Texture Index Research Articles

Development of a double-layered AA6061 build using additive friction stir processing

In this study, a double-layered AA6061 build was developed using solid-state additive friction stir processing (AFSP) at different linear and tool rotational speeds. The cross-section of the build was examined using optical macroscopy, and microhardness was measured using the Vickers hardness tester. Microstructural evolution was examined using advanced characterization techniques such as electron back scattered diffraction and transmission electron microscopy, while bulk texture evolution was investigated using the X-ray neutron diffraction method. Defect-free builds were attained at rotational speeds of 1100 and 1300 rpm. Compared to the starting alloy, hardness values were significantly lower along the builds, demonstrating loss in the material's strength due to precipitate dissolution. A maximum average hardness of 63.73 HV was observed on the build developed at 1100 rpm, 60 mm/min. AFSP caused grain refinement and reduced grain size to 4.95 μm in the build zone, but dissoluted strengthening β Mg2Si and AlFeMnSi phases due to the intense heat. The microstructure of base material exhibited a texture index of 1.4432, while the microstructure of the build zone showed a reduced texture index of 1.1742. Intense deformation caused randomized grain orientation with reduced texture but retained the shear type S {123}<634>, copper {112}<111> and brass {011}<211> components in the build zone.

Detection of woody species Schinopsis haenkeana using phenological spectral differences and NDVI texture measures in subtropical forests

Schinopsis haenkeana is a native tree species of great importance of South America. Different tree species diverge in their vegetation phenology, providing the opportunity to map their presence based on the seasonal dynamics of vegetation indices. Currently, spatially explicit information on tree species composition provides valuable insights for biodiversity conservation. The objective of this study was to detect the presence of S. haenkeana and its forest status in subtropical forests in central Argentina. We used a combination of RGB-NIR bands and indices (NDVI, EVI, GLI, RGR) derived from Sentinel-2 images. The analyses were processed using the Earth Engine platform and the random forest algorithm was used to discriminate S. haenkeana from other plant species. NDVI texture indices were also used to discriminate different forest states where the species is present. A fruiting period and a leaf color change were detected in July, and spectral differences between fruiting and preceding (May) or subsequent (October) months proved to be highly suitable for discriminating S. haenkeana. The final species presence map achieved an overall accuracy of 91%. Only 0.76% of the total area corresponds to S. haenkeana dense forests. This analysis demonstrated the value of the proposed approach for regularly detecting and mapping S. haenkeana using RGB-NIR spectral information, vegetation indices, and phenological spectral differences. Additionally, it highlighted the importance of using texture indices to differentiate between forests and scrublands, providing suitable data for forest management.

Integrating Spectral, Textural, and Morphological Data for Potato LAI Estimation from UAV Images

The Leaf Area Index (LAI) is a crucial indicator of crop photosynthetic potential, which is of great significance in farmland monitoring and precision management. This study aimed to predict potato plant LAI for potato plant growth monitoring, integrating spectral, textural, and morphological data through UAV images and machine learning. A new texture index named VITs was established by fusing multi-channel information. Vegetation growth features (Vis and plant height Hdsm) and texture features (TIs and VITs) were obtained from drone digital images. Various feature combinations (VIs, VIs + TIs, VIs + VITs, VIs + VITs + Hdsm) in three growth stages were adopted to monitor potato plant LAI using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), random forest (RF), and eXtreme gradient boosting (XGBoost), so as to find the best feature combinations and machine learning method. The performance of the newly built VITs was tested. Compared with traditional TIs, the estimation accuracy was obviously improved for all the growth stages and methods, especially in the tuber-growth stage using the RF method with 13.6% of R2 increase. The performance of Hdsm was verified by including it either as one input feature or not. Results showed that Hdsm could raise LAI estimation accuracy in every growth stage, whichever method is used. The most significant improvement appeared in the tuber-formation stage using SVR, with an 11.3% increase of R2. Considering both the feature combinations and the monitoring methods, the combination of VIs + VITs + Hdsm achieved the best results for all the growth stages and simulation methods. The best fitting of LAI in tuber-formation, tuber-growth, and starch-accumulation stages had an R2 of 0.92, 0.83, and 0.93, respectively, using the XGBoost method. This study showed that the combination of different features enhanced the simulation of LAI for multiple growth stages of potato plants by improving the monitoring accuracy. The method presented in this study can provide important references for potato plant growth monitoring.

