Meng Lv

    (Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Haoting Liu

    (Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Qing Li

    (Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China)

Abstract

In highland pastures, grazing is a common method for managing sheep due to the abundance of grassland resources. However, it is easy for sheep to encounter situations such as stray, deviation and attacks from natural enemies; therefore, the remote monitoring of sheep in the highland pastures is an urgent problem to be solved. This paper proposes a mid-infrared sheep segmentation method based on the multi-level region fusion maximum between-class variance algorithm, i.e., OTSU algorithm, for sheep surveillance. First, a mean adjustment OTSU algorithm is designed to better distinguish the interference areas in the background. Second, the Butterworth high-pass filter is combined with the mean adjustment OTSU segmentation algorithm to remove the high-brightness interference areas in the background with slow gray intensity changes. Finally, after filtering out the large area background and small stray point, the two processed results above are fused with the AND logical operation to obtain a final segmentation result. Our algorithm is evaluated using three objective evaluation indicators: the root mean square error ( RMSE ), structural similarity index metric ( SSIM ), and peak signal to noise ratio ( PSNR ). The RMSE , SSIM , PSNR of highland wetland image are 0.43187, 0.99526, and 29.16353. The RMSE , SSIM , PSNR of sandy land image are 0.87472, 0.98388, and 23.87430. The RMSE , SSIM , PSNR of grassland image are 0.65307, 0.99437, and 30.33159. The results show that our algorithm can meet the requirements for the mid-infrared sheep segmentation in highland pastures.

Suggested Citation

  • Mengmeng Wang & Meng Lv & Haoting Liu & Qing Li, 2023. "Mid-Infrared Sheep Segmentation in Highland Pastures Using Multi-Level Region Fusion OTSU Algorithm," Agriculture, MDPI, vol. 13(7), pages 1-22, June.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:7:p:1281-:d:1176629
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    References listed on IDEAS

    as
    1. Christos Tzanidakis & Ouranios Tzamaloukas & Panagiotis Simitzis & Panagiotis Panagakis, 2023. "Precision Livestock Farming Applications (PLF) for Grazing Animals," Agriculture, MDPI, vol. 13(2), pages 1-23, January.
    2. Ganesh Kumar Balakrishnan & Chong Tak Yaw & Siaw Paw Koh & Tarek Abedin & Avinash Ashwin Raj & Sieh Kiong Tiong & Chai Phing Chen, 2022. "A Review of Infrared Thermography for Condition-Based Monitoring in Electrical Energy: Applications and Recommendations," Energies, MDPI, vol. 15(16), pages 1-37, August.
    3. Maimunah Mohd Ali & Norhashila Hashim & Samsuzana Abd Aziz & Ola Lasekan, 2022. "Characterisation of Pineapple Cultivars under Different Storage Conditions Using Infrared Thermal Imaging Coupled with Machine Learning Algorithms," Agriculture, MDPI, vol. 12(7), pages 1-17, July.
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