Computational Techniques for Analysis of Thermal Images of Pigs and Characterization of Heat Stress in the Rearing Environment
Heat stress stands out as one of the main elements linked to concerns related to animal thermal comfort. This research aims to develop a sequential methodology for the extraction of automatic characteristics from thermal images and the classification of heat stress in pigs by means of machine learni...
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| Language: | English |
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MDPI AG
2024-09-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/6/3/183 |
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| author | Maria de Fátima Araújo Alves Héliton Pandorfi Rodrigo Gabriel Ferreira Soares Gledson Luiz Pontes de Almeida Taize Calvacante Santana Marcos Vinícius da Silva |
| author_facet | Maria de Fátima Araújo Alves Héliton Pandorfi Rodrigo Gabriel Ferreira Soares Gledson Luiz Pontes de Almeida Taize Calvacante Santana Marcos Vinícius da Silva |
| author_sort | Maria de Fátima Araújo Alves |
| collection | DOAJ |
| description | Heat stress stands out as one of the main elements linked to concerns related to animal thermal comfort. This research aims to develop a sequential methodology for the extraction of automatic characteristics from thermal images and the classification of heat stress in pigs by means of machine learning. Infrared images were obtained from 18 pigs housed in air-conditioned and non-air-conditioned pens. The image analysis consisted of its pre-processing, followed by color segmentation to isolate the region of interest and later the extraction of the animal’s surface temperatures, from a developed algorithm and later the recognition of the comfort pattern through machine learning. The results indicated that the automated color segmentation method was able to identify the region of interest with an average accuracy of 88% and the temperature extraction differed from the Therma Cam program by 0.82 °C. Using a Vector Support Machine (SVM), the research achieved an accuracy rate of 80% in the automatic classification of pigs in comfort and thermal discomfort, with an accuracy of 91%, indicating that the proposal has the potential to monitor and evaluate the thermal comfort of pigs effectively. |
| format | Article |
| id | doaj-art-a80d080ab0dd4a52938de589b7fee64f |
| institution | OA Journals |
| issn | 2624-7402 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-a80d080ab0dd4a52938de589b7fee64f2025-08-20T01:56:09ZengMDPI AGAgriEngineering2624-74022024-09-01633203322610.3390/agriengineering6030183Computational Techniques for Analysis of Thermal Images of Pigs and Characterization of Heat Stress in the Rearing EnvironmentMaria de Fátima Araújo Alves0Héliton Pandorfi1Rodrigo Gabriel Ferreira Soares2Gledson Luiz Pontes de Almeida3Taize Calvacante Santana4Marcos Vinícius da Silva5Department of Agricultural Engineering, Federal Rural University of Pernambuco, Recife 52171-900, PE, BrazilDepartment of Agricultural Engineering, Federal Rural University of Pernambuco, Recife 52171-900, PE, BrazilDepartment of Statistics and Computer Science, Federal Rural University of Pernambuco, Recife 52171-900, PE, BrazilDepartment of Agricultural Engineering, Federal Rural University of Pernambuco, Recife 52171-900, PE, BrazilDepartment of Agricultural Engineering, Federal Rural University of Pernambuco, Recife 52171-900, PE, BrazilPrograma de Pós-Graduação em Ciências Florestais, Universidade Federal de Campina Grande, Av. Universitária, s/n, Santa Cecília, Patos 58708-110, PB, BrazilHeat stress stands out as one of the main elements linked to concerns related to animal thermal comfort. This research aims to develop a sequential methodology for the extraction of automatic characteristics from thermal images and the classification of heat stress in pigs by means of machine learning. Infrared images were obtained from 18 pigs housed in air-conditioned and non-air-conditioned pens. The image analysis consisted of its pre-processing, followed by color segmentation to isolate the region of interest and later the extraction of the animal’s surface temperatures, from a developed algorithm and later the recognition of the comfort pattern through machine learning. The results indicated that the automated color segmentation method was able to identify the region of interest with an average accuracy of 88% and the temperature extraction differed from the Therma Cam program by 0.82 °C. Using a Vector Support Machine (SVM), the research achieved an accuracy rate of 80% in the automatic classification of pigs in comfort and thermal discomfort, with an accuracy of 91%, indicating that the proposal has the potential to monitor and evaluate the thermal comfort of pigs effectively.https://www.mdpi.com/2624-7402/6/3/183machine learninganimal welfarethermal monitoringinfrared thermography |
| spellingShingle | Maria de Fátima Araújo Alves Héliton Pandorfi Rodrigo Gabriel Ferreira Soares Gledson Luiz Pontes de Almeida Taize Calvacante Santana Marcos Vinícius da Silva Computational Techniques for Analysis of Thermal Images of Pigs and Characterization of Heat Stress in the Rearing Environment AgriEngineering machine learning animal welfare thermal monitoring infrared thermography |
| title | Computational Techniques for Analysis of Thermal Images of Pigs and Characterization of Heat Stress in the Rearing Environment |
| title_full | Computational Techniques for Analysis of Thermal Images of Pigs and Characterization of Heat Stress in the Rearing Environment |
| title_fullStr | Computational Techniques for Analysis of Thermal Images of Pigs and Characterization of Heat Stress in the Rearing Environment |
| title_full_unstemmed | Computational Techniques for Analysis of Thermal Images of Pigs and Characterization of Heat Stress in the Rearing Environment |
| title_short | Computational Techniques for Analysis of Thermal Images of Pigs and Characterization of Heat Stress in the Rearing Environment |
| title_sort | computational techniques for analysis of thermal images of pigs and characterization of heat stress in the rearing environment |
| topic | machine learning animal welfare thermal monitoring infrared thermography |
| url | https://www.mdpi.com/2624-7402/6/3/183 |
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