SOLARIA-SensOr-driven resiLient and adaptive monitoRIng of farm Animals
Sensor-enabled big data and artificial intelligence platforms have the potential to address global socio-economic trends related to the livestock production sector through advances in the digitization of precision livestock farming. The increased interest in animal welfare, the likely reduction in t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/13/2/436 |
| _version_ | 1827759306111451136 |
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| author | Suresh Neethirajan |
| author_facet | Suresh Neethirajan |
| author_sort | Suresh Neethirajan |
| collection | DOAJ |
| description | Sensor-enabled big data and artificial intelligence platforms have the potential to address global socio-economic trends related to the livestock production sector through advances in the digitization of precision livestock farming. The increased interest in animal welfare, the likely reduction in the number of animals in relation to population growth in the coming decade and the growing demand for animal proteins pose an acute challenge to prioritizing animal welfare on the one hand, while maximizing the efficiency of production systems on the other. Current digital approaches do not meet these challenges due to a lack of efficient and lack of real-time non-invasive precision measurement technologies that can detect and monitor animal diseases and identify resilience in animals. In this opinion review paper, I offer a critical view of the potential of wearable sensor technologies as a unique and necessary contribution to the global market for farm animal health monitoring. To stimulate the sustainable, digital and resilient recovery of the agricultural and livestock industrial sector, there is an urgent need for testing and developing new ideas and products such as wearable sensors. By validating and demonstrating a fully functional wearable sensor prototype within an operational environment on the livestock farm that includes a miniaturized animal-borne biosensor and an artificial intelligence (AI)-based data acquisition and processing platform, the current needs, which have not yet been met, can be fulfilled. The expected quantifiable results from wearable biosensors will demonstrate that the digitization technology can perform acceptably within the performance parameters specified by the agricultural sector and under operational conditions, to measurably improve livestock productivity and health. The successful implementation of the digital wearable sensor networks would provide actionable real-time information on animal health status and can be deployed directly on the livestock farm, which will strengthen the green and digital recovery of the economy due to its significant and innovative potential. |
| first_indexed | 2024-03-11T09:18:12Z |
| format | Article |
| id | doaj.art-ba02a0db1036460abd050f0c6ff40754 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T09:18:12Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-ba02a0db1036460abd050f0c6ff407542023-11-16T18:31:09ZengMDPI AGAgriculture2077-04722023-02-0113243610.3390/agriculture13020436SOLARIA-SensOr-driven resiLient and adaptive monitoRIng of farm AnimalsSuresh Neethirajan0Farmworx Research Institute, Van der Waals straat, 6706 JS Wageningen, The NetherlandsSensor-enabled big data and artificial intelligence platforms have the potential to address global socio-economic trends related to the livestock production sector through advances in the digitization of precision livestock farming. The increased interest in animal welfare, the likely reduction in the number of animals in relation to population growth in the coming decade and the growing demand for animal proteins pose an acute challenge to prioritizing animal welfare on the one hand, while maximizing the efficiency of production systems on the other. Current digital approaches do not meet these challenges due to a lack of efficient and lack of real-time non-invasive precision measurement technologies that can detect and monitor animal diseases and identify resilience in animals. In this opinion review paper, I offer a critical view of the potential of wearable sensor technologies as a unique and necessary contribution to the global market for farm animal health monitoring. To stimulate the sustainable, digital and resilient recovery of the agricultural and livestock industrial sector, there is an urgent need for testing and developing new ideas and products such as wearable sensors. By validating and demonstrating a fully functional wearable sensor prototype within an operational environment on the livestock farm that includes a miniaturized animal-borne biosensor and an artificial intelligence (AI)-based data acquisition and processing platform, the current needs, which have not yet been met, can be fulfilled. The expected quantifiable results from wearable biosensors will demonstrate that the digitization technology can perform acceptably within the performance parameters specified by the agricultural sector and under operational conditions, to measurably improve livestock productivity and health. The successful implementation of the digital wearable sensor networks would provide actionable real-time information on animal health status and can be deployed directly on the livestock farm, which will strengthen the green and digital recovery of the economy due to its significant and innovative potential.https://www.mdpi.com/2077-0472/13/2/436digital agricultureprecision livestock farmingsmart farmingartificial intelligencesensorsbig data |
| spellingShingle | Suresh Neethirajan SOLARIA-SensOr-driven resiLient and adaptive monitoRIng of farm Animals Agriculture digital agriculture precision livestock farming smart farming artificial intelligence sensors big data |
| title | SOLARIA-SensOr-driven resiLient and adaptive monitoRIng of farm Animals |
| title_full | SOLARIA-SensOr-driven resiLient and adaptive monitoRIng of farm Animals |
| title_fullStr | SOLARIA-SensOr-driven resiLient and adaptive monitoRIng of farm Animals |
| title_full_unstemmed | SOLARIA-SensOr-driven resiLient and adaptive monitoRIng of farm Animals |
| title_short | SOLARIA-SensOr-driven resiLient and adaptive monitoRIng of farm Animals |
| title_sort | solaria sensor driven resilient and adaptive monitoring of farm animals |
| topic | digital agriculture precision livestock farming smart farming artificial intelligence sensors big data |
| url | https://www.mdpi.com/2077-0472/13/2/436 |
| work_keys_str_mv | AT sureshneethirajan solariasensordrivenresilientandadaptivemonitoringoffarmanimals |