High Speed Railway Fastener Defect Detection by Using Improved YoLoX-Nano Model.

Rails play a vital role in the bearing and guidance of high-speed trains, and the normal condition of rail components is the guarantee of the operation and maintenance safety. Fasteners are critical components for fixing the rails, so it is particularly important to detect whether they are in a normal state or not. The current rail-fastener detection models have some drawbacks, including poor generalization ability, large model volume and low detection efficiency. In view of this, an improved YoLoX-Nano rail-fastener-defect-detection method is proposed in this paper. The CA attention mechanism is added to the three output feature maps of CSPDarknet and the enhanced feature extraction part of the Path Aggregation Feature Pyramid Network (PAFPN); the Adaptively Spatial Feature Fusion (ASFF) is added after the PAFPN output feature map, which enables the semantic information of the high-level features and the fine-grained features of the bottom layer to be further enhanced. The improved YoLoX-Nano model has improved the AP value by 27.42% on fractured fasteners, 15.88% on displacement fasteners and 12.96% on normal fasteners. Moreover, the mAP value is improved by 18.75%, and it is 14.75% higher than the two-stage model Faster-RCNN on mAP. In addition, compared with YoLov7-tiny, the improved YoLoX-Nano model achieves 13.56% improvement on mAP. Although the improved model increases a certain amount of calculation, the detection speed of the improved model has been increased by 30.54 fps and by 32.33 fps when compared with that of the Single-Shot Multi-Box Detector (SSD) model and the You Only Look Once v3 (YoLov3) model, reaching 54.35 fps. The improved YoLoX-Nano model enables accurate and rapid identification of the defects of rail fasteners, which can meet the needs of real-time detection. Furthermore, it has advantages in lightweight deployment of terminals for rail-fastener detection, thus providing some reference for image recognition and detection in other fields.

Research on Enhanced Detection of Benzoic Acid Additives in Liquid Food Based on Terahertz Metamaterial Devices.

It is very important for human health to supervise the use of food additives, because excessive use of food additives will cause harm to the human body, especially lead to organ failures and even cancers. Therefore, it is important to realize high-sensibility detection of benzoic acid, a widely used food additive. Based on the theory of electromagnetism, this research attempts to design a terahertz-enhanced metamaterial resonator, using a metamaterial resonator to achieve enhanced detection of benzoic acid additives by using terahertz technology. The absorption peak of the metamaterial resonator is designed to be 1.95 THz, and the effectiveness of the metamaterial resonator is verified. Firstly, the original THz spectra of benzoic acid aqueous solution samples based on metamaterial are collected. Secondly, smoothing, multivariate scattering correction (MSC), and smoothing combined with first derivative (SG + 1 D) methods are used to preprocess the spectra to study the better spectral pretreatment methods. Then, Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighted Sampling (CARS) are used to explore the optimal terahertz band selection method. Finally, Partial Least Squares (PLS) and Least square support vector machine (LS-SVM) models are established, respectively, to realize the enhanced detection of benzoic acid additives. The LS-SVM model combined with CARS has the best effect, with the correlation coefficient of prediction set (Rp) is 0.9953, the root mean square error of prediction set (RMSEP) is 7.3 × 10-6, and the limit of detection (LOD) is 2.3610 × 10-5 g/mL. The research results lay a foundation for THz spectral analysis of benzoic acid additives, so that THz technology-based detection of benzoic acid additives in food can reach requirements stipulated in the national standard. This research is of great significance for promoting the detection and analysis of trace additives in food, whose results can also serve as a reference to the detection of antibiotic residues, banned additives, and other trace substances.

A first-principles study on the magnetoelectric coupling induced by Fe in a two-dimensional BaTiO3(001) ultrathin film.

A first-principles approach is utilized to study the magnetoelectric coupling induced by Fe in two-dimensional BaTiO3(001) ultrathin film. It is observed that the Fe impurity increases the total magnetic moment but suppresses the spontaneous polarization. Furthermore, the total magnetic moment of Fe replacing Ti is influenced by the distance between dopants and decreases with reducing distance. A study of spin-orbit coupling under bi-axial compressive strain shows strong magnetoelectric coupling with Ti substitution and the configuration with neighbouring Fe is more readily adjusted than that with Fe distant from each other. Meanwhile, the replacement of Ba exhibits negligible interaction between spontaneous polarization and magnetic moment. Clearly, our current work may indicate that the careful substitution of Ti with Fe atoms can realize two-dimensional BaTiO3 behaving as a multiferroic material.

