Texture Index Research Articles

In-situ nanoscale precipitation behavior and strengthening mechanism of WC/IN718 composites manufactured by laser powder bed fusion

Nickel matrix composites (NMCs) have received much attention as they show excellent high temperature performances, which could meet the demand for hot-end components in severe environments. In this study, the duplex in-situ nanoscale carbides and oxides reinforcing IN718 were fabricated by laser powder bed fusion (LPBF) and addition of submicron WC particles. The microstructural features and mechanical properties were investigated with variations of WC content (0, 0.5, 2, and 4 wt%). The columnar dendrites with strong <001> texture that boundary distributed continuous Laves phase were found in the LPBF-processed IN718. Incorporating WC led to higher laser absorption and processability, whereas it also decreased the anisotropy of the matrix featured by weak texture index. With an optimized content of 2 wt%, the WC was dissolved into W and C atoms under laser irradiation, which promoted the homogeneous precipitation of intergranular cubic (Nb, Ti)C (∼50 nm) and intragranular spherical Al2O3 (∼20 nm). These precipitates would for heterogenous nucleation to refine grains (∼12.6 μm) and bring about high-density dislocations. The yield strength (YS) and ultimate tensile strength (UTS) increased by 28.8% and 26.5%, respectively, while maintaining high elongation compared with reported NMCs, demonstrating a favorable strength-ductility combination. Finally, the mechanism of microstructure evolution and strengthening was elucidated.

Exploring fine-scale urban landscapes using satellite data to predict the distribution of Aedes mosquito breeding sites

BackgroundThe spread of mosquito-transmitted diseases such as dengue is a major public health issue worldwide. The Aedes aegypti mosquito, a primary vector for dengue, thrives in urban environments and breeds mainly in artificial or natural water containers. While the relationship between urban landscapes and potential breeding sites remains poorly understood, such a knowledge could help mitigate the risks associated with these diseases. This study aimed to analyze the relationships between urban landscape characteristics and potential breeding site abundance and type in cities of French Guiana (South America), and to evaluate the potential of such variables to be used in predictive models.MethodsWe use Multifactorial Analysis to explore the relationship between urban landscape characteristics derived from very high resolution satellite imagery, and potential breeding sites recorded from in-situ surveys. We then applied Random Forest models with different sets of urban variables to predict the number of potential breeding sites where entomological data are not available.ResultsLandscape analyses applied to satellite images showed that urban types can be clearly identified using texture indices. The Multiple Factor Analysis helped identify variables related to the distribution of potential breeding sites, such as buildings class area, landscape shape index, building number, and the first component of texture indices. Models predicting the number of potential breeding sites using the entire dataset provided an R² of 0.90, possibly influenced by overfitting, but allowing the prediction over all the study sites. Predictions of potential breeding sites varied highly depending on their type, with better results on breeding sites types commonly found in urban landscapes, such as containers of less than 200 L, large volumes and barrels. The study also outlined the limitation offered by the entomological data, whose sampling was not specifically designed for this study. Model outputs could be used as input to a mosquito dynamics model when no accurate field data are available.ConclusionThis study offers a first use of routinely collected data on potential breeding sites in a research study. It highlights the potential benefits of including satellite-based characterizations of the urban environment to improve vector control strategies.

Effects of blown sand and soil properties on the abrasion rate of compacted soil

Abrasion caused by blown sand is an important process for compacted soils in grasslands. The abrasion rate is affected by a combination of the blown sand intensity and the properties of the underlying soil, but there is a paucity of relevant studies of the simultaneous effects of these factors, especially quantitative ones. Our wind tunnel simulation experiments showed that the abrasion rate of compacted soil increased rapidly with increasing transport rate of the abrasive particles and with increasing soil texture index (δ = (sand% + silt%)/[clay% + organic matter% + CaCO3%]), and decreased significantly with increasing soil compaction. Accordingly, we developed an abrasion rate prediction model that accounted for the transport rate of windblown particles (abraders) and the physicochemical properties of the compacted soil. The effects of abrader properties (grain size and basic density), abrader availability, and a limited upwind supply of the abraders on the abrasion rate of compacted soils were also addressed. We developed a comprehensive abrasion-rate equation that accounted for both particle size and the basic density of the abraders.

