Search Results (200)

Rising volumes of textile waste necessitate the development of more efficient recycling systems, with a primary focus on the optimization of sorting technologies. Near-infrared (NIR) spectroscopy is a state-of-the-art method for fiber identification; however, its accuracy in quantifying textile blends, particularly common polyester/cotton blend textiles, still requires refinement. This study explores the potential and limitations of NIR spectroscopy for quantifying cotton content in post-consumer textiles. A lab-scale NIR sorter and a handheld NIR spectrometer in complementary wavelength ranges were applied to a diverse range of post-consumer textile samples to test model accuracies. Results show that the commonly assumed 10% accuracy threshold in industrial sorting can be exceeded, especially when excluding textiles with <35% cotton content. Identifying and excluding the range of non-linearity significantly improved the model’s performance. The final models achieved an RMSEP of 6.6% and bias of −0.9% for the NIR sorter and an RMSEP of 3.1% and bias of −0.6% for the handheld NIR spectrometer. This study also assessed how textile characteristics—such as color, structure, product type, and alkaline treatment—affect spectral behavior and model accuracy, highlighting their importance for refining quantification when high-purity inputs are needed. By identifying current limitations and potential sources of errors, this study provides a foundation for improving NIR-based models. Full article

Background: The B3-domain transcription factor ABI3 (ABSCISIC ACID INSENSITIVE 3) is a critical regulator of seed maturation, stress adaptation, and hormonal signaling in plants. However, its evolutionary dynamics and functional roles in cotton (Gossypium spp.) remain poorly characterized. Methods: We conducted a comprehensive genome-wide investigation of the ABI3 gene family across 26 plant species, with a focus on 8 Gossypium species. Analyses included phylogenetics, chromosomal localization, synteny assessment, gene duplication patterns, protein domain characterization, promoter cis-regulatory element identification, and tissue-specific/spatiotemporal expression profiling under different organizations of Gossypium hirsutum. Results: Phylogenetic and chromosomal analyses revealed conserved ABI3 evolutionary patterns between monocots and dicots, alongside lineage-specific expansion events within Gossypium spp. Syntenic relationships and duplication analysis in G. hirsutum (upland cotton) indicated retention of ancestral synteny blocks and functional diversification driven predominantly by segmental duplication. Structural characterization confirmed the presence of conserved B3 domains in all G. hirsutum ABI3 homologs. Promoter analysis identified key stress-responsive cis-elements, including ABA-responsive (ABRE), drought-responsive (MYB), and low-temperature-responsive (LTRE) motifs, suggesting a role in abiotic stress regulation. Expression profiling demonstrated significant tissue-specific transcriptional activity across roots, stems, leaves, and fiber developmental stages. Conclusions: This study addresses a significant knowledge gap by elucidating the evolution, structure, and stress-responsive expression profiles of the ABI3 gene family in cotton. It establishes a foundational framework for future functional validation and targeted genetic engineering strategies aimed at developing stress-resilient cotton cultivars with enhanced fiber quality. Full article

The agricultural sector faces significant sustainability, productivity, and environmental impact challenges. In this context, geographic information systems (GISs) have become a key tool to optimize resource management and make informed decisions based on spatial data. These data support planning the best cotton planting and harvest dates based on agroclimatic conditions, such as temperature, precipitation, and soil type, as well as identifying areas with a lower risk of water or thermal stress. As a result, cotton productivity is optimized, and costs associated with supplementary irrigation or losses due to adverse conditions are reduced. However, data from automatic weather stations in Mexico are scarce and incomplete. Instead, grid meteorological databases (DMM, in Spanish) were used with daily temperature and precipitation data from 1983 to 2020 to determine the heat units (HUs) for each cotton crop development stage; daily and accumulated HU; minimum, mean, and maximum temperatures; and mean annual precipitation. This information was used to determine areas that comply with environmental, geographic, and regulatory conditions (NOM-059-SEMARNAT-2010, NOM-026-SAG/FITO-2014) to delimit areas with agricultural potential for planting genetically modified (GM) cotton. The methodology made it possible to produce thirty-four maps at a 1:250,000 scale and a digital GIS with 95% accuracy. These maps indicate whether a given agricultural parcel is optimal for cultivating GM cotton. Full article

