Detection of Coal and Gangue Based on Improved YOLOv8.

To address the lightweight and real-time issues of coal sorting detection, an intelligent detection method for coal and gangue, Our-v8, was proposed based on improved YOLOv8. Images of coal and gangue with different densities under two diverse lighting environments were collected. Then the Laplacian image enhancement algorithm was proposed to improve the training data quality, sharpening contours and boosting feature extraction; the CBAM attention mechanism was introduced to prioritize crucial features, enhancing more accurate feature extraction ability; and the EIOU loss function was added to refine box regression, further improving detection accuracy. The experimental results showed that Our-v8 for detecting coal and gangue in a halogen lamp lighting environment achieved excellent performance with a mean average precision (mAP) of 99.5%, was lightweight with FLOPs of 29.7, Param of 12.8, and a size of only 22.1 MB. Additionally, Our-v8 can provide accurate location information for coal and gangue, making it ideal for real-time coal sorting applications.

Protein content prediction of rice grains based on hyperspectral imaging.

This study utilized hyperspectral imaging technology combined with mathematical modeling methods to predict the protein content of rice grains. Firstly, the Kjeldahl method was used to determine the protein content of rice grains, and different preprocessing techniques were applied to the spectral information. Then, a prediction model for rice grain protein content was developed by combining the spectral data with the protein content. After performing multiplicative scatter correction (MSC) preprocessing and selecting feature wavelengths based on successive projections algorithm (SPA), the multivariate linear regression (MLR) model showed the best prediction performance, with a calibration set R2C of 0.9393, a validation set R2V of 0.8998, an RMSEV of 0.1725, and an RPD of 3.16. Finally, the quantitative protein content model was mapped pixel by pixel to visualize the distribution of rice protein, providing possibilities for non-destructive protein content detection.

Growth period determination and color coordinates visual analysis of tomato using hyperspectral imaging technology.

Growth period determination and color coordinates prediction are essential for comparing postharvest fruit quality. This paper proposes a tomato growth period judgment and color coordinates prediction model based on hyperspectral imaging technology. It utilizes the most effective color coordinates prediction model to obtain a color visual image. Firstly, hyperspectral images were taken of tomatoes at different growth periods (green-ripe, color-changing, half-ripe, and full-ripe), and color coordinates (L*, a*, b*, c, h) were obtained using a colorimeter. The sample set was divided by the sample set partitioning based on joint X-Y distances (SPXY). The support vector machine (SVM), K-nearest neighbors (KNN), and linear discriminant analysis (LDA) were used to discriminate growth period. Results show that the LDA model has the best prediction effect with a prediction set accuracy of 93.1%. In addition, effective wavelengths were selected using competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), and chromaticity prediction models were established using partial least squares regression (PLSR), multiple linear regression (MLR), principal component regression (PCR) and support vector machine regression (SVR) Finally, the color of each pixel of the tomato is calculated using the optimal model, generating a visual distribution image of the color coordinate. The results showed that hyperspectral imaging can non-destructively detect tomatoes' growth stage and color coordinates, providing great significance for designing a tomato quality grading system.

Spectral and image analysis of hyperspectral data for internal and external quality assessment of peach fruit.

Hyperspectral imaging was attempted to evaluate the internal and external quality of 'Feicheng' peach by providing the spectral and spatial data simultaneously. Mask-image was created from hyperspectral image at 810 nm and used to segment the fruit region where the average spectrum, after area normalization, was obtained for soluble solids content (SSC) and firmness evaluation. Pixel size and area were used for diameter and weight estimation. Then effective wavelengths were selected by competitive adaptive reweighted sampling (CARS) and random frog (RF), and employed to develop multiple linear regression (MLR) models. The more effective prediction performances emerged from CARS-MLR model withRV2 = 0.841, RMSEV = 0.546, RPD = 2.51 for SSC andRV2 = 0.826, RMSEV = 1.008, RPD = 2.401 for firmness, followed by creating pixel-wise and object-wise visualization maps for quantifying SSC and firmness. Furthermore, peach diameter was estimated by calculating the minimum bounding rectangle with an average percentage error of 1.01 %, and the MLR model forweightpredictionachieveda good performance ofRV2 = 0.957, RMSEV = 9.203, and RPD = 4.819. The overall results showed that hyperspectral imaging could be used as an effective and non-destructive tool for evaluating the internal and external quality attributes of 'Feicheng' peach, and provided a holistic approach to develop online grading systems for quality tiers identification.