Biological stabilization of Arthrospira bioactive-peptides within biopolymers: Functional food formulation; bitterness-masking and nutritional aspects

Physicochemical/biological instability and bitterness are among the major challenges limiting the shelf life and direct use of bioactive-peptides in the production of functional foods. This study focused on the structural modification and improvement of biological, nutritional and techno-functional properties of blue-green algae protein by alcalase (30–180 min). Then, the optimal sample (H-120) was stabilized with maltodextrin (MD), gum-arabic (GA), WPC, alginate (A), and pectin (P) biopolymeric-carriers by the spray-drying process. The MD-WPC-encapsulated peptide presented the highest production yield (∼65%), retention of antioxidant activity, functional, flowability, and stability properties. Morphological (formation of wrinkled, irregular, and dented particles) and chemical (FTIR) evaluations respectively indicated the effect of feed composition on the structural properties and entrapment of peptides (through hydrogen bonds and hydrophobic reactions) in the polymer matrix. Finally, the optimal sample (SDP: MD-WPC) was used at different ratios (1, 2, and 4% w/w) for pan-bread fortification. The examination of physicochemical properties (moisture content, water activity, and specific volume), textural (porosity and compressive strength) indices, a color histogram (crust and crumb), antioxidant activity (DPPH• and ABTS• scavenging), and sensory analysis (color, texture, chewiness, taste, and overall acceptability) indicated biological stability, bitterness-masking, and the feasibility of functional bread production with 2%-SDP.

Combining Texture, Color, and Vegetation Index from Unmanned Aerial Vehicle Multispectral Images to Estimate Winter Wheat Leaf Area Index during the Vegetative Growth Stage

Leaf Area Index (LAI) is a fundamental indicator of plant growth status in agronomy and environmental research. With the rapid development of drone technology, the estimation of crop LAI based on drone imagery and vegetation indices is becoming increasingly popular. However, there is still a lack of detailed research on the feasibility of using image texture to estimate LAI and the impact of combining texture indices with vegetation indices on LAI estimation accuracy. In this study, two key growth stages of winter wheat (i.e., the stages of green-up and jointing) were selected, and LAI was calculated using digital hemispherical photography. The feasibility of predicting winter wheat LAI was explored under three conditions: vegetation index, texture index, and a combination of vegetation index and texture index, at flight heights of 20 m and 40 m. Two feature selection methods (Lasso and recursive feature elimination) were combined with four machine learning regression models (multiple linear regression, random forest, support vector machine, and backpropagation neural network). The results showed that during the vegetative growth stage of winter wheat, the model combining texture information with vegetation indices performed better than the models using vegetation indices alone or texture information alone. Among them, the best prediction result based on vegetation index was RFECV-MLR at a flight height of 40 m (R2 = 0.8943, RMSE = 0.4139, RRMSE = 0.1304, RPD = 3.0763); the best prediction result based on texture index was RFECV-RF at a flight height of 40 m (R2 = 0.8894, RMSE = 0.4236, RRMSE = 0.1335, RPD = 3.0063); and the best prediction result combining texture and index was RFECV-RF at a flight height of 40 m (R2 = 0.9210, RMSE = 0.3579, RRMSE = 0.1128, RPD = 3.5575). The results of this study demonstrate that combining vegetation indices and texture from multispectral drone imagery can improve the accuracy of LAI estimation during the vegetative growth stage of winter wheat. In addition, selecting a flight height of 40 m can improve efficiency in large-scale agricultural field monitoring, as this study showed that drone data at flight heights of 20 m and 40 m did not significantly affect model accuracy.