A first-principles study on the influences of metal species Al, Zr, Mo and Tc on the mechanical properties of U3Si2.

A first-principles approach is employed to study the influences of the metal species Al, Zr, Mo and Tc on the mechanical properties of U3Si2. When the Al, Zr, Mo and Tc atoms diffuse into the vacancy sites, they dissolve into the lattice, as confirmed by the solution energies. It is found that the compounds of U3Si2 with low amounts of Al, Zr, Mo, and Tc in the Si vacancies or Al, Zr, and Mo in the U vacancies can behave in the manner of ductility. However, in the cases where Al, Zr, Mo and Tc occupy the interstitial sites, all the compounds are demonstrated to be brittle. Furthermore, the stress-strain relationship for the U3Si1.9375Mo0.0625 system was calculated, which illustrates the enhanced ductility. The current results indicate that the substitution of Si by carefully selected metal atoms can enhance the performance of U3Si2.

[Rapid Diagnosis of Sound, Yellow and Citrus Greening Leaves with Hyperspectral Imaging].

The feasibility was investigated for identifying sound, yellow and citrus greening leaves of navel orange trees based on hyperspectral imaging combined with correlation analysis and discriminant partial least square (DPLS) methods. The hyperspectral data of sound, yellow and citrus greening leaves were recorded in the wavelength range of 374.28～1 016.89 nm. Two regions of interest (ROI) were marked symmetrically on both sides along main veins with an ellipse of major axis of 60 pixels and minor axis of 30 pixels. The average reflectance spectrum was extracted from ROI regions. A pair wavelengths of 502.79 and 374.28 nm were chosen with correlation analysis method in the wavelength range of 374.28～1 016.89 nm. The classification model was developed with the peak ratio of the pair wavelengths. This model was effective to sound leaves with the recognition accuracy of 1.7% but yellow and citrus greening leaves. The DPLS model was employed with the preprocessing spectra of second derivative and Savitzky-Golay smoothing. The recognition accuracy of this model was 100% for citrus greening leaves and yellow ones. The number of latent variables (LVs) was optimized with the leave one out cross validation method. The optimal LVs, correlation coefficient and standard error of calibration of the DPLS model were 17, 0.96 and 0.13, respectively. The correction classification rate of the DPLS model was 100% for yellow leaves and citrus greening ones. Two-step method of the peak ratio models combination with the DPLS was proposed for identifying sound, yellow and citrus greening leaves. The new samples were applied to evaluation the classification ability of the two-step method, which included sound leaves of 10, citrus greening leaves of 10 and yellow leaves of 10. The correction classification rate reached 96.7%. Experimental results showed that it was feasible to identify sound, yellow and citrus greening leaves by hyperspectral imaging coupled with the peak ratio and DPLS models.

[Application of Raman Spectroscopy Technique to Agricultural Products Quality and Safety Determination].

The quality and safety of agricultural products and people health are inseparable. Using the conventional chemical methods which have so many defects, such as sample pretreatment, complicated operation process and destroying the samples. Raman spectroscopy as a powerful tool of analysing and testing molecular structure, can implement samples quickly without damage, qualitative and quantitative detection analysis. With the continuous improvement and the scope of the application of Raman spectroscopy technology gradually widen, Raman spectroscopy technique plays an important role in agricultural products quality and safety determination, and has wide application prospects. There have been a lot of related research reports based on Raman spectroscopy detection on agricultural product quality safety at present. For the understanding of the principle of detection and the current development situation of Raman spectroscopy, as well as tracking the latest research progress both at home and abroad, the basic principles and the development of Raman spectroscopy as well as the detection device were introduced briefly. The latest research progress of quality and safety determination in fruits and vegetables, livestock and grain by Raman spectroscopy technique were reviewed deeply. Its technical problems for agricultural products quality and safety determination were pointed out. In addition, the text also briefly introduces some information of Raman spectrometer and the application for patent of the portable Raman spectrometer, prospects the future research and application.

[Applications of near infrared reflectance spectroscopy technique (NIRS) to soil attributes research].

Soil is a much complicated substance, because animals, plants and microbes live together, organic and inorganic exist together. So soil contains a large amount of information. The traditional method in laboratory is a time-consuming effort. But the technology of near infrared reflectance spectroscopy (NIRS) has been widely used in many areas, owing to its rapidness, high efficiency, no pollution and low cost, NIRS has become the most important method to detect the composition of soil. This paper mainly introduce some traditional methods in laboratory, the basic processes of soil detection by NIRS, some algorithms for data preprocessing and modeling. Besides, the present paper illustrates the latest research progress and the development of portable near infrared instruments of the soil. According to this paper, the authors also hope to promote the application conditions of NIRS in the grassland ecology research in China, and accelerate the modernization of research measures in this area.

