Abstract:For the rapid, non-destructive detection of moisture content in hazelnuts, hyperspectral image technology was utilized. A dataset comprising hyperspectral images of 200 hazelnuts covering wavelengths of 400~1 000 nm was collected, and the average spectral information of the hazelnuts image regions was extracted. The dataset was divided into sample validation and prediction sets using the K-S algorithm. Additionally, four preprocessing methods were applied to enhance spectra quality. Spectral features were extracted using a competitive adaptive weighting algorithm (CARS) and a successive projection method (SPA). Image texture features were obtained using the gray-scale co-occurrence matrix method (GLCM). Partial least squares regression (PLSR) and support vector regression (SVR) models were developed based on spectral features, image texture features, and the fusion of both to predict hazelnut moisture. The CARS and SPA algorithms effectively selected feature wavelengths and enhanced prediction performance. Furthermore, image features showed potential in predicting hazelnut moisture content, particularly when extracted from principal component images. The fusion of spectral and image features significantly enhances the accuracy of hazelnut moisture content prediction, especially when combining CARS-selected feature wavelengths with texture features from principal component images. The SVR model achieved impressive results, with an RMSECV of 0.03, RC of 0.97, RMSEP of 0.04, and RP of 0.96. This study highlights the effectiveness of hyperspectral image and texture features in predicting hazelnut moisture content, providing a novel approach for moisture detection in hazelnuts.