As living standards rise, people have higher requirements for the quality of duck eggs. The quality of duck eggs is related to their origin. Thus, the origin traceability and identification of duck eggs are crucial for protecting the rights and interests of consumers and preserving food safety. As the world’s largest producer and consumer of duck eggs, China’s duck egg market suffers from a severe lack of duck egg traceability and rapid origin identification technology. As a result, a large number of duck eggs from other regions are sold as products from well-known brands, which seriously undermines the rights and interests of consumers and is not conducive to the sound development of the duck egg industry. To address the above issues, this study collected visible/near-infrared spectral data online from duck eggs of three distinct origins. To reduce noise in the spectral data, various pre-processing algorithms, including MSC, SNV, and SG, were employed to process the spectral data of duck eggs in the range of 400–1100 nm. Meanwhile, CARS and SPA were used to select feature variables that reflect the origin of duck eggs. Finally, classification models of duck egg origin were developed based on RF, SVM, and CNN, achieving the highest accuracy of 97.47%, 98.73%, and 100.00%, respectively. To promote the technology’s implementation in the duck egg industry, an online sorting device was built for duck eggs, which mainly consists of a mechanical drive device, spectral software, and a control system. The online detection performance of the machine was validated using 90 duck eggs, and the final detection accuracy of the RF, SVM, and CNN models was 90%, 91.11%, and 94.44%, with a detection speed of 0.1 s, 0.3 s, and 0.5 s, respectively. These results indicate that visible/near-infrared spectroscopy can be exploited to realize rapid online detection of the origin of duck eggs, and the methodologies used in this study can be immediately implemented in production practice.

Cotton plays a significant role in people’s lives, and cottonseeds serve as a vital assurance for successful cotton cultivation and production. Premium-quality cottonseeds can significantly enhance the germination rate of cottonseeds, resulting in increased cotton yields. The vitality of cottonseeds is a crucial metric that reflects the quality of the seeds. However, currently, the industry lacks a non-destructive method to directly assess cottonseed vitality without compromising the integrity of the seeds. To address this challenge, this study employed a hyperspectral imaging acquisition system to gather hyperspectral data on cottonseeds. This system enables the simultaneous collection of hyperspectral data from 25 cottonseeds. This study extracted spectral and image information from the hyperspectral data of cottonseeds to predict their vitality. SG, SNV, and MSC methods were utilized to preprocess the spectral data of cottonseeds. Following this preprocessing step, feature wavelength points of the cottonseeds were extracted using SPA and CARS algorithms. Subsequently, GLCM was employed to extract texture features from images corresponding to these feature wavelength points, including attributes such as Contrast, Correlation, Energy, and Entropy. Finally, the vitality of cottonseeds was predicted using PLSR, SVR, and a self-built 1D-CNN model. For spectral data analysis, the 1D-CNN model constructed after MSC+CARS preprocessing demonstrated the highest performance, achieving a test set correlation coefficient of 0.9214 and an RMSE of 0.7017. For image data analysis, the 1D-CNN model constructed after SG+CARS preprocessing outperformed the others, yielding a test set correlation coefficient of 0.8032 and an RMSE of 0.9683. In the case of fused spectral and image data, the 1D-CNN model built after SG+SPA preprocessing displayed the best performance, attaining a test set correlation coefficient of 0.9427 and an RMSE of 0.6872. These findings highlight the effectiveness of the 1D-CNN model and the fusion of spectral and image features for cottonseed vitality prediction. This research contributes significantly to the development of automated detection devices for assessing cottonseed vitality.

The Chinese mitten crab (Eriocheir sinensis) is highly valued by consumers for its delicious taste and high nutritional content, including proteins and trace elements, giving it significant economic value. However, variations in taste and nutritional value among crabs from different regions lead to considerable price differences, fueling the prevalence of counterfeit crabs in the market. Currently, there are no rapid detection methods to verify the origin of Chinese mitten crabs, making it crucial to develop fast and accurate detection techniques to protect consumer rights. This study focused on Chinese mitten crabs from different regions, specifically Hongze Lake, Tuo Lake, and Weishan Lake, by collecting near-infrared (NIR) diffuse reflectance spectral data from both the abdomen and carapace regions of the crabs. To eliminate noise from the spectral data, pretreatment was performed using Savitzky–Golay (SG) smoothing, Standard Normal Variate (SNV) transformation, and Multiplicative Scatter Correction (MSC). Key wavelengths reflecting the origin of Chinese mitten crabs were selected using Competitive Adaptive Reweighted Sampling (CARS), Bootstrap Soft Shrinkage (BOSS), and Uninformative Variable Elimination (UVE) algorithms. Finally, Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Back Propagation Neural Network (BP) models were developed for rapid detection of crab origin. The results demonstrated that MSC provided the best preprocessing performance for NIR spectral data from both the abdomen and back of the crabs. For abdomen data, the SVM model developed using feature wavelengths selected by the CARS algorithm after MSC preprocessing achieved the highest accuracy (Acc) of 90.00%, with precision (P), recall (R), and F1-score for crabs from Weishan Lake at 89.29%, 86.21%, and 87.72%, respectively; for crabs from Tuo Lake at 86.96%, 95.24%, and 90.91%; and for crabs from Hongze Lake at 90.00%, 93.10%, and 91.53%. For carapace data, the SVM model based on wavelengths selected by the BOSS algorithm after MSC pretreatment achieved the best performance, with an Acc of 87.50%, and P, R, and F1 for crabs from Weishan Lake at 77.14%, 93.10%, and 84.38%; for Tuo Lake crabs at 100%, 90.47%, and 95.00%; and for Hongze Lake crabs at 92.31%, 80.00%, and 85.71%. In conclusion, NIR spectroscopy can effectively detect the origin of Chinese mitten crabs, providing technical support for developing rapid detection instruments and thereby safeguarding consumer rights.

