Deep Learning Algorithms for Postharvest Quality Assessment: A New Sensing Methodology for Quail Eggs Freshness Estimation and Shelf-Life Revalidation

The Shelf-Life of Eggs is complex to determine since it is dependent on several parameters, including freshness. Available techniques to estimate the degradation of albumen and enlargement of the air cell are either destructive or not suitable for high-throughput applications. The aim of this research was to introduce a new approach to evaluate the air cell of quail eggs for freshness assessment as a fast, noninvasive, and nondestructive method. A new methodology was proposed by using a thermal microcamera and deep learning object detection algorithms. In this work, computer vision-based models were produced, referred to as “You Only Look Once” version 5, 7 (YOLOv5, YOLOv7) and EfficientDet. We tested the models in a new dataset composed of 60 eggs that were kept for 15 days after the labeled expiration label date. The validation of our methodology was performed by measuring the air cell area highlighted in the thermal images at the pixel level; thus, we compared the difference in the weight of eggs between the first day of storage and after 10 days under accelerated aging conditions. The statistical significance showed that the two variables (air cell and weight) were negatively correlated (R2 = 0.676). The deep learning models could predict freshness with F1 scores of 0.86, 0.90, and 0.86 for the YOLO (v5, v7), and EfficientDet models, respectively. The new methodology for freshness assessment demonstrated that the best model reclassified 38.33% of our testing dataset. Therefore, those expired eggs could have their expiration date extended for another 2 weeks from the original label date.