Hyperspectral imaging and machine learning for quality assessment of apples with different bagging types.

Abstract

This study examined how different bagging types (unbagged, mesh-bagged, and paper-bagged) affect the internal and external quality of fruits. Spectral images of 307 apples were collected using a visible-near infrared hyperspectral imaging system, and differences in color, size, firmness, soluble solids content (SSC), and aroma were analyzed. The effectiveness of various algorithms for extracting effective wavelengths was compared. Developed apple quality inspection models using various machine learning algorithms, achieving the best-performing model, CARS-MLR, with all indicators having an R²_p greater than 0.75. The results revealed significant differences in quality indicators based on bagging type, with paper-bagged apples showing the best overall quality-brighter color, moderate size and firmness, and rich aroma. This suggests that hyperspectral imaging, feature selection algorithms and machine learning methods, is an effective approach for non-destructive quality assessment of apples. This research offers valuable insights for assessing the quality of other fruits with different bagging types.