Influence of the Surface Texture Parameters of Asphalt Pavement on Light Reflection Characteristics

The optical reflection characteristics of asphalt pavement have an important influence on road-lighting design, and the macrotexture and microtexture of asphalt pavement significantly affect its reflection characteristics. To investigate the impact of texture parameters on the retroreflection coefficient of asphalt pavement, the texture indices of rutted plate specimens and field asphalt pavement were obtained by a pavement texture tester, including the macrotexture surface area (S1), microtexture surface area (S2), macrotexture distribution density (D1), microtexture distribution density (D2), root mean square slope (Δq), skewness (Rsk), and steepness (Rku). The corresponding retroreflective coefficient RL was measured by using a retroreflectometer. In the laboratory experiments, rutted specimens of AC-13, SMA-13, and OGFC-13 asphalt mixtures were formed. The changes in texture parameters and the retroreflection coefficient of rutting specimens before and after rolling were studied, and a factor-influence model between macro- and microtexture parameters and RL was established, along with correlation models of the texture index and RL. The results show that after the rutting test, S1, S2, D1, D2, Δq, and Rku decreased, Rsk increased, and RL increased. In the single-factor model, the parameters could be used to characterize RL with high prediction accuracy, whereas for the onsite measurements, the parameters Δq, Rsk, and Rku could well characterize RL. The nonlinear model established, based on the BP neural network algorithm, improved the prediction accuracy. This research provides ideas for optimizing the reflection characteristics of asphalt pavement and a decision-making basis for road-lighting design.

Potato late blight severity monitoring based on the relief-mRmR algorithm with dual-drone cooperation

Late Blight in Potato, a prevalent potato disease, significantly impacts potato yield and stands as one of the principal afflictions affecting the potato crop. Timely and effective monitoring of late blight severity in potato and its spatial distribution has become an immediate priority. In this study, we employed a dual-drone collaborative approach, utilizing the multispectral drone from DJI as well as an ultra-high-resolution RGB drone. By integrating the vegetation index derived from the multispectral UAV, the texture index from the RGB drone, and the estimated crown coverage feature, we combined the relief-mRmR technique with machine learning modeling algorithms to monitor late blight in potato. We compared and constructed severity monitoring models for late blight in potato using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) classification algorithms. The results indicate that the RF model exhibited the highest accuracy. For the training dataset, the overall accuracy (OA) and kappa coefficient were 92.50% and 0.90, respectively, while for the independent validation dataset, the OA and kappa coefficient reached 97.50% and 0.96, respectively. The findings also demonstrate that augmenting the vegetation index and texture index with the estimated high-resolution plant crown coverage information significantly enhances the accuracy of the late blight in potato monitoring model.

Performance Evaluation of Artificial Neural Network Modelling to a Ploughing Unit in Various Soil Conditions

Abstract The specific objective of this study is to find a suitable artificial neural network model for estimating the operation indicators (disturbed soil volume, effective field capacity, draft force, and energy requirement) of ploughing units (tractor disc) in various soil conditions. The experiment involved two different factors, i.e., (Ι) soil texture index and (ΙΙ) field work index, and included soil moisture content, tractor engine power, soil bulk density, tillage speed, tillage depth, and tillage width, which were linked to one dimensionless index. We assessed the effectiveness of artificial neural network and multiple linear regression models between the values predicted and the actual values using the mean absolute error criterion to test data points. When the artificial neural network model was applied, the mean absolute error values for disturbed soil volume, effective field capacity, draft force, and energy requirement were 69.41 m3·hr−1, 0.04 ha·hr−1, 1.24 kN, and 1.95 kw·hr·ha−1, respectively. In order to evaluate the behaviour of new models, the coefficient R 2 was used as a criterion, where R 2 values in artificial neural network were 0.9872, 0.9553, 0.9948, and 0.9718, respectively, for the aforementioned testing dataset. Simultaneously, R 2 values in multiple linear regression were 0.7623, 0.696, 0.492, and 0.5572, respectively, for the same testing dataset. Based on these comparisons, it was clear that predictions using the artificial neural network models proposed are very satisfactory.