Accurate identification of non-food crops underpins food security by clarifying land-use dynamics, promoting sustainable farming, and guiding efficient resource allocation. Proper identification and management maintain the balance between food and non-food cropping, a prerequisite for ecological sustainability and a healthy agricultural economy. Distinguishing large-scale non-food crops—such as oilseed rape, tea, and cotton—remains challenging because their canopy reflectance spectra are similar. This study proposes a novel phenology-aware Vision Transformer Model (PVM) for accurate, large-scale non-food crop classification. PVM incorporates a Phenology-Aware Module (PAM) that fuses multi-source remote-sensing time series with crop-growth calendars. The study area is Hunan Province, China. We collected Sentinel-1 SAR and Sentinel-2 optical imagery (2021–2022) and corresponding ground-truth samples of non-food crops. The model uses a Vision Transformer (ViT) backbone integrated with PAM. PAM dynamically adjusts temporal attention using encoded phenological cues, enabling the network to focus on key growth stages. A parallel Multi-Task Attention Fusion (MTAF) mechanism adaptively combines Sentinel-1 and Sentinel-2 time-series data. The fusion exploits sensor complementarity and mitigates cloud-induced data gaps. The fused spatiotemporal features feed a Transformer-based decoder that performs multi-class semantic segmentation. On the Hunan dataset, PVM achieved an F1-score of 74.84% and an IoU of 61.38%, outperforming MTAF-TST and 2D-U-Net + CLSTM baselines. Cross-regional validation on the Canadian Cropland Dataset confirmed the model’s generalizability, with an F1-score of 71.93% and an IoU of 55.94%. Ablation experiments verified the contribution of each module. Adding PAM raised IoU by 8.3%, whereas including MTAF improved recall by 8.91%. Overall, PVM effectively integrates phenological knowledge with multi-source imagery, delivering accurate and scalable non-food crop classification. Full article

Background: Hospitalized COVID-19 patients are particularly vulnerable to secondary bacterial infections, which can significantly worsen clinical outcomes. The aim of the study was to identify the cause of bacteremia in a group of hospitalized COVID-19 patients and find out the source of the outbreak to prevent further spread. Methods: Pathogen identification in blood cultures and sensitivity testing were carried out using the automated VITEK2 system. A total of 110 samples were tested; these were collected from patients’ colonization sites and from surfaces, materials and fluids used in the setting. Furthermore, multilocus sequence typing (MLST) and next-generation sequencing (NGS) were employed to characterize the isolates. Results: Achromobacter xylosoxidans was detected in the blood of nine hospitalized patients and in cotton used for disinfection; all isolates presented an identical antibiotic resistance pattern, and all carried the bla_OXA-114 gene which is intrinsic to this species. Infection control measures were implemented promptly. With one exception, all patients recovered and were discharged in good health. Conclusions: This outbreak underscores the urgent need for investigation and control of hospital infections, as bacteremia is associated with increased morbidity, mortality, hospitalization time, and cost. It also highlights the importance of close collaboration among healthcare professionals. Full article

Understanding the influence of environmental factors on cotton performance is crucial for enhancing yield and fiber quality in the context of climate change. This study investigates genotype-by-environment (G×E) interactions in cotton, using data from 250 recombinant inbred lines (CCRI70 RILs) cultivated across 14 diverse environments in China’s major cotton cultivation areas. Our findings reveal that environmental effects predominantly influenced yield-related traits (boll weight, lint percentage, and the seed index), contributing to 34.7% to 55.7% of their variance. In contrast fiber quality traits showed lower environmental sensitivity (12.3–27.0%), with notable phenotypic plasticity observed in the boll weight, lint percentage, and fiber micronaire. Employing six machine learning models, Random Forest demonstrated superior predictive ability (R² = 0.40–0.72; predictive Pearson correlation = 0.63–0.86). Through SHAP-based interpretation and sliding-window regression, we identified key environmental drivers primarily active during mid-to-late growth stages. This approach effectively reduced the number of influential input variables to just 0.1–2.4% of the original dataset, spanning 2–9 critical time windows per trait. Incorporating these identified drivers significantly improved cross-environment predictions, enhancing Random Forest accuracy by 0.02–0.15. These results underscore the strong potential of machine learning to uncover critical temporal environmental factors underlying G×E interactions and to substantially improve predictive modeling in cotton breeding programs, ultimately contributing to more resilient and productive cotton cultivation. Full article

