Optimal Texture Research Articles

Research on the Influence of Surface Texture on the Friction Performance of Continuous Rotary Motor Friction Pairs

To study the effect of surface texture on the friction performance of continuous rotary motor friction pairs, a simplified model of the clearance flow field between the motor blades and stator friction pairs was first established. Single factor experimental plans and orthogonal experimental plans were designed with the maximum positive pressure of the oil film as the optimization objective. Then, simulation calculations were conducted on the texture fluid models under different morphologies and geometric parameters using FLUENT software to analyze the maximum positive pressure of the oil film The relationship between bearing capacity and friction coefficient is compared to obtain the surface texture applied to the friction pairs of motor blades and stator; Finally, the optimal texture obtained based on simulation calculations is processed on the specimen and friction tests are conducted. By comparing the friction coefficients of the friction surface between the textured surface and the smooth surface friction pair at different speeds and loads, the influence of surface texture on the friction performance of the friction pair is analyzed. The results indicate that the surface texture designed in this article can effectively improve the friction performance of motor friction pairs.

Study of grain growth kinetics of Zr-2.5%Nb alloy quenched from (α+β) and β-phase region

The Zr-2.5 %Nb is used as the Pressure Tube (PT) material in Pressurized Heavy Water Reactors (PHWRs) in both cold worked and stress relieved as well as heat treated condition. For the development of heat-treated pressure tube, process parameters need to be tuned to achieve optimum microstructure and texture. In microstructural aspect, prior β grain size is a very important factor in influencing the property of the material. Hence, the prior β grain growth kinetics of the Zr-2.5 %Nb alloy has been investigated by subjecting the material to different heat treatments. Different heat treatment temperatures have been chosen from (α+β) and β phase region. The β transus temperature was measured through dilatometry. Specimens were soaked for different time and the grain size were measured using optical metallography. The evolution of grain size with soaking temperature and time were analyzed to study the grain growth kinetics.

Multi-objective optimization of surface texture shape in fluid mechanical face seals using mass-conserving cavitation boundary condition

Surface texture shape significantly influences the tribological and sealing properties of fluid mechanical face seals. Considering that it is a contradiction to simultaneously improve load capacity and reduce leakage, a numerical multi-objective optimization model is proposed herein to improve the load-carrying capacity while reducing the volume leakage rate via texture shape optimization. The mass-conserving cavitating boundary condition and the non-dominated sorting genetic algorithm II are employed for the optimization. The numerical results indicate that the optimal texture shape is spiral-like under the lowest volume leakage rate, which becomes an asymmetric flat-front chevron with increasing load-carrying capacity and volume leakage rate. The optimal shape outperforms four regular shapes (all obtained by multi-objective optimization) at different rotation speeds and sealing pressures. The cavitation within the optimal shape ensures zero leakage in fluid face seals. The comprehensive performance of the texture shape obtained by multi-objective optimization is superior to that obtained by single-objective optimization.

Optimal Image Characterization for In-Bed Posture Classification by Using SVM Algorithm

Identifying patient posture while they are lying in bed is an important task in medical applications such as monitoring a patient after a surgical intervention, sleep supervision to identify behavioral and physiological markers, or for bedsore prevention. An acceptable strategy to identify the patient’s position is the classification of images created from a grid of pressure sensors located in the bed. These samples can be arranged based on supervised learning methods. Usually, image conditioning is required before images are loaded into a learning method to increase classification accuracy. However, continuous monitoring of a person requires large amounts of time and computational resources if complex pre-processing algorithms are used. So, the problem is to classify the image posture of patients with different weights, heights, and positions by using minimal sample conditioning for a specific supervised learning method. In this work, it is proposed to identify the patient posture from pressure sensor images by using well-known and simple conditioning techniques and selecting the optimal texture descriptors for the Support Vector Machine (SVM) method. This is in order to obtain the best classification and to avoid image over-processing in the conditioning stage for the SVM. The experimental stages are performed with the color models Red, Green, and Blue (RGB) and Hue, Saturation, and Value (HSV). The results show an increase in accuracy from 86.9% to 92.9% and in kappa value from 0.825 to 0.904 using image conditioning with histogram equalization and a median filter, respectively.