High-quality cottonseed is essential for successful cotton production. The integrity of cottonseed hulls plays a pivotal role in fostering the germination and growth of cotton plants. Consequently, it is crucial to eliminate broken cottonseeds before the cotton planting process. Regrettably, there is a lack of rapid and cost-effective methods for detecting broken cottonseed at this critical stage. To address this issue, this study developed a dual-camera system for acquiring front and back images of multiple cottonseeds. Based on this system, we designed the hardware, software, and control systems required for the online detection of cottonseed breakage. Moreover, to enhance the performance of cottonseed breakage detection, we improved the backbone and YOLO head of YOLOV8m by incorporating MobileOne-block and GhostConv, resulting in Light-YOLO. Light-YOLO achieved detection metrics of 93.8% precision, 97.2% recall, 98.9% mAP50, and 96.1% accuracy for detecting cottonseed breakage, with a compact model size of 41.3 MB. In comparison, YOLOV8m reported metrics of 93.7% precision, 95.0% recall, 99.0% mAP50, and 95.2% accuracy, with a larger model size of 49.6 MB. To further validate the performance of the online detection device and Light-YOLO, this study conducted an online validation experiment, which resulted in a detection accuracy of 86.7% for cottonseed breakage information. The results demonstrate that Light-YOLO exhibits superior detection performance and faster speed compared to YOLOV8m, confirming the feasibility of the online detection technology proposed in this study. This technology provides an effective method for sorting broken cottonseeds.

Color is a key indicator in evaluating seed cotton quality. Accurate and rapid detection of seed cotton color is essential for its storage, processing, and trade. In this study, an RGB imaging and semantic segmentation-based method was proposed for seed cotton color detection. First, a color detection system utilizing machine vision technology was developed to capture seed cotton images. Next, a Color-Unet model, incorporating convolutional block attention and improved inception E modules based on Unet, was applied to effectively remove impurities and shadows from the images, resolving the over-segmentation issue commonly encountered in traditional threshold segmentation. The results demonstrated that the pixel accuracy of segmentation reached 97.20%, the mean intersection over union was 91.81%, and the average segmentation speed was 322.3 ms per image. The Color-Unet model effectively addressed the over-segmentation problem. Subsequently, seed cotton color indexes were calculated using Hunter color formulas based on the segmented images. To evaluate the accuracy of color measurement obtained with the proposed method, a regression analysis was performed, comparing the results of those from the HX-410 measurement. The coefficient of determination of yellowness was 0.883, with a root mean square error of 0.150 and a mean relative error of 2.61%. The coefficient of determination of reflectance degree was 0.832, with a root mean square error of 1.56% and a mean relative error of 1.84%. The proposed method allows for the rapid and accurate assessment of seed cotton color from RGB images, providing a valuable technical reference for seed cotton color evaluation.

This study developed a rapid, non-destructive method for the quantitative detection of protein in cottonseed by integrating near-infrared (NIR) fiber spectroscopy with chemometric machine learning. The establishment of this method holds significant importance for the rational and efficient utilization of cottonseed resources, advancing research on the genetic improvement of cottonseed nutritional quality, and promoting the development of equipment for raw cottonseed protein detection. Fuzzy cottonseed samples from three varieties were collected, and their NIR fiber-optic spectra were acquired. Reference protein contents were measured using the Kjeldahl method. Spectra were denoised through preprocessing, after which informative wavelengths were selected by combining Uninformative Variable Elimination (UVE) with Competitive Adaptive Reweighted Sampling (CARS) and the Random Frog (RF) algorithm. Partial least squares regression (PLSR), least-squares support vector machine (LSSVM), and support vector regression (SVR) models were then constructed to predict protein content. Model performance was assessed using the coefficient of determination (R²), root-mean-square error (RMSE), residual predictive deviation (RPD), and range error ratio (RER). The results indicate that the standard normal variate (SNV) is the most effective preprocessing step. The best performance was achieved by the LSSVM model coupled with UVE + CARS, yielding R² = 0.8571, RMSE = 0.0033, RPD = 2.7078, and RER = 10.72, outperforming the PLSR and SVR counterparts. These findings provide technical support for the rapid detection of fuzzy cottonseed protein and lay the groundwork for the development of related detection equipment.