A Comprehensive Review of Advanced Deep Learning Approaches for Food Freshness Detection

Abstract

This comprehensive review highlights the significant strides made in the field of food freshness detection through the integration of deep learning and imaging techniques. By leveraging advanced neural networks, researchers have developed innovative methodologies that enhance the accuracy and efficiency of freshness monitoring. The fusion of various imaging modalities, with sophisticated deep learning algorithms has enabled more precise detection of quality attributes and spoilage indicators. This multidimensional approach not only improves the reliability of freshness assessments but also provides a more holistic view of condition of the food. Additionally, the review underscores the growing potential for these technologies to be applied in real-time monitoring systems, offering valuable insights for both producers and consumers. The advancements discussed pave the way for future research and development, emphasizing the need for continued innovation in integrating these technologies to address the challenges of food safety and quality assurance in an increasingly complex and dynamic market.

Graphical Abstract