A Generalised Method for Friction Optimisation of Surface Textured Seals by Machine Learning

Friction behaviour is an important characteristic of dynamic seals. Surface texturing is an effective method to control the friction level without the need to change materials or lubricants. However, it is difficult to put the manual prediction of optimal friction reducing textures as a function of operating conditions into practice. Therefore, in this paper, we use machine learning techniques for the prediction of optimal texture parameters for friction optimisation. The application of pneumatic piston seals serves as an illustrative example to demonstrate the machine learning method and results. The analyses of this work are based on experimentally determined data of surface texture parameters, defined by the dimple diameter, distance, and depth. Furthermore friction data between the seal and the pneumatic cylinder are measured in different friction regimes from boundary over mixed up to hydrodynamic lubrication. A particular innovation of this work is the definition of a generalised method that guides the entire machine learning process from raw data acquisition to model prediction, without committing to only a few learning algorithms. A large number of 26 regression learning algorithms are used to build machine learning models through supervised learning to evaluate the suitability of different models in the specific application context. In order to select the best model, mathematical metrics and tribological relationships, like Stribeck curves, are applied and compared with each other. The resulting model is utilised in the subsequent friction optimisation step, in which optimal surface texture parameter combinations with the lowest friction coefficients are predicted over a defined interval of relative velocities. Finally, the friction behaviour is evaluated in the context of the model and optimal value combinations of the surface texture parameters are identified for different lubrication conditions.

Restructured camel meat sausage product

At present Kazakhstan cannot fully provide itself with domestic production meat, and accordingly the search for domestic raw material resources of the region is relevant. In this connection it is necessary to utilise the available resources of meat species. Possible ways of utilisation of researches and developments in the field of technology of restructured sausages from camel meat are revealed. Research determines the optimum proportions of ingredients and additives to achieve the desired texture, flavour and quality of the product. Research can lead to the development of new methods and technologies that improve the sausage making process, including restructuring of camel meat. Application of restructuring in combination with structure-forming substances allows to regulate organoleptic and structural-mechanical properties of products, to attract into production raw materials limited by traditional technologies, chemical composition of finished products, to expand the assortment, to increase the depth of processing of meat raw materials, to increase the yield of finished products and profitability of production.The research results of the technology of restructured sausages from camel meat can find practical application in various fields. In general, the practical application of the research results on the development of restructured camel meat sausage technology can contribute to product diversification, health improvement and development of new market segments. The innovative technological product in this area will be "restructured camel meat sausage". The sausage is restructured using modern technology and techniques to achieve the optimum texture and flavour of the product. The food industry can also utilise an innovative technological product such as restructured camel meat sausage. This product is part of the range of sausage and meat products available in the market.

Effect of textured parameters on tribological properties of hydrodynamic journal bearing

PurposeThis study aims to improve the tribological properties of hydrodynamic journal bearing via surface texture, as well as the wear and antifriction mechanisms of textured bearing were represented. It provides a design direction for solving the tribological problem of rotor-bearing system.Design/methodology/approachIn this paper, the variation of surface texture parameters (e.g. texture diameter, d; area density, sp; and depth, hp) were analyzed based on finite difference method. The optimal surface texture parameters were obtained by designing orthogonal experiments, and the relationship between friction and wear properties and microstructure was studied via combining electron probe microanalyzer, scanning electron microscope, X-ray diffractometer and friction and wear testing machine.FindingsDimensionless film pressure P increased as the d increased, whereas P first increased and then decreased as the sp and hp increased, and the maximum P was got as sp = 15% and hp = 25 µm, respectively. The friction coefficient of textured surface with suitable parameters was effectively reduced and the textured surface with the best antifriction effect was 5#. Orthogonal experimental design analysis showed that the influence order of factors on friction coefficient was as follows: sp > sp × d > d > d × hp > hp > sp × hp and the friction coefficient first decreased and then increased as the sp, d and hp increased. In addition, the friction and wear mechanism of textured bearing were three body friction and abrasive wear as the matrix structure and hard phase were a single β phase and Mn5Si3, respectively. While the antifriction mechanism of textured surface was able to store abrasive particles and secondary hydrodynamic lubrication was formed.Originality/valueThe sample with reasonable texture parameter design can effectively reduce friction and wear of hydrodynamic journal bearing without reducing the service life, which can provide a reference for improving the lubrication performance and mechanical efficiency of rotor-bearing system.