Combining vegetation, color, and texture indices with hyperspectral parameters using machine-learning methods to estimate nitrogen concentration in rice stems and leaves

Context or problemNitrogen is one of the important elements of crops, which plays a decisive role in crop growth and development and the formation of yields. Monitoring of rice organ-scale nitrogen concentration based on the unmanned aerial vehicle (UAV) images is of great significance for rice field management and yield prediction. Objective or research questionPrevious studies have focused on the use of traditional statistical methods and chlorophyll-related vegetation indices to construct plant nitrogen concentration, with models lacking generalizability. MethodsIn this study, rice field trials of two varieties (NJ9108, YD6) and nitrogen fertilizer treatments (N0-N3: 0, 105, 210 and 315 kg/ha) were conducted for 3 years with manual sampling and UAV digital and hyperspectral images during key fertility periods. Based on the data of the whole growth periods and combined with vegetation indices (VIs), color indices (CIs), hyperspectral parameters (HPs), texture indices (TIs) and machine-learning algorithms, monitoring models of nitrogen concentration at the organ scale of rice were constructed and used to estimate the N content of multiple organs (leaf and stem) of rice at different periods. Field experiments were used to collect the multi-organ nitrogen concentration of rice and the remote sensing (RS) data of UAV during the critical growth period of the two years (2021, 2022), and machine-learning algorithms were used to construct the estimation models. ResultsThe results showed that VIs had good correlations with leaf nitrogen concentration (LNC), stem nitrogen concentration (SNC) and plant nitrogen concentration (PNC), with correlation coefficients (r) of 0.86, 0.74 and 0.81, respectively. Machine learning estimation models combining multiple types of RS indices were more accurate than single parameter models constructed by traditional statistical methods, with the LNC optimal model (R2 = 0.8, RMSE = 3.83 mg/g), the SNC optimal model (R2 = 0.7, RMSE = 2.43 mg/g) and the PNC optimal model (R2 = 0.7, RMSE = 3.19 mg/g). Validated using data from 2020, the machine-learning models were far more accurate than traditional methods. ConclusionsThese results show that the use of multi-source remote sensing data based on machine-learning algorithms can effectively estimate the nitrogen concentration of organs in rice. ImplicationsThis study provides an accurate, stable and universal method for estimating rice nitrogen concentration in rice organs, which can be used as a reference for estimating rice nitrogen concentration in large fields using UAV RS technology.

Relationship among grain morphology, texture and sliding wear resistance of ZrB2/316L SS composites processed by SLM

Pure 316L stainless steel and 5 wt% ZrB2-reinforced 316L stainless steel were prepared by selective laser melting (SLM) in this paper. As compared to 316L SS, ZrB2/316L SS displays smaller columnar grains as well as less anisotropic microstructure. EBSD results show that the SLM 316L stainless steel exhibits a maximum texture index of 13 for the brass-like texture, while the ZrB2/316L SS composite exhibits a maximum texture index of 7 for the cubic texture. The hardness of the ZrB2/316L SS composite is significantly improved, reaching 415HV, which is much higher than the hardness of 316L SS (215HV). In addition, the wear resistance of ZrB2/316L SS increases, implying broadened applications of the stainless steel composites.

Monitoring leaf area index of the sown mixture pasture through UAV multispectral image and texture characteristics