Textural features for BLB disease damage assessment in paddy fields using drone data and machine learning: Enhancing disease detection accuracy

Detecting Bacterial Leaf Blight (BLB) in paddy fields is a critical challenge in Indonesia, where the disease poses a significant threat to rice production by reducing the photosynthetic ability and ultimately compromising plant productivity. This study explored the effectiveness of using drone-acquired data for textural analysis in paddy fields in West Java, with the aim of improving BLB detection by integrating textural and thermal characteristics. Utilizing advanced machine learning techniques, we combined drone data to assess different levels of damage caused by BLB. The normalized difference texture index, derived from the Haralick textural features, was employed as a key predictor. Our findings demonstrate that the inclusion of textural features markedly enhances disease detection accuracy compared with traditional methods based solely on spectral indices. Specifically, the random forest algorithm, which integrates texture and vegetation indices, achieved an impressive classification accuracy of 0.984. This innovative approach offers a robust, non-invasive solution for detecting BLB, significantly contributing to the protection of crop yields and addressing global food security challenges. This study underscores the potential of advanced remote sensing technologies and machine learning to revolutionize agricultural disease management.

Improved estimation of canopy water status in cotton using vegetation indices along with textural information from UAV-based multispectral images

Precise and timely estimation of crop canopy water status is of great importance for precision irrigation. The unmanned aerial vehicle (UAV)-based remote sensing technology has become increasingly popular for crop canopy water status estimation. Nevertheless, the capability of vegetation indices (VIs) along with textural information from high-resolution imagery for estimating cotton canopy water status has been rarely explored. The VIs, texture features (TFs), and texture indices (TIs) were obtained from UAV multispectral images of cotton with different irrigation levels and nitrogen rates in the southern Xinjiang of China. The performances of three machine learning models, i.e. support vector machine (SVM), back-propagation neural network (BPNN) and extreme gradient boosting (XGBoost) were evaluated for estimating canopy equivalent water thickness (CEWT) and crop water stress index (CWSI) throughout the cotton growing season. The results showed that combining vegetation indices and textural information significantly improved the estimation accuracy of models compared to vegetation indices or textural information alone. The XGBoost_VIs + TFs model exhibited the best accuracy in estimating CEWT (R2 = 0.75, RMSE = 0.01 cm, RE = 19.46 % at upper half-canopy level, and R2 = 0.65, RMSE = 0.02 cm, RE = 24.59 % at all-canopy level), while the XGBoost_VIs + TFs + TIs model performed best in predicting CWSI among the models (R2 = 0.90, RMSE = 0.05, RE = 5.84 %). Although CEWT estimation was fair to some extent, CWSI estimation was more applicable for diagnosing cotton water stress. The CWSI maps created from the optimal XGBoost_VIs + TFs + TIs model intuitively reflected the cotton canopy water status under various irrigation levels and nitrogen rates, which could help farmers implement timely and precision irrigation in cotton production.

Sensory Evaluation of Vacuum-Packed Rice Eel (Monopterus albus) Fillet Marinade

This study focused on the formulation of a good marinate substitute for vacuum-packed fillets. Treatment marinates are vinegar (T1), lemon juice (T2), calamansi juice (T3), and sweet orange juice (T4), with the same amount of salt, garlic powder, white pepper, and chili powder. Acceptability was determined by its sensory attributes such as taste, odor, color, texture, and Acceptability Composite Index. Results revealed that T 2 (rice eel fillet marinated with 75 ml lemon juice is the most acceptable treatment among the four treatments. In the sensory acceptability, there are no significant differences in the product's hedonic odor, taste, and texture among the four treatments. However, there is a significant difference in the hedonic color of the product among the 4 treatments. Different marinades can be developed for rice eel to improve its taste and acceptability in terms of color, aroma, and texture.