Textile recycling approaches require input material streams of defined purity. Establishing sorting facilities and defining viable sorting fractions for efficient subsequent recycling necessitates knowledge on the composition and material content of the textiles to be processed. Subsequently, this information is crucial for the implementation of a sustainable circular economy for textiles. This study presents the results of a comprehensive waste textile sampling and characterisation along with data on the quantities and composition of waste textiles in Vienna in 2022. The data reveals that only 28% of the 19,975 t of waste textiles generated end up in separate collection, of which a significant amount goes to the international market. However, the results regarding the fibre composition show that textiles from mixed municipal solid waste and separate collection are very similar. Cotton fibres accounted for approx. half of the fibre mass from non-complex textiles, with 9328 t overall (6776 t in the mixed municipal solid waste and 2522 t in separate collection). A further analysis regarding fibre blends found that a total of 6275 t of single-fibre materials and 5132 t of two-fibre materials were present. This reveals great potential for using this waste stream in fibre-to-fibre recycling processes. Collecting accurate data on this waste stream enables sorters and recyclers to tailor their processes to the expected input material. By increasing the amount of recycled materials, the share of incinerated or landfilled textiles will decrease, which in turn will have a positive impact on the environment. However, further research in textile identification and material separation as well as regulations to keep these materials in a sustainable closed loop are required. Full article

The increasing prevalence of microplastic (MP) pollution and the labor-intensive nature of existing identification methods necessitate improved large-scale detection approaches. Nile Red (NR) fluorescence, which varies with polarity, offers a potential classification method, but standardization of carrier solvents and fluorescence differentiation techniques remains lacking. This study evaluated eight NR-carrier solvents (n-hexane, chloroform, acetone, methanol, ethanol, acetone/hexane, acetone/ethanol, and acetone/water) across ten common MP polymers (HDPE, LDPE, PP, EPS, PS, PC, ABS, PVC, PET, and PA). Fluorescence intensity, Stokes shift, and solvent-induced polymer degradation were analyzed. The study also assessed HSV (Hue/Saturation/Value) color spaces for Stokes shift representation and MP differentiation. Fenton oxidation effectively quenched fluorescence in natural organic matter (e.g., eggshells, fingernails, wood, cotton) while preserving NR-stained MPs. Acetone/water [25% (v/v)] emerged as the optimal solvent, balancing fluorescence performance and minimal degradation. Full article

(1) Background: This study aims to analyze the defoliation and boll opening performance of 123 upland cotton germplasm resources after spraying defoliant, using multispectral data to select relevant vegetation indices and identify germplasm resources sensitive to defoliants, providing methods for cotton variety improvement and high-quality parental resources. (2) Methods: 123 historical upland cotton germplasm resources from Xinjiang were selected, and the defoliation and boll opening of cotton leaves were investigated at 0, 4, 8, 12, 16, and 20 days after defoliant application. Simultaneously, multispectral digital images were collected using drones to obtain 12 vegetation indices. Based on defoliation rate, the optimal vegetation index was selected, and defoliant-sensitive germplasm resources were identified. (3) Results: The most significant difference in defoliation rate of cotton germplasm resources occurred 16 days after application. Cluster analysis grouped the 123 breeding materials into three categories, with Class I showing the best defoliation effect. Among the 12 vegetation indices, the Plant Senescence Reflectance Index (PSRI) has the highest correlation coefficient with the defoliation rate; and when the PSRI value is higher, the defoliation effect of the material is better. By comparing the traditional investigation method with the unmanned aerial vehicle multispectral technology, 15 cotton materials sensitive to defoliants were determined, with a defoliation rate of over 85%, a lint percentage ranging from 76.67% to 98.04%, and a PSRI value ranging from 0.1607 to 0.1984. (4) Conclusions: The study found that the vegetation index with sensitive response can be used as an effective indicator to evaluate the sensitivity of cotton breeding materials to defoliants. Using an unmanned aerial vehicle (UAV) equipped with vegetation indices for screening shows a high consistency with the manual investigation and screening method in screening excellent defoliation materials; it proves that it is feasible to screen cotton breeding materials with excellent defoliation effects using UAV multispectral technology. Full article

