Citrus fruits were sorted based on external qualities, such as size, weight, and color, and internal qualities, such as soluble solid content (SSC), acidity, and firmness. Visible and near-infrared (VNIR) hyperspectral imaging techniques were used as rapid and nondestructive techniques for determining the internal quality of fruits. The applicability of the VNIR hyperspectral imaging technique for predicting the SSC in citrus fruits was evaluated in this study. A VNIR hyperspectral imaging system with a wavelength range of 400–1000 nm and 100 W light source was used to acquire hyperspectral images from citrus fruits in two orientations (i.e., stem and calyx ends). The SSC prediction model was developed using partial least-squares regression (PLSR). Spectrum preprocessing, effective wavelength selection through competitive adaptive reweighted sampling (CARS), and outlier detection were used to improve the model performance. The performance of each model was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). In the present study, the PLSR model was developed using only a citrus cultivar. The SSC prediction CARS-PLSR model with outliers removed exhibited R² and RMSE values of approximatively 0.75 and 0.56 °Brix, respectively. The results of this study are expected to be useful in similar fields such as agricultural and food post-harvest management, as well as in the development of an online system for determining the SSC of citrus fruits.

Soluble solid content (SSC) is a key indicator of strawberry quality. Conventional SSC measurement methods are destructive and impractical for large-scale applications. Therefore, this study developed a region-based hyperspectral imaging (HSI) and lightweight one-dimensional convolutional neural network (1D CNN) framework for nondestructive SSC prediction in strawberries. To evaluate spatial effects on predictive accuracy, the fruit surface was segmented into five groups (G1–G5). Three spectral preprocessing methods were applied with partial least squares regression and five convolutional neural network (CNN) architectures, including a simplified VGG-CNN. Larger regions generally improved prediction performance; however, the 50% region (G2) and 75% region (G3) achieved comparable performance to the full region, reducing data requirements. The simplified VGG-CNN model with SNV outperformed other models, exhibiting high accuracy with reduced computational cost, supporting its potential integration into portable and real-time sensing systems. The proposed approach can contribute to improved postharvest quality control and enhanced consumer confidence in strawberry products.

External defects considerably reduce the quality, consumer acceptance, and market value of citrus fruits. Therefore, a rapid and reliable, non-destructive inspection method is necessary for postharvest processing. In this study, a non-destructive approach for external defect classification of citrus fruits is developed by combining visible–near infrared hyperspectral imaging (HSI) with effective wavelength selection (EWS) algorithms. First, 1702 spectral samples of normal and defective regions on citrus fruit surfaces were collected. A partial least squares discriminant analysis (PLS-DA) model was developed using the full wavelength range (400–1000 nm), which achieved 99.02% prediction accuracy. Four EWS algorithms—weighted regression coefficients, variable importance in projection, sequential forward selection (SFS(5, 10, 15)), and random frog—were evaluated for optimal spectral dimensionality and computational efficiency. The SFS15-PLS-DA model, which selected 15 optimal variables out of the initial 300 and used maximum normalization preprocessing, achieved the highest prediction accuracy of 99.80%. This model demonstrated near-perfect classification while reducing the total number of wavelengths by 95.0% (from 300 to 15 wavelengths). Further, a pixel-wise image classification algorithm was implemented using the optimal model, which effectively detected physical damage, pest infestation, and fungal decay. These results demonstrate that combining HSI with EWS enables compact, interpretable, and high-performance models suitable for real-time postharvest sorting. This approach has strong potential to enhance automation, speed, and reliability in commercial citrus quality assessment.