Synergistic formulation approach for developing pea protein and guar gum enriched olive oil-in-water emulsion gels as solid fat substitutes: Formulation optimization, characterization, and molecular simulation

This study aimed to optimize the formulation of olive oil-in-water (O/W) emulsion gels by incorporating Pea Protein (PP) and Guar Gum (GG) as alternative options for solid fats. The optimum rheological (consistency index, apparent viscosity, recovery) and texture (firmness) properties of the emulsion gels were obtained using a mixture of 2 % PP, 1 % GG, 60 % Olive Oil (OO), and 37 % Water (W). The blend of PP2/GG1 showed the highest results for recovery and firmness, 111.27 % and 33.89 g, respectively. PP/GG blend emulsion gels exhibited higher absolute ζ-potential values, ranging between −72.3 and −77.4 mV. The polydispersity index (PDI) ranged from 0.185 to 0.535, with the most uniform distributions found in the PP/GG blend emulsion gels. Strong phase separation resistance indicated strong stability of PP-GG complex emulsion gels. Higher PP concentrations decreased emulsion oxidation. FTIR and XRD research showed that PP and GG interact strongly, indicating good compatibility. The free binding energy of the most stable configuration of the molecules was −6.8 kcal mol−1, indicating a high affinity. PP interacted with GG through 9 amino acid residues, with notable residues being Asp 224, Thr 235, Ala 332, Ile 334, and Arg 336, and their respective interaction distances ranged between 2.69 Å and 3.87 Å.

A single-building damage detection model based on multi-feature fusion: A case study in Yangbi

Accurate and effective identification, determination of the location, and classification of damaged buildings are essential after destructive earthquakes. However, the accuracy of image change detection is limited because of the many texture features and changes in non-building information. In this context, a model for single-building damage detection based on multi-feature fusion is proposed. First, the normalized Digital Surface Model (nDSM) was extracted from the DSM through iterative filtering and point cloud thinning, followed by the extraction of building contour information. Next, single-building images were generated from different data sources through the region of interest (ROI), and the optimal texture feature parameters were extracted for fusion. Afterward, principal-component analysis (PCA) was conducted to suppress multi-feature correlation-induced information redundancy. Finally, the damage to buildings was quantitatively evaluated, and the model was compared with 13 models. The results confirmed the practicability of the model for the Yangbi MS6.4 and Honghe MS5.0 earthquakes.

Optimization design of irregular grooved texture on the surface of sliding pair based on adaptive genetic algorithm

