INTERPORC: Artificial intelligence at the service of the white pig value chain
The first is animal health, where AI allows for anticipating problems through the early detection of symptoms or behavioral changes. The second is animal welfare, thanks to continuous monitoring systems that enable objective assessments of animal conditions and intervention before stressful situations arise. The third is precision nutrition, adjusting feed to the animals' actual needs at each stage. The fourth area is process efficiency, both on the farm and in processing plants, optimizing resources, workflows, and maintenance. And the fifth is traceability and food safety, strengthening control throughout the entire supply chain.
All of this is possible because artificial intelligence allows us to collect data, analyze it in real time and turn it into practical decisions, moving from reactive management to predictive management, with clear improvements in efficiency, sustainability and transparency.
These benefits are particularly evident in the daily operations of farms and in industrial processes.
In many farms, artificial intelligence is already being used for early disease detection through image or audio analysis, for intelligent environmental control of facilities, or for precision nutrition.
As examples, we can talk about tools that aim to identify respiratory diseases early by analyzing the cough of animals.
In animal welfare, there are platforms that work on the continuous monitoring of animal behavior and condition, allowing us to evaluate welfare objectively and permanently, not just at specific times.
In the field of nutrition, there are already projects that seek to optimize diets to reduce environmental impact and improve the use of resources through the use of data and predictive models.
In industry, artificial intelligence also provides clear value in improving, for example, hygiene control and food safety in processing plants through hyperspectral vision technologies.
Today, artificial intelligence is already a reality in the pig sector because, although its implementation is not yet homogeneous, there are farms and industries with a high level of digitalization, which already work with fully operational sensors, data analysis platforms and predictive models.
In Spain there are initiatives whose objective is to test and validate solutions based on artificial intelligence in areas such as health, food and animal welfare, for their subsequent application in commercial farms.
At the same time, more traditional models coexist, so the deployment of these technologies will be gradual. The challenge is to scale these solutions and adapt them to the diversity of the sector.
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