Estimation of Chlorophyll Content in Apple Leaves Infected with Mosaic Disease by Combining Spectral and Textural Information Using Hyperspectral Images

Leaf chlorophyll content (LCC) is an important indicator of plant nutritional status and can be a guide for plant disease diagnosis. In this study, we took apple leaves infected with mosaic disease as a research object and extracted two types of information on spectral and textural features from hyperspectral images, with a view to realizing non-destructive detection of LCC. First, the collected hyperspectral images were preprocessed and spectral reflectance was extracted in the region of interest. Subsequently, we used the successive projections algorithm (SPA) to select the optimal wavelengths (OWs) and extracted eight basic textural features using the gray-level co-occurrence matrix (GLCM). In addition, composite spectral and textural metrics, including vegetation indices (VIs), normalized difference texture indices (NDTIs), difference texture indices (DTIs), and ratio texture indices (RTIs) were calculated. Third, we applied the maximal information coefficient (MIC) algorithm to select significant VIs and basic textures, as well as the tandem method was used to fuse the spectral and textural features. Finally, we employ support vector regression (SVR), backpropagation neural network (BPNN), and K-nearest neighbors regression (KNNR) methods to explore the efficacy of single and combined feature models for estimating LCC. The results showed that the VIs model (R2 = 0.8532, RMSE = 2.1444, RPD = 2.6179) and the NDTIs model (R2 = 0.7927, RMSE = 2.7453, RPD = 2.2032) achieved the best results among the single feature models for spectra and texture, respectively. However, textural features generally exhibit inferior regression performance compared to spectral features and are unsuitable for standalone applications. Combining textural and spectral information can potentially improve the single feature models. Specifically, when combining NDTIs with VIs as input parameters, three machine learning models outperform the best single feature model. Ultimately, SVR achieves the highest performance among the LCC regression models (R2 = 0.8665, RMSE = 1.8871, RPD = 2.7454). This study reveals that combining textural and spectral information improves the quantitative detection of LCC in apple leaves infected with mosaic disease, leading to higher estimation accuracy.

Milk Proteins as Alternative Ingredients Replacing Sugar and Their Effects on Chemical, Technological and Sensory Quality of Cookies

ABSTRACT This paper aims to evaluate nutritional, technological and sensory characteristics of cookies with reduced sugar content and added milk proteins. Five formulations of cookies had their weight, height, weight loss, yield, apparent volume, specific volume, spread ratio, color, pH, texture, proximate composition, acceptability, purchase intention and acceptability index were evaluated. Cookies made with low sugar presented promising quality for apparent volume, specific volume, spread ratio and yield. High protein and ashes content, lower moisture and low sugar concentration were also observed. Treatments added with whey protein highlighted in the sensory analysis and reached a satisfactory acceptability index. Cocoa powder can covered up the sugar reduction and can be used in cookies formulation to diminish sugar. Casein did not show potential as an alternative for cookies. Cookies produced with low sugar and added with whey protein had appropriate chemical, technological and sensory characteristics.

Influence of Image Compositing and Multisource Data Fusion on Multitemporal Land Cover Mapping of Two Philippine Watersheds

Cloud-based remote sensing has spurred the use of techniques to improve mapping accuracy where individual images may have lower quality, especially in areas with complex terrain or high cloud cover. This study investigates the influence of image compositing and multisource data fusion on the multitemporal land cover mapping of the Pagsanjan-Lumban and Baroro Watersheds in the Philippines. Ten random forest models for each study site were used, all using a unique combination of more than 100 different input features. These features fall under three general categories. First, optical features were derived from reflectance bands and ten spectral indices, which were further subdivided into annual percentile and seasonal median composites; second, radar features were derived from ALOS PALSAR by computing textural indices and a simple band ratio; and third, topographic features were computed from the ALOS GDSM. Then, accuracy metrics and McNemar’s test were used to assess and compare the significance of about 90 pairwise model outputs. Data fusion significantly improved the accuracy of multitemporal land cover mapping in most cases. However, image composition had varied impacts for both sites. This could imply local characteristics and feature inputs as potential determinants of the ideal composite method. Hence, the iterative screening or optimization of both input features and composites is recommended to improve multitemporal mapping accuracy.