PurposeThis study aims to investigate the effects of irregular groove textures on the friction and wear performance of sliding contact surfaces. These textures possess multiple depths and asymmetrical features. To optimize the irregular groove texture structure of the sliding contact surface, an adaptive genetic algorithm was used for research and optimization purposes.Design/methodology/approachUsing adaptive genetic algorithm as an optimization tool, numerical simulations were conducted on surface textures by establishing a dimensionless form of the Reynolds equation and setting appropriate boundary conditions. An adaptive genetic algorithm program in MATLAB was established. Genetic iterative methods were used to calculate the optimal texture structure. Genetic individuals were selected through fitness comparison. The depth of the groove texture is gradually adjusted through genetic crossover, mutation, and mutation operations. The optimal groove structure was ultimately obtained by comparing the bearing capacity and pressure of different generations of micro-convex bodies.FindingsAfter about 100 generations of iteration, the distribution of grooved textures became relatively stable, and after about 320 generations, the depth and distribution of groove textures reached their optimal structure. At this stage, irregular texture structures can support more loads by forming oil films. Compared with regular textures, the friction coefficient of irregular textures decreased by nearly 47.01%, while the carrying capacity of lubricating oil films increased by 54.57%. The research results show that irregular texture structures have better lubrication characteristics and can effectively improve the friction performance of component surfaces.Originality/valueSurface textures can enhance the friction and lubrication performance of metal surfaces, improving the mechanical performance and lifespan of components. However, surface texture processing is challenging, as it often requires multiple experimental comparisons to determine the optimal texture structure, resulting in high trial-and-error costs. By using an adaptive genetic algorithm as an optimization tool, the optimal surface groove structure can be obtained through simulation and modeling, effectively saving costs in the process.

Preparation of MgO Self-Epitaxial Films for YBCO High-Temperature Coated Conductors.

Ion beam-assisted deposition (IBAD) has been proposed as a promising texturing technology that uses the film epitaxy method to obtain biaxial texture on a non-textured metal or compound substrate. Magnesium oxide (MgO) is the most well explored texturing material. In order to obtain the optimal biaxial texture, the actual thickness of the IBAD-MgO film must be controlled within 12nm. Due to the bombardment of ion beams, IBAD-MgO has large lattice deformation, poor texture, and many defects in the films. In this work, the solution deposition planarization (SDP) method was used to deposit oxide amorphous Y2O3 films on the surface of Hastelloy C276 tapes instead of the electrochemical polishing, sputtering-Al2O3 and sputtering-Y2O3 in the commercialized buffer layer. An additional homogeneous epitaxy MgO (epi-MgO) layer, which was used to improve the biaxial texture in the IBAD-MgO layer, was deposited on the IBAD-MgO layer by electron-beam evaporation. The effects of growth temperature, film thickness, deposition rate, and oxygen pressure on the texture and morphology of the epi-MgO film were systematically studied. The best full width at half maximum (FWHM) values were 2.2° for the out-of-plane texture and 4.8° for the in-plane texture for epi-MgO films, respectively. Subsequently, the LaMnO3 cap layer and YBa2Cu3O7-x (YBCO) functional layer were deposited on the epi-MgO layer to test the quality of the MgO layer. Finally, the critical current density of the YBCO films was 6 MA/cm2 (77 K, 500 nm, self-field), indicating that this research provides a high-quality MgO substrate for the YBCO layer.

Using an optimized texture index to monitor the nitrogen content of potato plants over multiple growth stages

Plant nitrogen content (PNC) is vital for evaluating crop nitrogen nutrient status and for net primary productivity. Therefore, rapid and accurate acquisition of crop PNC information can help reduce fertilizer and increase efficiency in modern agriculture. This study investigated whether an optimized texture index constructed from hyperspectral characteristic wavelength images acquired from a unmanned aerial vehicle (UAV) may be used to estimate PNC in potatoes over multiple growth stages. A potato field trial conducted in 2019 in Beijing, China, included different planting densities, nitrogen fertilizer gradients, and potato varieties with three replicates. A UAV remote sensing platform served to acquire hyperspectral images during three critical N demand periods for potatoes. In addition, we simultaneously conducted field sampling to obtain ground-truth PNC measurements. Following the classical form of vegetation indices, 12 texture indices were constructed based on hyperspectral texture features, and an arbitrary variable combination optimization algorithm was used to optimize these indices. Finally, the texture index, which had the highest correlation with potato PNC, was used as the best indicator for estimating the PNC status of potato at multiple growth stages and whether this indicator, in combination with spectral information, could further improve the accuracy of potato PNC estimation was subsequently explored. The results showed that (i) the optimal texture index TTI3 (R494-Cor, R578-Hom, R514-Sem) maintained a good linear relationship with PNC at the late stage of potato growth, and the accuracy and stability of the PNC estimation models constructed based on it was significantly better than that of a single texture metric. (ii) Compared with spectral information alone, the texture index combined with spectral features improved the accuracy of potato PNC estimation. More specifically, TTI3 (R494-Cor, R578-Hom, R514-Sem) combined with the three-band spectral index TBI 5 (530, 734, 514) achieved the best estimation accuracy with calibrated R2, root mean square error (RMSE), and normalized RMSE of 0.77%, 0.28%, and 9.88%, respectively. The results of this study showed that the texture index constructed by combining multiple texture metrics enhanced the association between texture features and potato PNC over multiple growth stages, thus improving the monitoring accuracy of potato nitrogen nutrition status. This study can provide a reference for accurately managing crop nitrogen nutrition.