Leaf area index (LAI) is an important phenotypic trait closely related to photosynthesis, respiration, and water utilization. In recent years, unmanned aerial vehicles (UAVs) multispectral capabilities enable the acquisition of spectral information from visible light to near infrared, facilitating vegetation growth monitoring. This study aims to explore the methodology of combining vegetation indices, color indices, texture information, and ecological factors based on UAV multispectral images to enhance the accuracy of the sown mixture pasture LAI estimation. A field experiment involving 13 mixed sowing combinations of alfalfa (Medicago sativa L.), tall fescue (Festuca elata Keng ex E. Alexeev) and plantain (Plantaga lanceolata L.) was conducted out. Multiple linear regression, Bagging algorithm, support vector machine (SVM), random forest algorithm (RF), KNN algorithm, and back propagation neural network (BP) were used to construct the LAI prediction model. The results showed that combining vegetation index (VI) + color index (CI) + normalized difference texture index (NDTI), and ecological factors (EF) could enhance the accuracy of LAI estimation. The sensitive characteristic combinations for alfalfa, tall fescue, and plantain were found to be NDRE (Normalized difference red-edge index) + NGBDI (Normalized green–blue difference index) + (B) MEA-(G) HOM (Blue band Mean - Green band Homogeneity) + DTR (Daily temperature difference), MSR (Modified simple ratio) + NGRDI (Normalized green–red difference index) + (G) COR-(R) VAR (Green band Correlation – Red band Variance) + DTR (Daily temperature difference)), and MSR (Modified simple ratio) + ExG (Excess green) + (G) SEM-(G) MEA (Green band Second-order moment – Green band Mean) + DTR (Daily temperature difference), respectively. RF exhibited superior prediction capability, further enhancing the accuracy of forage LAI prediction. The alfalfa, tall fescue, and plantain obtained coefficient of determination (R2) of 0.83, 0.79 and 0.79, root mean squared error (RMSE) of 0.50, 0.58 and 0.70, and mean absolute error (MAE) of 0.36, 0.45 and 0.55, respectively. These findings provide valuable insights for the estimation of leaf area index of the sown mixture pasture through UAV multispectral images and texture characteristics.

Revised models for evaluating hydrocarbon saturation of tight sandstone formations based on dielectric dispersion logs

AbstractAccurate estimation of hydrocarbon saturation is significant for log interpreters to evaluate tight sandstone formations. It is difficult to use conventional wireline logs to calculate hydrocarbon saturation in complex reservoirs. Dielectric dispersion logs are processed using dielectric models to obtain accurate saturation assessment, especially in high salinity shaly sand formations. However, the dielectric models consist of complex refractive index method model and shaly sand model ignore the impact of clay effects on complex refractive index method model, and the dielectric dispersion logs generated by them appear several unphysical phenomena in the clay‐bearing formations, such as the permittivity data at the highest frequency are greater than those at the second highest frequency and the conductivity data at the highest frequency are lower than those at the second highest frequency. This paper modifies the dielectric models by introducing a combined conductive phase in complex refractive index method model to correct the unphysical behaviours. Revised models are combined with four permittivity and four conductivity logs acquired by dielectric scanner at 24, 102, 360 and 960 MHz to inverse the water saturation, water salinity, textural index for water‐bearing pore and fraction of clay‐related phase. The complex refractive index method model interprets the logs measured at 960 MHz and shaly‐sand model interprets other dielectric dispersion logs. The particle swarm optimization algorithm is used to search inversion parameters. The results of forward simulation show that the revised models can correct unphysical behaviours of dielectric dispersion logs generated by the original models. Moreover, we apply original models and revised models to laboratory core data. The results of experiments show that the mean relative error values of the original models are 19.38% and 23.60% and of the revised models are 5.54% and 6.28% in two cases. It shows that the revised models are more accurate than the original models to interpret dielectric dispersion logs of tight sandstone reservoirs.

Better Inversion of Wheat Canopy SPAD Values before Heading Stage Using Spectral and Texture Indices Based on UAV Multispectral Imagery

In China’s second-largest wheat-producing region, the mid-lower Yangtze River area, cold stress impacts winter wheat production during the pre-heading growth stage. Previous research focused on specific growth stages, lacking a comprehensive approach. This study utilizes Unmanned Aerial Vehicle (UAV) multispectral imagery to monitor Soil-Plant Analysis Development (SPAD) values throughout the pre-heading stage, assessing crop stress resilience. Vegetation Indices (VIs) and Texture Indices (TIs) are extracted from UAV imagery. Recursive Feature Elimination (RFE) is applied to VIs, TIs, and fused variables (VIs + TIs), and six machine learning algorithms are employed for SPAD value estimation. The fused VIs and TIs model, based on Long Short-Term Memory (LSTM), achieves the highest accuracy (R2 = 0.8576, RMSE = 2.9352, RRMSE = 0.0644, RPD = 2.6677), demonstrating robust generalization across wheat varieties and nitrogen management practices. This research aids in mitigating winter wheat frost risks and increasing yields.

