Prediction of Sugar Levels and Freshness of Grapes from Multispectral Imaging Using Deep Learning

Post-pandemic, wellness and healthcare sector is vocal about balanced food consumption, inclusion of fresh fruits and regular exercise. Freshness and sugar contents of fruits are crucial to understand before their consumption. A normal RGB camera can provide information about the freshness of fruits based on the surface composition of images generated by the camera. Interestingly, the Multi-Spectral Imaging (MSI) provides information that is superior to a standard RGB camera, as it considers the NIR band. In this article, the authors have deliberated how MSI can be used to predict the sugar level and freshness in fruits, particularly grapes using modified EfficientNet-B0 as a Deep Learning (DL) model for the analysis. For predicting sugar level, the average value across the 5-fold cross-validation (cv) achieved by the DL model was RMSE of 2.68 (std 0.33) ◦Bx, MAE of 2.11 (0.34) ◦Bx, MAPE of 12% (2%) using MSI and RMSE of 7.76 (1.79) ◦Bx, MAE of 6.65 (1.69) ◦Bx, MAPE of 41% (9%) using RGB camera images. For freshness prediction, the DL model achieved an average 5-fold cv accuracy of 88.35% (9.71%) using MSI and 82.22% (7.9%) using RGB camera images. Results indicated that MSI can predict both the sugar level and freshness whereas RGB camera images can be used only for predicting the freshness but not the sugar level prediction of the grapes. The findings suggest that MSI offers a valuable and versatile solution for quality assessment in the fruit industry, enabling better dietary choices and healthcare regimes.