Investigation on the environmental causes of tomato fruit cracking and its propagation prediction in greenhouse.

In this study, Provence tomato variety was chosen for investigating the environmental causes of tomato fruit cracking, cracks characteristics, and their propagation prediction in a greenhouse. Fruit bagging approach was used to alter the temperature and humidity and to create a microclimate around the fruit to induce fruit cracking for testing. Results showed that the fruit cracking rate increased when the environment temperature exceeded 30°C, and the difference between the highest and lowest temperature values in a day was greater than 20°C. The cracking rate was aggravated when the difference between the highest and lowest humidity values in a day was less than 20%. The proportions of top cracking, longitudinal cracking, ring cracking, radial cracking, and combined cracking were 5.4%, 16.1%, 28.3%, 26.8%, and 32.1%, respectively. The fruit shoulder was the most susceptible region to crack, followed by fruit belly and top regions, whereas longer cracks were observed in the fruit belly region indicating a higher propensity to crack propagation in that region. Finally, the measured data were used to validate an extended finite element method developed to effectively predict cracking susceptibility and propagation in tomato fruit with a relative error of 4.68%.

[Estimation of the TN and SOM contents in soil from GAN NAN navel orange plant area by NIR diffuse spectroscopy].

The soil sampled from GAN NAN navel orange plant area was selected as research object, and the feasibility of analyzing the total nitrogen (TN) and soil organic matter (SOM) of soil was investigated by near infrared spectroscopy (NIR) techniques in the wavelength range of 4 000 - 7 500 cm(-1). Different pretreatment methods including multiplicative scatter correction (MSC), first derivative(1st D), second derivative (2nd D), Savitzkv-Golay (SG), standard normalized variate (SNV) and baseline were used. The partial least square regress (PLS) was built for the calibration models. The best TN model using SG pretreatment features the prediction correlation coefficients (r(c)) of 0.802, the root mean square error of calibration (RMSEC) of 2.754, the calibration correlation coefficients (r(p)) of 0.715, and the root mean square error of prediction (RMSEP) of 3.077 in the wave-length range of 4 000 - 7 500 cm(-1). The best SOM model using SNV pretreatment has r(c) of 0.848, RMSEC of 0.128, r(p) of 0.790, and RMSEP of 0.152. The results showed that the NIR diffuse reflectance can be used for quick estimate of the TN and SOM contents in soil with the wavelength range of 4 000 - 7 500 cm(-1).

Detection of Chili Foreign Objects Using Hyperspectral Imaging Combined with Chemometric and Target Detection Algorithms.

Chilies undergo multiple stages from field production to reaching consumers, making them susceptible to contamination with foreign materials. Visually similar foreign materials are difficult to detect manually or using color sorting machines, which increases the risk of their presence in the market, potentially affecting consumer health. This paper aims to enhance the detection of visually similar foreign materials in chilies using hyperspectral technology, employing object detection algorithms for fast and accurate identification and localization to ensure food safety. First, the samples were scanned using a hyperspectral camera to obtain hyperspectral image information. Next, a spectral pattern recognition algorithm was used to classify the pixels in the images. Pixels belonging to the same class were assigned the same color, enhancing the visibility of foreign object targets. Finally, an object detection algorithm was employed to recognize the enhanced images and identify the presence of foreign objects. Random forest (RF), support vector machine (SVM), and minimum distance classification algorithms were used to enhance the hyperspectral images of the samples. Among them, RF algorithm showed the best performance, achieving an overall recognition accuracy of up to 86% for randomly selected pixel samples. Subsequently, the enhanced targets were identified using object detection algorithms including R-CNN, Faster R-CNN, and YoloV5. YoloV5 exhibited a recognition rate of over 96% for foreign objects, with the shortest detection time of approximately 12 ms. This study demonstrates that the combination of hyperspectral imaging technology, spectral pattern recognition techniques, and object detection algorithms can accurately and rapidly detect challenging foreign objects in chili peppers, including red stones, red plastics, red fabrics, and red paper. It provides a theoretical reference for online batch detection of chili pepper products, which is of significant importance for enhancing the overall quality of chili pepper products. Furthermore, the detection of foreign objects in similar particulate food items also holds reference value.

Study on qualitative identification of aflatoxin solution based on terahertz metamaterial enhancement.