Influence of LaB6 inoculant on the thermodynamics within the molten pool and subsequent microstructure development and cracking behavior of laser powder bed fused TiAl-based alloys

A LaB6 particles decorated Ti48Al2Cr2Nb composite almost free of any cracks has been successfully fabricated by LPBF in this work. To better understand the role of LaB6 inoculant in LPBF-fabricated γ-TiAl alloys, the effects of LaB6 inoculant on the thermal behavior, microstructure evolution and crack sensitivity were investigated by simulation and experimental methods. Based on a single particle model, it was predicted that the addition of LaB6 particle could induce a significant change in temperature gradient G. Due to the LaB6→La2O3 in-situ transformation, the temperature field was severely perturbed, thus causing random G orientations of the matrix surrounding the particle. Furthermore, based on the multi-particle model, predictions indicated that the 0.5 wt% LaB6 inoculant led to a significant decrease of the Gmax from 1.14× 107 °C/m to 6.9 × 106 °C/m but a little influence on solidification rate R at solidified front, therefore contributing to a lower G/R and resultant larger undercooling. In this condition, fine equiaxed grains with random orientations were expected to dominate in the solidified microstructure, which got well verified by the experiments where the maximum texture index dropped from 15.06 to 2.23 and the average grain size decreased from 16.13 ± 8.52 μm to 1.52 ± 1.10 μm due to the addition of LaB6 inoculant. What's more, the LaB6 inoculant also induced an apparent reduce in crack density from 7.02 ± 1.06 mm/mm2 to 0.52 ± 0.12 mm/mm2 and a rise in relative density from 97.65 ± 0.58% to 99.88 ± 0.10%, which could be mainly attributed to grain refinement, grain purification and texture attenuation.

Improving estimation of maize leaf area index by combining of UAV-based multispectral and thermal infrared data: The potential of new texture index

Crop leaf area index (LAI) is one of the important indicators to evaluate crop growth and guide field management, and can be used to predict crop yield. Spectral and thermal information extracted by multispectral (MS) and thermal infrared (TIR) sensors mounted on an unmanned aerial vehicle (UAV) can be used for LAI estimation. Image texture is sensitive to the changes in crop surface grayscale or color characteristics, and can be combined with spectral and thermal information to estimate the LAI. But single texture metric has limitations in LAI estimation. Therefore, the purpose of this study is to construct new texture indices based on texture metrics extracted from MS and TIR images, and combined spectral and thermal information to enhance the estimation accuracy of maize LAI. Three replicates of maize experiments under different irrigation treatments were conducted in 2020. The MS and TIR sensors were mounted on a UAV to acquire maize canopy images during critical growth stages and acquire field LAI value of samples synchronously. The LAI estimation models were established using MS data, TIR data, as well as their combination. These models were constructed by Back Propagation Neural Network (BPNN), Partial least squares regression (PLSR), and Random Forest Regression (RFR). Finally, the performance of LAI estimation models was evaluated by the coefficient of determination (R2), root mean square error (RMSE) and relative root mean square error (rRMSE). Results shown that: (i) Among the eight kinds of texture metrics extracted from MS and TIR images, the texture metric mean (MEA) has the best performance. Compared with single texture metrics, texture indices constructed by different metrics has stronger correlation with LAI. (ii) Adding texture indices to estimation models significantly improved model accuracy, especially multispectral three-texture index(MS-TTI) has higher LAI estimation potential than thermal infrared three-texture index(TIR-TTI). (iii) Compared with the use of MS or TIR data alone, the estimation model constructed by combining MS data and TIR data have better performance. The best estimation model obtained by the RFR method (R2 = 0.862, RMSE = 0.246 and rRMSE = 10.20 %) further improved the LAI estimation of maize, with R2 increasing by 6.55 % and 14.48 %, respectively. In conclusion, the combination of MS and TIR data can effectively improve the estimation accuracy of maize LAI, and also provide a feasible method for monitoring crop growth.

