Detection of early bruises in Gongcheng persimmon using hyperspectral imaging

Abstract

This paper explores early bruise detection and bruise region extraction on the Gongcheng persimmon by hyperspectral imaging techniques in the visible and near-infrared (400–1000 nm) regions. In picking and storage, the surface of the Gongcheng persimmon will inevitably be slightly scratched due to impact, extrusion, or abrasion, which is not easy to detect by the naked eye. To find a suitable method to identify crisp persimmons in three stages (sound stage, 12 h, and 24 h after bruising), hyperspectral images of Gongcheng persimmon with wavelengths between 400 and 1000 nm were acquired using a hyperspectral imaging system. The effects of multivariate scatter correction (MSC), standard normal variate transform (SNV), normalization, and Savitzky–Golay (SG) smoothing to remove noise on the accuracy of the support vector machine (SVM) classification model were compared. A genetic algorithm (GA) was used to optimize the SVM parameters, and SPA was used to select the best wavelength for identifying the bruised part of the Gongcheng persimmon. The results show that using a successive projections algorithm (SPA) for characteristic wavelength selection can reduce redundant information and improve computing efficiency. The SVM model has the best classification effect after MSC preprocessing, and the classification accuracy can reach 100 %. The minimal noise fraction (MNF) method was used to successfully segment the bruised parts of Gongcheng persimmons according to the selected optimal wavelength, which indicated that MNF is an effective method to identify the bruised areas.