Interpretation of Hyperspectral Images Using Integrated Gradients to Detect Bruising in Lemons

Abstract

Lemons are a popular citrus fruit known for their medicinal and nutritional properties. However, fresh lemons are vulnerable to mechanical damage during transportation, with bruising being a common issue. Bruising reduces the fruit’s shelf life and increases the risk of bacterial and fungal contamination, leading to economic losses. Furthermore, discoloration typically occurs after 24 h, so it is crucial to detect bruised fruits promptly. This paper proposes a novel method for detecting bruising in lemons using hyperspectral imaging and integrated gradients. A dataset of hyperspectral images was captured in the wavelength range of 400–1100 nm for lemons that were sound and artificially bruised (8 and 16 h after bruising), with three distinct classes of images corresponding to these conditions. The dataset was divided into three subsets i.e., training (70%), validation (20%), and testing (10%). Spatial–spectral data were analyzed using three 3D-convolutional neural networks: ResNetV2, PreActResNet, and MobileNetV2 with parameter sizes of 242, 176, and 9, respectively. ResNetV2 achieved the highest classification accuracy of 92.85%, followed by PreActResNet at 85.71% and MobileNetV2 at 83.33%. Our results demonstrate that the proposed method effectively detects bruising in lemons by analyzing darker pixels in the images, subsequently confirming the presence of bruised areas through their spatial distribution and accumulation. Overall, this study highlights the potential of hyperspectral imaging and integrated gradients for detecting bruised fruits, which could help reduce food waste and economic losses.