Monitoring selective logging intensities in central Africa with sentinel-1: A canopy disturbance experiment

Forest degradation is a major threat to tropical forests, and effective monitoring using remotely sensed data is subject to significant challenges. In particular, consistent methods for detecting subtle changes in the forest canopy structure caused by selective logging are lacking. Here, using a unique dataset collected in southeastern Cameroon, covering over 22,000 ha of monthly harvesting areas, >6000 locations of harvested trees, and an independent canopy gap dataset developed from an uninhabited aerial vehicle flight (UAV; RGB camera) of approximately 1500 ha, a new method was designed to monitor canopy disturbance and logging intensity in Central Africa. Using Sentinel-1 synthetic aperture radar (SAR) data, the method was conceptualised using a two-step, two-scale approach, which better matched logging practices. First, (non-)harvesting activity areas were identified using textural indices at a spatial resolution of 300 m (step 1), and within these harvesting activity areas, canopy gaps were detected at a resolution of 10 m (step 2). Both steps were based on monthly differences in the Sentinel-1 SAR time series computed using the average of the 12 months preceding and the average of the three months following the month of interest. This method identified harvesting activity areas (step 1 at 300 m resolution) of over 12,004 km2 with high accuracy (omission and commission errors for both classes ≤0.05) and, within them, detected canopy gaps (step 2 at 10 m resolution) with a global accuracy of 0.89. Although some canopy gaps were subject to omission and commission errors (0.39 and 0.05, respectively), this method yielded better results than other available approaches. Compared to the UAV canopy gaps, this method detected most of the small gaps (≤ 500 m2), which represent 80% of all disturbed areas, whereas other available approaches missed at least 70% of these and consequently missed most of the disturbance events occurring in a selectively logged forest. Furthermore, the predictions were correlated with logging intensity, i.e., the number of trees and volume cut per hectare, which are two important criteria for assessing the sustainability of logging activities. This two-step two-scale method for short-term, monthly monitoring of logging disturbances and intensity has strong practical implications for forest administration and certification bodies in Central Africa.

Tongue texture may contribute to the assessment of malignant risk of thyroid nodules.

In the present study, it was aimed to evaluate whether there is an objective tongue image indicator that could be used to evaluate malignant risk of thyroid nodules through a cross sectional study. From December 2018 to December 2020, the TFDA-1 digital tongue-face diagnostic instrument was used to collect the tongue images. TDAS 2.0 software was used for tongue image analysis. A standardized database was constructed by combining patient physical examination results and tongue image analysis results. The relationship between tongue image index and TI-RADS classification of thyroid nodules was tested. A total of 5,900 cases were collected and 4,615 cases were included in the present study after excluding 154 cases due to incomplete information, 1,221 cases with thyroid nodules were separated into 417 cases TI-RADS 2 group, 693 cases in TI-RADS 3 group and 111 cases in TI-RADS 4 group. Without considering confounding factors, tongue image indexes zhiCon, zhiASM, zhiENT, zhiMEAN, zhiClrB, zhiClrR, zhiClrG, zhiClrI, zhiClrL and zhiClrY were significantly different among the three groups (P<0.05). Excluding the influence of age, sex, body mass index, smoking and drinking, the results of one-way variance linear trend analysis showed that the values of zhiCon, zhiENT and zhiMEAN increased with the increasing TI-RADS category, while the values of zhiASM decreased with the increase of TI-RADS category. Tongue texture index may be helpful for differentiating the benign and malignant of thyroid nodules.

