Detection of hidden bruises on kiwifruit using hyperspectral imaging combined with deep learning

During harvesting, transportation and storage of kiwifruit, the flesh is often bruised by collision or compression. However, the bruises in kiwifruit are extremely difficult to recognise by naked eyes and are called hidden bruises. Accordingly, a fast method for detecting hidden bruises in kiwifruit was developed in this study based on hyperspectral imaging (HSI) coupled with deep learning. The spectral range (924-1277 nm) and feature wavelengths (928.19, 1051.03 and 1190.47 nm) sensitive to hidden bruises in kiwifruit were selected using the principal component analysis (PCA). Subsequently, three-channel images (Dataset 1), grayscale images (Dataset 2) and pseudo-colour images (Dataset 3) were generated according to the images of feature wavelengths of the kiwifruit. The YOLOv5s model for detecting the hidden bruised areas of the kiwifruit was developed using these three datasets. The results showed that the YOLOv5s detection model performed best at Dataset 1, and the values of Precision, Recall, F1, mAP and FNR of this model were 98.25%, 97.50%, 97.87%, 99.12% and 2.50% respectively. The study showed that HSI technology combined with the YOLOv5s model can effectively detect hidden bruises in kiwifruit, providing references for detecting hidden bruises in other fruit. In this study, hyperspectral image data of the kiwifruit were acquired. Subsequently, feature wavelengths (924.87, 1051.03 and 1190.47 nm) sensitive to hidden bruising within the kiwifruit were determined. Finally, an accurate recognition model for the hidden bruised areas of kiwifruit was established based on the greyscale images at the feature wavelengths. image