An intelligent machine vision-based smartphone app for beef quality evaluation

Abstract

Beef tenderness is the most important attribute correlated with beef quality, consumer satisfaction, and purchasing decisions. Nowadays, a rapid, non-invasive, and non-destructive evaluation and prediction of beef tenderness and quality from fresh product attributes is desired in industries, laboratories, and markets dealing with beef handling, processing, analyzing, and buy and sell. In this study, a new machine vision-based smartphone app was developed and verified for the first time in order to predict beef tenderness from fresh beef image captured under uncontrolled conditions. In order to eliminate the effects of uncontrolled imaging conditions, an illumination-, rotation-, scale-, and translation-invariant image processing algorithm was developed so that a common user can easily capture the image of the beef sample with more degree of freedom in terms of luminance, rotation, scale, and translation with no worries about the accuracy of the results. The obtained preprocessed image textural features were well correlated with instrumental data obtained using Warner-Bratzler shear force measurement through artificial neural network technique. The developed android app was installed on a LG G4 H815 smartphone and its performance was assessed using thirty unseen beef samples. The probability of occurrence of 2-D correlation coefficients obtained from the analyses of all the beef samples subjected to the image processing algorithm showed the average probability of 0.92, which strongly supported the robustness of the developed algorithm. The best obtained neural network model could predict the tenderness values with mean absolute percentage error (MAPE) of 3.28% and coefficient of determination (R2) of 0.97. The app promisingly predicted the beef tenderness values of the unseen samples with mean squared error (MSE) of 3.34, MAPE of 3.74%, and R2 of 0.99. Accordingly, the developed app can be a low-cost and user-friendly tool for predicting beef tenderness and quality from its real-world image.