A Study on Hyperspectral Non-Destructive Testing of Mechanical Damage in Yali Pears Using Linear Dimension Reduction...

To enable rapid, non-destructive identification of damage to Yali pears, this study proposes a detection method that integrates short-wave infrared hyperspectral imaging (1000-2500 nm), regularised linear discriminant analysis (R-LDA) and a lightweight convolutional neural network (CNN). The experiments utilised 180 Yali pears (60 each of healthy, with Mechanical scratch and Compression damage specimens) as training samples, whilst a further 300 independent fruits (150 healthy and 150 damaged) were used for fruit-level sorting validation. Following pre-processing using Principal Component Analysis (PCA) to eliminate multicollinearity, the classification performance of three feature extraction strategies-PCA, Independent Component Analysis (ICA) and R-LDA-was compared when combined with the same lightweight CNN. The results indicate that R-LDA's Fisher discrimination criterion (5.0275) and separation index (2.3543) were both superior to those of PCA and ICA, and its dimension-reduced features exhibited stronger inter-class separability. In pixel-level testing, the R-LDA + lightweight CNN achieved recognition accuracies of 98.60 per cent and 99.32 per cent for Compression damage and background, respectively, and 93.54 per cent and 96.33 per cent for Mechanical scratch and intact tissue, respectively. In a validation study involving the sorting of 300 independent fruits, this method achieved a recall rate of 100.00% for damaged fruits (zero false negatives), with precision and F1 scores of 97.00% and 97.09% respectively, both of which outperformed PCA combined with a lightweight CNN and ICA combined with a lightweight CNN. The above results indicate that the combination of R-LDA discriminant dimensionality reduction and a lightweight CNN can effectively reduce redundancy in hyperspectral data whilst maintaining a high damage detection rate, thereby providing a viable solution for the rapid, non-destructive detection of post-harvest damage in Yali pears.