Study on the influence of aggregate on rheological properties of recycled aggregate asphalt mixture using microstructural quantification

Recycled aggregate (RA) is generated through the crushing process of Construction and Demolition Waste (CDW). Although there has been extensive research on the mechanical properties of recycled aggregate asphalt mixture (RAAM), the impact of recycled aggregate (RA) on its rheological characteristics remains underexplored. This paper aims to assess the rheological properties of recycled aggregate asphalt mixture (RAAM) during the processes of mixing and compaction. Firstly, different percentages of recycled aggregate (RA) were utilized in the design of the recycled aggregate asphalt mixture (RAAM), and rheological behaviors of RAAM during the mixing and compaction stage were characterized by mixing torque and compaction energy, respectively. Then, high-precision three-dimensional models of RA were reconstructed utilizing CT images, and four indices were established to quantitatively assess the macro shape and micro-morphology of RA particles, utilizing the spatial model data for precise measurements. Finally, a correlation was found between rheological properties of RAAM and RA morphology through a comprehensive analysis of laboratory test results and spatial model indicators. Results show that aggregate particles’ microstructural morphology significantly influences asphalt mixture’s rheological behavior during mixing and compaction. Additionally, the 3D spherical degree and Needle-flake index performed more significant correlations with mixing and compaction than the 3D angular index and 3D texture index. As opposed to natural aggregate, RA exhibits more intricate morphological characteristics stemming from its porous cement mortar composition. Increasing the RA content causes more energy consumption and increases the thickness of asphalt mortar film during the compaction process. In general, methods established in this study offer an efficient mean of quantifying the morphological characteristics of aggregate particles, thereby enabling the assessment of the impact of RA on the rheological properties of asphalt mixtures.

Soybean (Glycine max L.) Leaf Moisture Estimation Based on Multisource Unmanned Aerial Vehicle Image Feature Fusion.

Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021-2022), soybean (Glycine max L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients (p < 0.05) were selected as input variables for the model (combination 1: vegetation indices; combination 2: texture features; combination 3: randomly extracted texture indices in combination; combination 4: combination of vegetation indices, texture features, and randomly extracted texture indices). Subsequently, extreme learning machine (ELM), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN) were utilized to model the leaf moisture content. The results indicated that most vegetation indices exhibited higher correlation coefficients with soybean leaf moisture compared with texture features, while randomly extracted texture indices could enhance the correlation with soybean leaf moisture to some extent. RDTI, the random combination texture index, showed the highest correlation coefficient with leaf moisture at 0.683, with the texture combination being Variance1 and Correlation5. When combination 4 (combination of vegetation indices, texture features, and randomly extracted texture indices) was utilized as the input and the XGBoost model was employed for soybean leaf moisture monitoring, the highest level was achieved in this study. The coefficient of determination (R2) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth.

Microstructure, tensile strength, and hardness of AA5024 modified with ZrH4 additions produced by laser powder bed fusion

This work investigates the effect of ZrH4 additions on a commercial AA5024 aluminum alloy produced via Laser Powder Bed Fusion (L-PBF) where pre-mixed powders of AA5024 and ZrH4 were used. The microstructure was characterized using light and scanning electron microscopy assisted by energy-dispersive X-ray spectroscopy, and electron backscattered diffraction. The mechanical properties were evaluated by hardness and tensile measurements. The ZrH4 additions modify the microstructure. Regions without particles originate the large and elongated grains, while regions with undissolved ZrH4 or formed particles during solidification originate a fine equiaxed microstructure. The fine-grained region is typically located at the boundary of the melt pools, while the coarse-grained region is formed within the melt pools. There is substantial grain refinement with additions higher than 2.0 wt% ZrH4, nearly eliminating the coarse-grained region. The grain size does not differ in the fine-grained region for the different ZrH4, cross-sections (perpendicular or along the printing direction), or printing parameters. The 〈110〉 crystallographic fiber texture is identified, and a few particular texture components are more pronounced, such as the Cube {001} 〈100〉, the rotated Cube {001} 〈110〉, the Goss {011} 〈001〉, and Z {111} < 110>. Low texture index values are obtained in the fine-grained region, indicating the highly isotropic microstructure. The hardness and tensile strength increase with the increase in ZrH4 content for the as-built condition. A saturation in tensile strength is reached after 1.0 wt% ZrH4 additions.

