[Comparative study of transmission and reflection hyperspectral imaging technology for potato damage detection]

The randomly placed damage parts of potato will affect the detection accuracy, this paper used transmission and reflection hyperspectral imaging technology to acquire potato images of three directions(the damage part facing to the camera, back to the camera, side to the camera), and then processed the comparative study for damage detection. Independent component (IC) analysis was used to analyze the transmission and reflection hyperspectral images and to extract the features, the resulting char acteristics were used for the secondary IC analysis of the reflected images and the variable selection of the transmittance and re flectance spectroscopy. Finally, the potato injury qualitative recognition model was established based on the reflection images, the reflectance spectral and the transmittance spectral; Further optimization was done for high recognition accuracy of model, and secondary variable selection was carried out for the transmission spectrum by the Sub-window Permutation Analysis(SPA) and the optimal model for damage identification of potato randomly placed was established. The results of experiments show that the accuracy of the identification model based on the reflection image and the reflection spectrum is low, wherein the potato bruise based on the reflection images falls into the lowest recognition accuracy of 43. 10% when it is side to the camera; The accuracy of the model for identification based on the transmittance spectroscopy information is the highest, the recognition accuracy with the damage part facing and back to the camera is 100%t, and 99. 53% when it is side to the camera. The accuracy of the optimal model for identification based on the 3 kinds of transmittance spectroscopy information of randomly placed potato is 97. 39%. Then the application of transmission hyperspectral imaging technology could detect potato injury in any orientation, and the research can provide technical support for the online detection of potato quality.