Early detection of apple bruises using spectral-spatial enhanced 3D CNN and region-based hyperspectral analysis

Hyperspectral imaging (HSI) has revolutionized the non-destructive detection of fruit defects by integrating spectral and spatial information. However, detecting early-stage minor bruises in apples remains challenging due to weak hyperspectral signals, high data dimensionality, and low computational efficiency. To address these issues, this study proposes a novel hyperspectral apple bruise detection network, termed 3D-HDI. The model constructs a backbone network using multiple 3D convolutions and integrates a feature enhancement module with a path aggregation network to amplify spectral signals and improve damage differentiation. Furthermore, it replaces pixel-by-pixel classification with a region-based detection head, significantly enhancing computational efficiency and accuracy. Experimental results demonstrate that the proposed model achieves a higher recognition rate (96.25%) while maintaining comparable detection efficiency, outperforming traditional classification networks such as 3D-EfficientNet, 3D-MobileNet, and 3D-AlexNet. This research advances the application of HSI and deep learning in fruit quality assessment, providing a robust solution for early-stage bruises detection.