Research on Internal Flow Field Characteristics of Straight-Groove Texture Using Three-Dimensional Modeling

Surface texture modification is a reasonable strategy for improving the tribological property of friction pairs. The internal flow behavior of the surface texture significantly impacts its performance. In this study, a three-dimensional computational fluid dynamics (CFD) model is constructed to explore the internal flow behavior of the straight-groove texture in the thrust bearing. The influences of the Reynolds number, depth ratio, and area ratio of the straight-groove texture on the internal flow behavior are systematically investigated. Furthermore, the streamline and tribological performance parameters are checked to reveal the mechanism of the groove texture influencing the tribological properties. It is found that the vortex and cavitation significantly affect the tribological performance of textured surfaces under hydrodynamic lubrication. The cavitation and upstream vortex areas increase with the Reynolds number, while the downstream vortex area shows a reverse trend. The increase in depth ratio strengthens the upstream and downstream vortexes while reducing the cavitation area. Additionally, a method is proposed to determine the location of the cavitation within the groove texture. Certain operating conditions create the optimal texture depth ratio and area ratio, which could maximize the load-carrying capacity (LCC) of the oil film, and the friction coefficient is relatively small.

Anti-icing characteristics of hierarchical micro-dimple textured surface generated by 3D resonant vibration assisted machining

Anti-icing techniques are essential for safe functioning in applications such as aircraft and power transmission lines. In the present study, a long lasting and minimal maintenance required hierarchical micro-dimple surface textures were produced on a cylindrical specimen using a dual frequency surface texturing method. The water contact angle and time delay in freezing on five diverse type of micro-dimples with three different surface texture densities and non-textured surfaces were examined and compared experimentally. The textured surfaces showed an approximately 28 % increase in delayed time compared to non-textured surfaces. A size effect on the performance of micro-dimples was observed. Smaller size micro-dimples have better delayed time for higher texture density surfaces. The delayed time increases with the increase in micro-dimple size for optimum and low texture density surfaces. Introducing of hierarchical structures increased the delayed time by 8 % compared to single scale micro-dimple surface textures. The idea of introducing hierarchical micro-dimples as an anti-icing technique will directly result in improving the delayed time, which in turn reduces the load on the secondary system for ice removing and improves its working efficiency.

Performance of hydrodynamic textured journal bearing with the combined influence of elastic deformation and pseudo-plastic lubricant