Aquatic vegetation mapping with UAS-cameras considering phenotypes

Aquatic vegetation species at the genus level in an oxbow lake were identified in Hungary based on a multispectral Uncrewed Aerial System (UAS) survey within an elongated oxbow lake area of the Tisza River under continental climate. Seven and 13 classes were discriminated using three different classification methods (Support Vector Machine [SVM], Random Forest [RF], and Multivariate Adaptive Regression Splines [MARS]) using different input data in ten combinations: original spectral bands, spectral indices, Digital Surface Model (DSM), and Haralick texture indices. We achieved a high (97.1%) overall accuracies (OAs) by applying the SVM classifier, but the RF performed only <1% worse, as it was represented in the first places of the classification rank before the MARS. The highest classification accuracies (>84% OA) were obtained using the most important variables derived by the Recursive Feature Elimination (RFE) method. The best classification required DSM as an input variable. The poorest classification performance belonged to the model that used only texture indices or spectral indices. On the class level, Stratiotes aloides exhibit the lowest degree of separability compared to the other classes. Accordingly, we recommend using supplementary input data for the classifications besides the original spectral bands, for example, DSM, spectral, and texture indices, as these variables significantly improve the classification accuracies in the proper combinations of the input variables.

A Comparison of Pixel Based and Object Based Image Classification for Cropland Area Estimation

Pixel-based (PB) and object-based (OB) Land Use–Land Cover (LULC) classification techniques can be applied in Google Earth Engine (GEE), a flexible cloud-based platform, because of its numerous state-of-the-art functions that comprise several Machine Learning (ML) methods. Adding some texture measure, any measure to a classification usually improves the accuracy. Object segmentation and object textural analysis are two OB methods that are still uncommon in the GEE environment. Object based image classification is difficult because it can be challenging to concatenate the correct functions and adjust various parameters in order to get past the computational limitations of GEE. The goal of this work is to develop and test an OB classification approach that combines the ML algorithm Random Forest (RF) to perform the final classification, the Gray-Level Co-occurrence Matrix (GLCM) to calculate cluster textural indices and the Simple Non-Iterative Clustering (SNIC) algorithm to identify spatial clusters. The primary seven GLCM indices are subjected to a Principal Components Analysis (PCA) in order to combine the textural data needed for the OB classification into a single band. The proposed methodology was broadly tested in a 304 km2 study area, located in the Telangana state (India), using Sentinel 2 (S2).

A Pixel Texture Index Algorithm and Its Application

A Pixel Texture Index Algorithm and Its Application

Construction and acceptability of different complementary foods texture in infants and young children aged 6 to 23 months

To construct the complementary food texture in infants and young children aged 6 to 23 months, and observe the acceptability of complementary food of different months old infants. Based on the domestic and foreign guidelines, consensus and literatures on complementary feeding, and combined with the characteristics of children&apos;s growth and development in China. The complementary food texture index of 6-23 months old infants and young children was constructed. One province was selected in the south and north respectively, one city and one rural area was selected as the observation point in each province. The stratified random sampling principle was adopted in each observation point, 240 infants and young children were selected for the acceptability study. According to the food type, 12 common foods were selected to make the complementary food toolkit. The parents were instructed to make complementary food at home according to the corresponding month age, observe and record the acceptability of single/mixed complementary food feeding. The complementary food texture index of 6 months, 7-8 months, 9-11 months, 12-17 months, 18-23 months was constructed. Caregivers could make complementary food at the corresponding month age according to the established complementary food texture index. The acceptability of single complementary food for infants and young children aged 6-23 months was 98.3%, 98.7%, 99.8%, 96.9% and 97.5%, respectively. The acceptability of mixed complementary food for children aged 7-23 months was 98.3%, 99.6%, 93.8% and 97.5%, respectively. The complementary food texture index of different month age can be made at home, and the acceptability of different texture is good.