Arabinogalactan proteins (AGPs) constitute a diverse class of hydroxyproline-rich glycoproteins implicated in various aspects of plant growth and development. However, their functional characterization in cotton (Gossypium spp.) remains limited. As a globally significant economic crop, cotton serves as the primary source of natural fiber, making it essential to understand the genetic mechanisms underlying its growth and development. This study aims to perform a comprehensive genome-wide identification and characterization of the AGP gene family in Gossypium spp., with a particular focus on elucidating their structural features, evolutionary relationships, and functional roles. A genome-wide analysis was conducted to identify AGP genes in Gossypium spp., followed by classification into distinct subfamilies based on sequence characteristics. Protein motif composition, gene structure, and phylogenetic relationships were examined to infer potential functional diversification. Subcellular localization of a key candidate gene, GhAGP50, was determined using fluorescent protein tagging, while gene expression patterns were assessed through β-glucuronidase (GUS) reporter assays. Additionally, hormonal regulation of GhAGP50 was investigated via treatments with methyl jasmonate (MeJA), abscisic acid (ABA), indole-3-acetic acid (IAA), and gibberellin (GA). A total of 220 AGP genes were identified in Gossypium spp., comprising 19 classical AGPs, 28 lysine-rich AGPs, 55 AG peptides, and 118 fasciclin-like AGPs (FLAs). Structural and functional analyses revealed significant variation in gene organization and conserved motifs across subfamilies. Functional characterization of GhAGP50, an ortholog of AGP18 in Arabidopsis thaliana, demonstrated its role in promoting epidermal hair formation in leaves and stalks. Subcellular localization studies indicated that GhAGP50 is targeted to the nucleus and plasma membrane. GUS staining assays revealed broad expression across multiple tissues, including leaves, inflorescences, roots, and stems. Furthermore, hormonal treatment experiments showed that GhAGP50 expression is modulated by MeJA, ABA, IAA, and GA, suggesting its involvement in hormone-mediated developmental processes. This study presents a comprehensive genome-wide analysis of the AGP gene family in cotton, providing new insights into their structural diversity and functional significance. The identification and characterization of GhAGP50 highlight its potential role in epidermal hair formation and hormonal regulation, contributing to a deeper understanding of AGP functions in cotton development. These findings offer a valuable genetic resource for future research aimed at improving cotton growth and fiber quality through targeted genetic manipulation. Full article

Cotton is a crucial cash crop widely valued for its fiber. It is an important source of natural fiber and has diverse applications. Improving fiber quality is of significant economic and agricultural importance. Purple acid phosphatases (PAPs) are multifunctional enzymes critical for plant cell wall biosynthesis, root architecture modulation, low-phosphorus stress adaptation, and salt/ROS stress tolerance. In this study, a comprehensive genome-wide analysis of the PAP gene family was performed for four cotton species (G. hirsutum, G. barbadense, G. raimondii, and G. arboreum) to explore its potential role in improving fiber quality. A total of 193 PAP genes were identified in these species, revealing several conserved domains that contribute to their functional diversity. Phylogenetic analysis showed that the cotton PAP2 genes exhibited high homology with NtPAP12, a cell wall synthesis-related gene. Using cotton varieties with contrasting fiber thickness (EZ60, micronaire 4.5 vs. CCRI127, micronaire 3.5), qRT-PCR analysis demonstrated significantly higher expression levels of GhPAP2.2, GhPAP2.6, GhPAP2.8, and GhPAP2.9 in EZ60 fibers during 20–25 DPA compared to CCRI127. These results highlight the potential influence of PAP genes on cotton fiber development and provide valuable insights for improving fiber quality in cotton breeding. Full article

The positioning of the top bud by the topping machine in the cotton topping operation depends on the recognition algorithm. The detection results of the traditional target detection algorithm contain a lot of useless information, which is not conducive to the positioning of the top bud. In order to obtain a more efficient recognition algorithm, we propose a top bud segmentation algorithm CBLN-YOLO based on the YOLO11n-seg model. Firstly, the standard convolution and multihead self-attention (MHSA) mechanisms in YOLO11n-seg are replaced by linear deformable convolution (LDConv) and coordinate attention (CA) mechanisms to reduce the parameter growth rate of the original model and better mine detailed features of the top buds. In the neck, the feature pyramid network (FPN) is reconstructed using an enhanced interlayer feature correlation (EFC) module, and regression loss is calculated using the Inner CIoU loss function. When tested on a self-built dataset, the mAP@0.5 values of CBLN-YOLO for detection and segmentation are 98.3% and 95.8%, respectively, which are higher than traditional segmentation models. At the same time, CBLN-YOLO also shows strong robustness under different weather and time periods, and its recognition speed reaches 135 frames per second, which provides strong support for cotton top bud positioning in the field environment. Full article

