Quality prediction using artificial intelligence

Problem addressed

Agri-food processes are subject to variability and uncertainty. Relying solely on end-product controls makes it difficult to respond in time, increasing costs and food waste.

What we do and how we do it

• Collection and structuring of production process data.
• Development of AI-based predictive quality models.
• Validation using historical data and pilot-scale trials.
• Integration into process control systems to anticipate deviations.
• Training and support to facilitate interpretation and use of results.

Key benefits

• Real-time quality prediction.
• Reduced incidents, losses, and costs.
• Process optimisation and improved performance.
• Greater anticipation capacity and better decision-making.