A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary Study
Coccidiosis is still one of the major parasitic infections in poultry. It is caused by protozoa of the genus <i>Eimeria</i>, which cause concrete economic losses due to malabsorption, bad feed conversion rate, reduced weight gain, and increased mortality. The greatest damage is registere...
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| Format: | Article |
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MDPI AG
2020-04-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/10/4/747 |
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| author | Federica Borgonovo Valentina Ferrante Guido Grilli Riccardo Pascuzzo Simone Vantini Marcella Guarino |
| author_facet | Federica Borgonovo Valentina Ferrante Guido Grilli Riccardo Pascuzzo Simone Vantini Marcella Guarino |
| author_sort | Federica Borgonovo |
| collection | DOAJ |
| description | Coccidiosis is still one of the major parasitic infections in poultry. It is caused by protozoa of the genus <i>Eimeria</i>, which cause concrete economic losses due to malabsorption, bad feed conversion rate, reduced weight gain, and increased mortality. The greatest damage is registered in commercial poultry farms because birds are reared together in large numbers and high densities. Unfortunately, these enteric pathologies are not preventable, and their diagnosis is only available when the disease is full-blown. For these reasons, the preventive use of anticoccidials—some of these with antimicrobial action—is a common practice in intensive farming, and this type of management leads to the release of drugs in the environment which contributes to the phenomenon of antibiotic resistance. Due to the high relevance of this issue, the early detection of any health problem is of great importance to improve animal welfare in intensive farming. Three prototypes, previously calibrated and adjusted, were developed and tested in three different experimental poultry farms in order to evaluate whether the system was able to identify the coccidia infection in intensive poultry farms early. For this purpose, a data-driven machine learning algorithm was built, and specific critical values of volatile organic compounds (VOCs) were found to be associated with abnormal levels of oocystis count at an early stage of the disease. This result supports the feasibility of building an automatic data-driven machine learning algorithm for an early warning of coccidiosis. |
| first_indexed | 2024-03-10T20:14:13Z |
| format | Article |
| id | doaj.art-278ff4db68e44f4a829cbec8ba8fa3d2 |
| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2024-03-10T20:14:13Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj.art-278ff4db68e44f4a829cbec8ba8fa3d22023-11-19T22:38:38ZengMDPI AGAnimals2076-26152020-04-0110474710.3390/ani10040747A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary StudyFederica Borgonovo0Valentina Ferrante1Guido Grilli2Riccardo Pascuzzo3Simone Vantini4Marcella Guarino5Department of Environmental Science and Policy, Università degli Studi di Milano, 20133 Milan, ItalyDepartment of Environmental Science and Policy, Università degli Studi di Milano, 20133 Milan, ItalyDepartment of Veterinary Medicine, Università degli Studi di Milano, 20133 Milan, ItalyNeuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, 20133 Milan, ItalyMOX-Department of Mathematics, Politecnico di Milano, 20133 Milan, ItalyDepartment of Environmental Science and Policy, Università degli Studi di Milano, 20133 Milan, ItalyCoccidiosis is still one of the major parasitic infections in poultry. It is caused by protozoa of the genus <i>Eimeria</i>, which cause concrete economic losses due to malabsorption, bad feed conversion rate, reduced weight gain, and increased mortality. The greatest damage is registered in commercial poultry farms because birds are reared together in large numbers and high densities. Unfortunately, these enteric pathologies are not preventable, and their diagnosis is only available when the disease is full-blown. For these reasons, the preventive use of anticoccidials—some of these with antimicrobial action—is a common practice in intensive farming, and this type of management leads to the release of drugs in the environment which contributes to the phenomenon of antibiotic resistance. Due to the high relevance of this issue, the early detection of any health problem is of great importance to improve animal welfare in intensive farming. Three prototypes, previously calibrated and adjusted, were developed and tested in three different experimental poultry farms in order to evaluate whether the system was able to identify the coccidia infection in intensive poultry farms early. For this purpose, a data-driven machine learning algorithm was built, and specific critical values of volatile organic compounds (VOCs) were found to be associated with abnormal levels of oocystis count at an early stage of the disease. This result supports the feasibility of building an automatic data-driven machine learning algorithm for an early warning of coccidiosis.https://www.mdpi.com/2076-2615/10/4/747Poultryearly warning systemVOCscoccidiosisdata-driven machine learning algorithm |
| spellingShingle | Federica Borgonovo Valentina Ferrante Guido Grilli Riccardo Pascuzzo Simone Vantini Marcella Guarino A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary Study Animals Poultry early warning system VOCs coccidiosis data-driven machine learning algorithm |
| title | A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary Study |
| title_full | A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary Study |
| title_fullStr | A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary Study |
| title_full_unstemmed | A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary Study |
| title_short | A Data-Driven Prediction Method for an Early Warning of Coccidiosis in Intensive Livestock Systems: A Preliminary Study |
| title_sort | data driven prediction method for an early warning of coccidiosis in intensive livestock systems a preliminary study |
| topic | Poultry early warning system VOCs coccidiosis data-driven machine learning algorithm |
| url | https://www.mdpi.com/2076-2615/10/4/747 |
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