Quantitative study on impact damage of yellow peach based on hyperspectral image information combined with spectral information

Abstract

Yellow peaches are graded based on their degree of impact damage to reduce economic losses, and the values of mechanical parameters can be used to characterize the degree of impact damage of them. In this paper, the combined hyperspectral image features and spectral features method was proposed to predict the values of mechanical parameters of yellow peaches. A collision device based on the pendulum principle was built to acquire the mechanical parameters which were maximum force, absorbed energy and average pressure. A hyperspectral imaging system was used to acquire the images of the bruised yellow peaches and the spectral information and color characteristics of images were extracted, respectively. The damage areas of yellow peaches were obtained based on the combined principal component analysis (PCA) and threshold segmentation method. Texture features of the characteristic wavelength images were extracted based on the grey-level co-generation matrix (GLCM). The PLSR models of mechanical parameters were built based on spectral information, image information, and spectral information combined with image information, respectively. The results show that the PLSR models based on spectral information combined with image information are best in prediction the values of damage area and maximum force. However, the PLSR models have not achieved satisfactory performance in prediction the values of absorbed energy and average pressure. To improve the performance of PLSR models of absorbed energy and average pressure, the feature wavelengths were selected by CARS, and the correlation analysis was used between image information and mechanical parameters. The PLSR models were built based on feature spectral information, image feature information, and feature spectral information combined with image feature information, respectively. The results show that the accuracy of all mechanical parameters is significantly improved by the PLSR models based on the feature spectral information combined with the image feature information. The prediction set correlation coefficient (RP) and the root mean square error (RMSEP) of the damaged area, absorbed energy, maximum force and average pressure are 0.928 and 86.632 mm2, 0.826 and 1.469 J, 0.924 and 61.765 N, 0.815 and 0.050 MPa, respectively. The results of the study not only provide a theoretical basis for postharvest grading of fruit, but also provide a reference for analyzing the mechanical properties of fruit.