Influence of coarse aggregate morphology on the mechanical characteristics of skeleton in porous asphalt concrete

ABSTRACT The properties of coarse aggregates have a great influence on the mechanical characteristics of porous asphalt concrete (PAC). The objective of this study is to reveal the influence of coarse aggregate morphology on the skeleton mechanical characteristics of PAC-13. To achieve this goal, a microstructure analysis method based on X-ray CT was developed to acquire three-dimensional morphological characteristics of coarse aggregates, and the morphology parameters were proposed. Based on the morphology of the actual aggregate, the coarse aggregate skeleton structure and skeleton penetration test were simulated by the discrete element method (DEM) and the values of skeleton strength under different morphologies were determined. The sensitive influences of coarse aggregate morphology parameters on the skeleton strength were quantified by the GA-BP neural network and the recommended value ranges of morphology parameters were proposed. The results show that the morphology of coarse aggregates within the size range of 4.75∼9.5mm and 9.5∼13.2mm has the greatest influence on the skeleton strength of PAC-13. The composite flat-elongated index and composite texture index of aggregates in 9.5∼13.2mm and 4.75∼9.5mm, and the composite angularity index of aggregates in 4.75∼9.5mm, which are referred to as Icom(9.5∼13.2), Icom(4.75∼9.5), Tcom(9.5∼13.2), Tcom(4.75∼9.5), AIcom(4.75∼9.5), are the high-sensitive influencing factors to the skeleton strength of PAC-13.

Effect of Pinto Bean Starch Fortification on Bread Texture and Expected Glycemic Index

Pulse starches are known to have a low glycemic index due to their unique starch conformation and high amylose content. Hence, pinto bean starch (PBS) was extracted from pinto beans and added to bread formulations at 0% (control), 5%, 10%, and 15% concentrations. Texture profile analysis (TPA) on the loaves was completed 24 hours after baking. Following TPA, the breads were analyzed to determine their starch hydrolysis profiles and expected glycemic index (eGI). TPA results showed that inclusion of native PBS in bread did not significantly alter textural characteristics (hardness, springiness, and chewiness), except for cohesiveness which was significantly lower in the bread loaves containing PBS (0.52 &amp;ndash; 0.54) compared to the control (0.61). The addition of native PBS (eGI = 37.5) significantly reduced eGI in bread loaves by approximately 2%. However, the final eGI was still within the range considered to be high GI (&amp;gt;70). The findings demonstrated that pinto bean starch may be added to bread to reduce eGI without significantly altering textural properties.

Using the plant height and canopy coverage to estimation maize aboveground biomass with UAV digital images

The rapid and efficient estimation of aboveground biomass (AGB) in maize proves advantageous for growth assessment, grain quality and yield prediction, and timely field management. The unmanned aerial vehicle (UAV) imaging system's adaptable operation and its capability to capture images with comparatively high temporal and spatial resolutions underscore its potential for supplanting conventional techniques in crop AGB measurement. However, previous studies have predominantly focused on utilizing spectral, structural, textural, and vegetation indices extracted from UAV imagery to forecast crop AGB through linear models or machine learning methods. Frequently, these approaches lack essential mechanistic elucidation. This study seeks to formulate a three-dimensional (3D) AGB estimation model for maize inbred lines by utilizing plant height and canopy coverage data obtained from UAV digital images. The results indicated that: (1) The amalgamation of UAV digital imagery with the canopy height model (CHM) proficiently enabled the estimation of maize plant height. The coefficient of determination (R2) and root mean square error (RMSE) values for measured and estimated plant height across various growth stages were 0.93 and 0.17 m, respectively. (2) The maize AGB estimation models employing canopy coverage exhibited higher accuracy in comparison to those relying on plant height within the testing set, with the R2 value for the measured and estimated maize AGB across four genotypes all equal to or greater than 0.91. (3) The accuracy of the maize AGB estimation model, developed by incorporating both comprehensive plant height and canopy coverage, surpassed that achieved by solely utilizing plant height or canopy coverage. In applying different genotypes across multiple years, the AGB estimation model presented in the study demonstrated both high accuracy and versatility. The utilization of UAV digital imaging technology offers an expedient and cost-effective approach for AGB in maize breeding.