Aflatoxin is the main carcinogen that contaminates agricultural products and foods such as peanuts and corn. There are many kinds of aflatoxins, mainly including aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), aflatoxin G1 (AFG1) and aflatoxin G2 (AFG2). Different types of aflatoxins have different toxicity and different levels of contamination to agricultural products as well as food. Therefore, the rapid, non-destructive and highly sensitive qualitative identification of aflatoxin species is of great significance to maintain people's life and health. The conventional terahertz detection method can only qualitatively identify the samples at the milligram level, but it is not suitable for the qualitative analysis of trace samples. In this paper, a terahertz metamaterial sensor with "X" composite double-peak structure was designed based on electromagnetic theory to investigate the feasibility of THz-TDS technology based on a metamaterial sensor for the qualitative identification of trace aflatoxin B2, G1 and G2 solutions. Firstly, the terahertz transmission spectra of eight different concentrations of aflatoxin B2, G1 and G2 were collected respectively, and then the differences of terahertz transmission spectra of different aflatoxin species were investigated. Finally, the terahertz transmission spectra of aflatoxin B2, G1 and G2 solutions were modeled and analyzed using chemometric methods. It was found that there were significant differences in the transmission peak curves of different kinds of aflatoxin. Through the comparative analysis of different models, it was concluded that the prediction accuracy of the CARS-RBF-SVM model was the highest, and the accuracy of the calibration set reached 100%. 119 out of 120 predicted samples were correctly predicted, and the prediction accuracy was 99.17%. This study verified the feasibility of qualitative identification of trace aflatoxin B2, G1 and G2 solutions by a metamaterial sensor based on the "X" composite double-peak structure combined with THz-TDS technology, and provided a theoretical basis and a new detection method for the qualitative identification of trace aflatoxins. This will facilitate the rapid, non-destructive and highly sensitive qualitative detection of different kinds of aflatoxins in food and agricultural products. At the same time, this study has important implications for promoting the qualitative detection of other trace substances.

3D-printed tortuous vessels with Photodissociable and morphology-controllable ink.

Acute ischemic stroke (AIS) is a high mortality cerebrovascular disease associated with vessel curvature. However, the relevant mechanism remains unclear due to a lack of appropriate tortuous vascular models to investigate and validate. This study explores the combination of projection-based 3D bioprinting (PBP) with photo-stimulus-responsive techniques to fabricate a sodium alginate (SA)/acrylamide (AAM) hydrogel vascular scaffold capable of bending deformation. The coordination of Fe3+ ions with carboxylate groups in the alginate chains of the vascular scaffold acts as a molecular switch, which can be dissociated through photoreduction to enable the deformation response. Fourier Transform Infrared (FTIR) and X-ray Photoelectron Spectroscopy (XPS) results verified the deformation principle. By subjecting the scaffold to UV light exposure, Fe3+ is reduced to Fe2+ in spatially selected regions, resulting in the release of strain and subsequent deformation. Furthermore, it also controlled the degree and direction of curvature of the vessels. The cell seeding experiment verified that the vascular scaffold showed excellent biocompatibility. Overall, our approach could be used to generate an in vitro model of curved vascular pathology to investigate the pathogenesis and provide new directions for the diagnosis and treatment of vascular diseases in the future.

[Fast determination of heavy metal Cu in Ludwigia prostrata leaves using near infrared diffuse spectroscopy].

Heavy metal ions in plants can be determined by using the near-infrared spectral (NIRS) technique, because they combine with the organic molecular groups that have NIRS absorptions. The present article analyzed the fast detection of heavy metal Cu in Ludwigia prostrata leaves by near infrared diffuse spectral technology. Different preprocessing methods were compared, combined with partial least squares (PLS), and the fast detection models of heavy metal Cu in Ludwigia prostrata leaves were established. The results showed that the best model was obtained by PLS with the preprocessing method of average smoothing. The correlation coefficient (r) and root mean square error of calibration(RMSECV) was 0.950 and 5.99 respectively; External validation correlation coefficient (r) and root mean square error of prediction(RMSEP) was 0.923 and 7.38 respectively. The study shows that fast determination of heavy metal Cu in Ludwigia prostrata leaves using near infrared diffuse spectroscopy is feasible.

[Analysis of chlorophyll in Gannan navel orange with algorithm of GA and SPA based on hyperspectral].