This study outlines the performances of finite-length journal bearing with textured liner, shear-thinning Rabinowitsch lubricants, and considers the elastic deformation of the bearing liner. The yielded nonlinear Rabinowitsch-Reynolds PDE system has been solved using the finite difference method combined with Elrod’s algorithm for the case of cylindrical textures. The static performance of hydrodynamic lubrication, in this case, involves different parameters such as depth of texture, eccentricity ratio, elastic deformation factor, rheological index, etc. Results showed that, at a fixed eccentricity ratio, texturing of the bearing’s converging area enhances significantly the load-carrying capacity and reduces the friction coefficient compared to the smooth bearing surface (up to [Formula: see text] in load capacity and [Formula: see text] in friction coefficient). On the other hand, using a lubricant with more pronounced shear-thinning behavior and/or elastic liner reduces the journal bearing performances (up to [Formula: see text] in load capacity and [Formula: see text] in pressure). By applying the optimization technique (particle swarm optimization), an optimal arrangement of textures that can compensate for these losses in performance even at high eccentricity has been found. This optimal texture with dimensionless dimple’s depth set to 1 [Formula: see text] improves the load capacity with [Formula: see text] for a lubricant with a rheological index [Formula: see text] and elastic bearing liner with deformation parameter [Formula: see text].

Improvement of lubrication performance of sliding pairs with multi-depth groove textures based on genetic algorithm

During the wear and tear process of bearings, the friction coefficient between the friction pairs can be effectively decreased by employing the suitable surface texture on the frition surface. In the study, the distribution and depth variation of the surface texture were used as variables, and the genetic algorithm was used for iterative optimization to obtain the optimal texture distribution and depth. The friction and wear performance of the rectangular texture bearing sliding blocks was optimized. The depth of the texture was represented by a 4-bit binary number, and different binary numbers were set to represent different texture depths. Finally, the genetic algorithm was used for continuous iteration and evolution to obtain the optimal texture combination. The study showed that, compared with the regular texture with a depth of 0.2 μm, the friction coefficient decreased by 15.0% under the optimal texture with a non-uniform depth. Simultaneously, compared with the regular 3 μm deep texture, texture with a optimized depth makes the friction coefficient decreased by 37.5%, and the minimum oil film thickness increased by 0.979 μm. The optimal texture and oil film thickness combination obtained from the study can effectively reduce solid contact force and alleviate mechanical wear.

Multispectral versus texture features from ZiYuan-3 for recognizing on deciduous tree species with cloud and SVM models

Tree species recognition accuracy greatly affects forest remote sensing mapping and forestry resource monitoring. The multispectral and texture features of the remote sensing images from the ZiYuan-3 (ZY-3) satellite at two phenological phases of autumn and winter (September 29th and December 7th) were selected for constructing and optimizing sensitive spectral indices and texture indices. Multidimensional cloud model and support vector machine (SVM) model were constructed by the screened spectral and texture indices for remote sensing recognition of Quercus acutissima (Q. acutissima) and Robinia pseudoacacia (R. pseudoacacia) on Mount Tai. The results showed that, the correlation intensities of the constructed spectral indices with tree species were preferable in winter than in autumn. The spectral indices constructed by band 4 showed the superior correlation compared with other bands, both in the autumn and winter time phases. The optimal sensitive texture indices for both phases were mean, homogeneity and contrast for Q. acutissima, and contrast, dissimilarity and second moment for R. pseudoacacia. Spectral features were found to have a higher recognition accuracy than textural features for recognizing on both Q. acutissima and R. pseudoacacia, and winter showing superior recognition accuracy than autumn, especially for Q. acutissima. The recognition accuracy of the multidimensional cloud model (89.98%) does not show a superior advantage over the one-dimensional cloud model (90.57%). The highest recognition accuracy derived from a three-dimensional SVM was 84.86%, which was lower than the cloud model (89.98%) in the same dimension. This study is expected to provide technical support for the precise recognition and forestry management on Mount Tai.

