A nondestructive intelligent approach to real-time evaluation of chicken meat freshness based on computer vision technique

In this study, the capability of a procedure based on combination of computer vision (CV) and artificial intelligence techniques examined for intelligent and nondestructive prediction of chicken meat freshness during the spoilage process at 4 degrees C. The proposed system comprises the following stages: capture images, image preprocessing, image processing, computing channels, feature extraction, feature selection by a hybrid of genetic algorithm (GA) and artificial neuronal network (ANN), and prediction by using ANN. The number of neurons in input layer was determined 33 (selected features) and freshness used as the output. The ideal ANN model was obtained with 33-10-1 topology. The high performance of the model was provided with a correlation coefficient of 0.98734 and MSE of 0.002045. The encouraging results of the current study obviously indicated the high potential of CV-based system combined with an intelligence method as a smart, nondestructive, and reliable technique for online evaluation of chicken meat freshness. Practical application Diagnosis and estimation of chicken meat freshness are considered a significant concern in meat quality for consumers. Computer Vision as a novel nondestructive technique can be utilized to evaluate the quality of products. We present the potential of computer vision-based method as a smart, nondestructive, and reliable method for online prediction of the freshness of chicken meat.