Combining AI with Automacted Quality Control in the Food Sector

Artificial intelligence (AI) and automated quality control (AQC) have transformed the food production industry by increasing productivity, uniformity, and safety requirements. AI-driven technologies, such as computer vision and machine learning algorithms, reduce waste and human error by enabling real-time monitoring, problem diagnosis, and predictive maintenance. Robots and sensor-based technologies are used in AQC to check for contamination, texture, and color to ensure that rules are being followed. In order to improve overall food safety and lower recall rates, deep learning (DL) models improve supply chain efficiency and traceability. AI-powered predictive analytics also aids in identifying possible risks before they have an impact on customers. These developments not only boost productivity and save expenses, but they also boost customer trust in food items. As AI develops and plays a bigger role in quality control, the food sector will become a more technologically sophisticated and dependable sector. The effects, advantages, and difficulties of AI-driven automation in food quality assurance are examined in this chapter. We provide a framework for optimization that can be used in the food industry's neural network classification to improve AQC by improving contaminant identification, defect detection, and product consistency. Among the DL models utilized in the proposed system are convolutional neural networks (CNNs) for image-based quality assessment and recurrent neural networks (RNNs) for anomaly detection and predictive maintenance. This newly constructed CNN-RNN model provides an accuracy of 96.28% with a minimal processing time duration of 24.63 ms.