Hierarchically Nanoporous Carbon for CO2 Capture and Separation: Roles of Morphology, Porosity, and Surface Chemistry

Nanoporous carbons, as promising adsorbents for CO2 capture and separation from flue gas, span the major challenge of optimizing the physicochemical property to meet the need of CO2 capture and separation. Herein, we rationally engineered sustainable nanoporous carbons with tailorable porosity and rich surface chemistry using an activating agent of small-molecule organic acids. The porosity could be easily tailored by varying the activation temperature and activator dosage. The surface chemistry and morphology could be tuned by changing the dopant concentration. In detail, porosity played a determinant role in CO2 adsorption capacity, especially ultramicroporosity. Narrow micropores brought major contributions to CO2 uptake at low pressure, while mesopores played a great role at high adsorption pressure. Surface chemistry played a critical role in CO2 capture, especially adsorption selectivity and the isosteric heat of adsorption when the ultramicroporosity was optimal. We revealed that the C–OH group (one type of oxygen-containing species) made a relatively high contribution for improving CO2 capture by hydrogen bonding. For nitrogen-containing groups, the pyridinic-N, pyrrolic-N, and amine groups provoked more improvements in CO2 capture by offering more basic CO2-philic sites, while the oxidized-N group made a weak influence. Sheet-like structures exhibited more accessible affine sites for fast CO2 capture and separation. We proposed a valuable reference for rationally engineering nanoporous carbons with the optimal texture to meet the different requirements of CO2 capture. More importantly, the conversion of biowaste into high-added-value adsorbents for effective CO2 capture and separation opened up an elegant route of killing two birds with one stone.

Physical-chemical, water distribution and protein conformation of reduced-fat frankfurters with Artemisia sphaerocephala Krasch gum to substitute pork back fat

This study evaluated the potential of Artemisia sphaerocephala Krasch gum (ASK gum) solution as a fat replacer. With an increased proportion of ASK gum, the fat and energy of frankfurters significantly decreased, the cooking yield significantly increased, and the L* values exhibited an upward trend. Frankfurters with 75% pork back fat substitution by ASK gum yielded optimal gel strength and texture. Low-field nuclear magnetic resonance (LF-NMR) analysis indicated that ASK gum reduced the T2 relaxation time and heightened the percentage of immobilized water. Meanwhile, an increase in the β-sheet and decreased α-helix content indicated that ASK gum altered the protein secondary structure. Moreover, ASK gum addition significantly changed the dynamic rheological properties and microstructure. Partial fat substitution with ASK gum did not impact the sensory score. Overall, using ASK gum solution to partially substitute animal fat could be a promising approach for improving the gel quality of reduced-fat frankfurters.

Estimation of Aboveground Biomass for Winter Wheat at the Later Growth Stage by Combining Digital Texture and Spectral Analysis

Aboveground biomass (AGB) is an important indicator used to predict crop yield. Traditional spectral features or image textures have been proposed to estimate the AGB of crops, but they perform poorly at high biomass levels. This study thus evaluated the ability of spectral features, image textures, and their combinations to estimate winter wheat AGB. Spectral features were obtained from the wheat canopy reflectance spectra at 400–1000 nm, including original wavelengths and seven vegetation indices. Effective wavelengths (EWs) were screened through use of the successive projection algorithm, and the optimal vegetation index was selected by correlation analysis. Image texture features, including texture features and the normalized difference texture index, were extracted using gray level co-occurrence matrices. Effective variables, including the optimal texture subset (OTEXS) and optimal normalized difference texture index subset (ONDTIS), were selected by the ranking of feature importance using the random forest (RF) algorithm. Linear regression (LR), partial least squares regression (PLS), and RF were established to evaluate the relationship between each calculated feature and AGB. Results demonstrate that the ONDTIS with PLS based on the validation datasets exhibited better performance in estimating AGB for the post-seedling stage (R2 = 0.75, RMSE = 0.04). Moreover, the combinations of the OTEXS and EWs exhibited the highest prediction accuracy for the seeding stage when based on the PLS model (R2 = 0.94, RMSE = 0.01), the post-seedling stage when based on the LR model (R2 = 0.78, RMSE = 0.05), and for all stages when based on the RF model (R2 = 0.87, RMSE = 0.05). Hence, the combined use of spectral and image textures can effectively improve the accuracy of AGB estimation, especially at the post-seedling stage.