The hyperspectral effective information of Gannan navel orange was extracted by genetic algorithm (GA) and successive projections algorithm (SPA) for partial least squares (PLS) model. Average spectral was extracted from region of interest (ROD) of hyperspectral images after preprocessing. GA and SPA were used to select 27 and 8 characteristic wavelengths for chlorophyll model with PLS. The correlation coefficients and rms error of GA-PLS were 0.80 and 2.45, and the correlation coefficients and rms error of SPA-PLS were 0.83 and 2.30, respectively. Overall results sufficiently demonstrate that SPA-PLS model has a greater advantage which can be combined with hyperspectral technique to be a nondestructive and rapid analytical method.

[Construction and simplification of the calibration model for spectral analysis of fuel oil properties based on mutual information method].

Near infrared diffuse reflectance (NIRS) and ultraviolet (UV) spectral analysis were adopted for quantitative determination of octane number and monoaromatics in fuel oil. Partial least squares regression (PLSR) was used for construction of vibrational spectral calibration models. Variables selection strategy based on mutual information (MI) theory was introduced to optimize the models for improving the precision and reducing the complexity. The results indicate that MI-PLSR method can effectively improve the predictive ability of the models and simplify them. For octane number models, the root mean square error of prediction (RMSEP) and the number of calibration variables were reduced from 0.288 and 401 to 0.111 and 112, respectively, and correlation coefficient (R) was improved from 0.985 to 0.998. For monoaromatics models, RMSEP and the number of calibration variables were reduced from 0.753 and 572 to 0.478 and 37, respectively, and R was improved from 0.996 to 0.998. Vibrational spectral analysis combined with MI-PLSR method can be used for quantitative analysis of fuel oil properties, and improve the cost-effectiveness.

[Partial least squares regression variable screening studies on apple soluble solids NIR spectral detection].

Abstract To improve the predictive ability and robustness of the NIR correction model of the soluble solid content (SSC) of apple, the reverse interval partial least squares method, genetic algorithm and the continuous projection method were implemented to select variables of the NIR spectroscopy of the soluble solid content (SSC) of apple, and the partial least squares regression model was established. By genetic algorithm for screening of the 141 variables of the correction model, prediction has the best effect. And compared to the full spectrum correction model, the correlation coefficient increased to 0.96 from 0.93, forecast root mean square error decreased from 0.30 degrees Brix to 0.23 degrees Brix. This experimental results show that the genetic algorithm combined with partial least squares regression method improved the detection precision of the NIR model of the soluble solid content (SSC) of apple.

Calibration of Near Infrared Spectroscopy of Apples with Different Fruit Sizes to Improve Soluble Solids Content Model Performance.

The transmission spectrum of apples is affected by the fruit's size, which leads to poor prediction performance of the soluble solids content (SSC) models built for their different apple sizes. In this paper, three sets of near infrared (NIR) spectra of apples with various apple diameters were collected by applying NIR spectroscopy detection equipment to compare the spectra differences among various apple diameter groups. The NIR spectra of apples were corrected by studying the extinction rates within different apples. The corrected spectra were used to develop a partial least squares prediction model for their soluble solids content. Compared with the prediction model of the soluble solids content of apples without size correction, the Rp of PLSR improved from 0.769 to 0.869 and RMSEP declined from 0.990 to 0.721 in the small fruit diameter group; the Rp of PLSR improved from 0.787 to 0.932 and RMSEP declined from 0.878 to 0.531 in the large fruit diameter group. The proposed apple spectra correction method is effective and can be used to reduce the influence of sample diameter on NIR spectra.

Study on Qualitative Impact Damage of Loquats Using Hyperspectral Technology Coupled with Texture Features.