Nondestructive evaluation of harvested cabbage texture quality using 3D scanning technology

Current nondestructive methods for evaluating the texture quality of leafy vegetables have limitations due to their complicated leafy structure. In this study, a promising solution was proposed using 3D scanning technology to nondestructively assess the texture quality of leafy vegetables, and the harvested cabbages were chosen as the experimental samples. The cabbages were scanned to extract the morphological traits, especially for surface features of vein distribution. Results demonstrated that morphological traits exhibited better correlations with texture indices compared to traditional compression features. Texture indices were well predicted based on the XGBoostR algorithm with high R2 values of over 0.89 and low RMSE values. The texture quality of cabbages at different harvesting times analyzed by linear discrimination analysis also showed well-discriminative results with an accuracy exceeding 98.3%. These results successfully indicated that 3D scanning technology was effective in evaluating the texture quality of cabbages, showcasing its potential in the nondestructive texture evaluation of leafy vegetables.

Modelling and Mapping of Forest Biodiversity Indicators Using Sentinel-2 and PlanetScope Remotely Sensed Data

Tropical montane forests harbour exceptionally high biodiversity yet face severe threats from human activities. Assessing forest biodiversity over large areas is crucial yet extremely challenging. Remote sensing provides an efficient monitoring solution, but few studies have focused on Tanzania's diverse, montane forests. We collected field data on tree species composition within 159 plots across montane forests in Tanzania's West Usambara region. We calculated species richness, evenness, and Shannon diversity index as indicators of tree diversity. Using Sentinel-2 and PlanetScope satellite imagery, we derived spectral, textural, and vegetation index predictors to model these indices via generalized additive models and extreme gradient boosting. PlanetScope-based XGBoost models performed best, explaining 19.7% of variation in Shannon diversity. Incorporating textural predictors further improved model accuracy. Despite inherent challenges in modelling complex tropical forests, our findings demonstrate promising potential of Sentinel-2 and PlanetScope for regional biodiversity monitoring where field surveys are limited. Further research could enhance these initial results by leveraging higher resolution data and increasing field sampling for effective monitoring of tropical biodiversity.

Skin Examination Analysis System Using Ayurvedic Interpretation

A human skin moisture measuring device having an interdigitated resistive sensor formed on a plate-like transparent Support is described, with a face designed to be placed into contact with the skin to be subjected to the moisture degree measurement. We can estimate/measure Skin Colour Index , Skin Texture Index, Skin Allergies Index , Skin Radical Index, Means for processing the signal coming from the interdigitated resistive sensor and from the image sensing means give as output a value of the measured moisture degree that is normalized with respect to the actual Surface of contact between skin and sensor. KeyWords: AI/MI,SKIN INDEX

A Hybrid Index for Monitoring Burned Vegetation by Combining Image Texture Features with Vegetation Indices

The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover and the interference of shadow effects caused by terrain. In this work, a novel Vegetation Anomaly Spectral Texture Index (VASTI) is proposed, which leverages the merits of both spectral and spatial texture features to identify abnormal pixels for extracting burned vegetation areas. The performance of the VASTI and its components, the Global Environmental Monitoring Index (GEMI), the Enhanced Vegetation Index (EVI), and the texture feature Autocorrelation (AC) were assessed based on a global dataset previously established, which contains 1774 pairs of samples from 10 different sites. The results illustrated that, compared with the GEMI and EVI, the VASTI improved the user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficient across the ten study areas by approximately 5% to 10%. Compared to AC, the VASTI improved the accuracy of abnormal vegetation detection by 13% to 25%. The improvements were mainly caused by the fact that the incorporation of texture features can reduce spectral confusion between pixels. The innovation of the VASTI is that it considers the relationship between anomalous pixels and surrounding pixels by explicitly integrating spatial texture features with traditional spectral features.