A new quantitative index for the assessment of tomato quality using Vis-NIR hyperspectral imaging.

The quality of tomatoes is usually predicted by measuring a single index, rather than a comprehensive index. To find a comprehensive index, visible and near infrared (Vis-NIR) hyperspectral imaging was used for capturing the images of three varieties of tomatoes, and twelve quality indexes were measured as the reference standards. The changing trends and correlations of different indexes were analyzed, and comprehensive quality index (CQI) was proposed through factor analysis. The characteristic wavelengths were selected by successive projection algorithm (SPA) based on the hyperspectral data, which was used to establish three regression models for CQI prediction. The result indicated that MLR achieved good performance withRV2 = 0.87, RMSEV = 1.33 and RPD = 2.58. After that, spatial distribution map was generated to visualize the CQI in tomato fruit. This study indicated that the comprehensive quality of tomatoes can be predicted non-destructively based on hyperspectral imaging and chemometrics, determining the optimal harvesting period.

Application of hyperspectral imaging for spatial prediction of soluble solid content in sweet potato.

Visible and near infrared (Vis-NIR) hyperspectral imaging was used for fast detection and visualization of soluble solid content (SSC) in 'Beijing 553' and 'Red Banana' sweet potatoes. Hyperspectral images were acquired from 420 ROIs of each cultivar of sliced sweet potatoes. There were 8 and 10 outliers removed from 'Beijing 553' and 'Red Banana' sweet potatoes by Monte Carlo partial least squares (MCPLS). The optimal spectral pretreatments were determined to enhance the performance of the prediction model. Successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) were employed to select characteristic wavelengths. SSC prediction models were developed using partial least squares regression (PLSR), support vector regression (SVR) and multivariate linear regression (MLR). The more effective prediction performances emerged from the SPA-SVR model with R p 2 of 0.8581, RMSEP of 0.2951 and RPDp of 2.56 for 'Beijing 553' sweet potato, and the CARS-MLR model with R p 2 of 0.8153, RMSEP of 0.2744 and RPDp of 2.09 for 'Red Banana' sweet potato. Spatial distribution maps of SSC were obtained in a pixel-wise manner using SPA-SVR and CARS-MLR models for quantifying the SSC level in a simple way. The overall results illustrated that Vis-NIR hyperspectral imaging was a powerful tool for spatial prediction of SSC in sweet potatoes.

Determination of the bruise degree for cherry using Vis-NIR reflection spectroscopy coupled with multivariate analysis.

Determination and classification of the bruise degree for cherry can improve consumer satisfaction with cherry quality and enhance the industry's competiveness and profitability. In this study, visible and near infrared (Vis-NIR) reflection spectroscopy was used for identifying bruise degree of cherry in 350-2500 nm. Sampling spectral data were extracted from normal, slight and severe bruise samples. Principal component analysis (PCA) was implemented to determine the first few principal components (PCs) for cluster analysis among samples. Optimal wavelengths were selected by loadings of PCs from PCA and successive projection algorithm (SPA) method, respectively. Afterwards, these optimal wavelengths were empolyed to establish the classification models as inputs of least square-support vector machine (LS-SVM). Better performance for qualitative discrimination of the bruise degree for cherry was emerged in LS-SVM model based on five optimal wavelengths (603, 633, 679, 1083, and 1803 nm) selected directly by SPA, which showed acceptable results with the classification accuracy of 93.3%. Confusion matrix illustrated misclassification generally occurred in normal and slight bruise samples. Furthermore, the latent relation between spectral property of cherries in varying bruise degree and its firmness and soluble solids content (SSC) was analyzed. The result showed both colour, firmness and SSC were consistent with the Vis-NIR reflectance of cherries. Overall, this study revealed that Vis-NIR reflection spectroscopy integrated with multivariate analysis can be used as a rapid, intact method to determine the bruise degree of cherry, laying a foundation for cherry sorting and postharvest quality control.