Bruising is one of the main problems in the post-harvest grading and processing of 'Zaozhong 6' loquats, reducing the economic value of loquats, and even food quality and safety problems are caused by it. Therefore, one of the main tasks in the post-harvest processing of loquats is to detect whether loquats are bruised, as well as the degree of bruising of loquats, to reduce the loss by proper treatment. An appropriate dimensionality reduction method can be used to reduce the redundancy of variables and improve the detection speed. The multispectral analysis method (MAM) has the advantage of accurate, rapid, and nondestructive detection, which was proposed to identify the different bruising degrees of loquats in this study. Firstly, the visible and near-infrared region (Vis-NIR, 400-1000 nm), the visible region (Vis, 400-780 nm), and the near-infrared region (NIR, 781-1000 nm) were analyzed using principal component analysis (PCA) to obtain the spectral regions and PC vectors, which could be used to effectively distinguish bruised loquats from normal loquats. Then, based on the selected second PC (PC2) score images, a morphological segmentation method (MSM) was proposed to distinguish bruised loquats from normal loquats. Furthermore, the weight coefficients of corresponding wavelength points of different degrees of bruising of loquats were analyzed, and the local extreme points and both sides of the interval were selected as the characteristic wavelength points for multi-spectral image processing. A gray level co-occurrence matrix (GLCM) was used to extract texture features and gray information from two-band ratio images K782/999. Finally, the MAM was proposed to detect the degree of bruising of loquats, which included the spectral data of three characteristic wavelength points in the NIR region coupled with texture features of the two-band ratio images, and the classification accuracy was 91.3%. This study shows that the MAM can be used as an effective dimensionality reduction method. The method not only improves the effect of prediction but also simplifies the process of prediction and ensures the accuracy of classification. The MSM can be used for rapid detection of normal and bruised fruits, and the MAM can be used to classify the degree of bruising of bruised fruits. Consequently, the processed methods are effective and can be used for the rapid and nondestructive detection of the degree of bruising of fruit.

Quantitative study of impact damage on yellow peaches based on reflectance, absorbance and Kubelka-Munk spectral data.

Impact damage is one of the main forms of damage during the postharvest transportation and processing of yellow peaches. Thus, a quantitative prediction of the impact damage degree of yellow peaches is significant for their postharvest grading. In the present study, mechanical parameters such as the damage area, absorbed energy and maximum force were obtained based on a single pendulum collision device and an intelligent data acquisition system. The reflection spectra (R) of the damaged areas of yellow peaches were collected by a hyperspectral imaging system and transformed into absorbance (A) spectra and Kubelka-Munk (K-M) spectra. The R, A and K-M spectra were preprocessed by standard normal variables (SNV), moving average (MA) and Gaussian filtering (GF). Partial least squares regression (PLSR) models and support vector regression (SVR) models based on original and preprocessed spectra were established, respectively. By comparative analysis, the spectral data with better prediction performance (raw or preprocessed spectra) were selected from all spectra, and the characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE). The PLSR and SVR models based on characteristic wavelengths were established. The results revealed that the prediction performance of the K-M-GF-CARS-PLSR model is the best. For the damage area, absorbed energy and maximum force, the R P 2 and RMSEP of the K-M-GF-CARS-PLSR model were 0.870 and 77.865 mm2, 0.772 and 1.065 J, 0.895 and 47.996 N, respectively. Furthermore, the values of their RPD were 2.700, 1.768 and 3.050, respectively. The characteristic wavelengths of the model were 18.8%, 10.2% and 21.6%, respectively. The results of this study showed that there was a strong correlation between the mechanical parameters and K-M spectrum, which demonstrates the feasibility of quantitatively predicting the damage degree of yellow peaches based on the K-M spectrum. Therefore, the results of this work not only provide theoretical guidance for the postharvest grading of fruits, but also enrich the theoretical system of biomechanics.

Detection of early bruises on loquat using hyperspectral imaging technology coupled with band ratio and improved Otsu method.

The bruising is one of the major factors affecting the quality of loquat and the bruised areas of loquat are also prone to harbor bacteria and molds. Therefore, it is critical to detect early bruises of loquat. In this study, a method based on hyperspectral imaging technology coupled with band ratio and improved Otsu method was proposed to detect early bruises of loquat. Firstly, the principal component cluster analysis was used to analyze the three regions of Vis-NIR (397.5-1014.0 nm), Vis (397.5-780.0 nm), and NIR (780.0-1014.0 nm), respectively. It was found that the Vis-NIR and NIR spectral regions along PC1 could be used to effectively distinguish bruised tissues. Then, the key wavelength images corresponding to the two regions were selected according to the load curve, respectively, and two sets of PC images and band ratio images of them were established. After comparison, it was found that the band ratio image Q651.3 / 904.3 was the most suitable for subsequent analysis of detecting early bruises of loquat. Finally, in order to evaluate the segmentation effect of the improved Otsu method, the segmentation results of the global threshold and the Otsu method were compared with it, respectively, and it was found that the performance of the improved Otsu method was best. However, since the stem-end area and the bruised area have similar intensity features causing mis-segmentation, the stem-end area was removed by curvature-assisted Hough transform circle detection (CACD) algorithm. And all test set samples were used to evaluate the performance of the proposed method, and the overall accuracy of it was 96.0 %. The results show that the detection method proposed in this study has the potential to detect early bruises of loquat in online practical applications, and it provides a theoretical basis for hyperspectral imaging in the bruise detection of fruit.