Non-destructive detection of chicken freshness based on multiple features image fusion and support vector machine

With the rise in global meat consumption and chicken becoming a principal source of white meat, methods for efficiently and accurately determining the freshness of chicken are of increasing importance, since traditional detection methods fail to satisfy modern production needs. A non-destructive method based on machine vision and machine learning technology was proposed for detecting chicken breast freshness. A self-designed machine vision system was first used to collect images of chicken breast samples stored at 4°C for 1-7 d. The Region of Interest (ROI) for each image was then extracted and a total of 1254 ROI images were obtained. Six color features were extracted from two different color spaces RGB (red, green, blue) and HSI (hue, saturation, intensity). Six main Gray Level Co-occurrence Matrix (GLCM) texture feature parameters were also calculated from four directions. Principal Component Analysis (PCA) was used to reduce the dimension of these 30 extracted feature parameters for multiple features image fusion. Four principal components were taken as input and chicken breast freshness level as output. A 10-fold cross-validation was used to partition the dataset. Four machine learning methods, Particle Swarm Optimization–Support Vector Machine (PSO-SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Naive Bayes Classifier (NBC), were used to establish a chicken breast freshness level prediction model. Among these, SVM had the best prediction effect with prediction accuracy reaching 0.9867. The results proved the feasibility of using a detection method based on multiple features image fusion and machine learning, providing a theoretical reference for the non-destructive detection of chicken breast freshness.