Accurate detection of textile composition is a major challenge for textile reuse/recycling. This paper investigates the feasibility of identification of textile materials using a Near Infra-Red (NIR) camera. A transportable metric has been defined which could be capable of identification and distinction between cotton and polyester. The NIR camera provides a single data value in the form of the “intensity” of the exposed light at each pixel across its 2D pixel array. The feasibility of textile material identification was investigated using a combination of various statistical methods to evaluate the output images from the NIR camera when a bandpass filter was attached to the camera’s lens. A repeatable and stable metric was identified and was shown to be independent of both the camera’s exposure setting and the physical illumination spread over the textiles. The average value of the identified metric for the most suitable bandpass filter was found to be 0.68 for cotton, with a maximum deviation of 2%, and 1.0 for polyester, with a maximum deviation of 1%. It was further shown that carbon black dye, a known challenge in the industry, was easily detectable by the system, and, using the proposed technique in this paper, areas that are not covered by carbon black dye can be identified and analysed. Full article

Cotton aphids are the primary pests that adversely affect cotton growth, and they also transmit a variety of viral diseases, seriously threatening cotton yield and quality. Although the traditional remote sensing method with a single data source improves the monitoring efficiency to a certain extent, it has limitations with regard to reflecting the complex distribution characteristics of aphid pests and accurate identification. Accordingly, there is a pressing need for efficient and high-precision UAV remote sensing technology for effective identification and localization. To address the above problems, this study began by presenting a fusion of two kinds of images, namely panchromatic and multispectral images, using Gram–Schmidt image fusion technique to extract multiple vegetation indices and analyze their correlation with aphid damage indices. After fusing the panchromatic and multispectral images, the correlation between vegetation indices and aphid infestation degree was significantly improved, which could more accurately reflect the spatial distribution characteristics of aphid infestation. Subsequently, these machine learning techniques were applied for modeling and evaluation of the performance of multispectral and fused image data. The results of the validation revealed that the GBDT (Gradient-Boosting Decision Tree) model for GLI, RVI, DVI, and SAVI vegetation indices based on the fused data performed the best, with an estimation accuracy of R² of 0.88 and an RMSE of 0.0918, which was obviously better than that of the other five models, and that the monitoring method of combining fusion of panchromatic and multispectral imagery with the accuracy and efficiency of the GBDT model were noticeably higher than those of single multispectral imaging. The fused panchromatic and multispectral images combined with the GBDT model significantly outperformed the single multispectral image in terms of precision and efficiency. In conclusion, this study demonstrated the effectiveness of image fusion combined with GBDT modeling in cotton aphid pest monitoring. Full article

Intensive and large-scale drip irrigation plays a crucial role in ensuring cotton production in Northwest China. However, significant differences in cotton production have occurred at times within large-scale irrigation systems, and quantitative information on the importance and interactions of factors related to cotton growth and constraints is scarce. In 2018–2019, we monitored six possible constraints (irrigation depth, soil texture, soil salt, soil moisture, soil inorganic nitrogen and soil organic matter) associated with drip irrigation management and seed cotton yields in a collective drip irrigation system (CDIS, composed of several drip irrigation subsystems (DISs)) in southern Xinjiang to assess the importance of different factors and identify the main constraints. In 2023, other more refined field trials were conducted to further evaluate the influencing mechanism of the main constraints on crop growth in one typical DIS within the selected CDIS. The results revealed large yield differences within the CDIS; although the average seed cotton yield was good (2018: 8051 kg ha⁻¹, 2019: 6617 kg ha⁻¹). Excessive irrigation depths (>500 mm) and coarse soil texture (sand content > 70%) were identified as the main constraints, affecting more than 45% of the plant area in the CDIS based on boundary line analysis (a typical analysis method to study the responses between variables) The results from the DISs revealed that the two constraints directly affected the soil moisture and soil inorganic nitrogen in the root zone, which reduced the effectiveness of irrigation and fertilization under drip irrigation. The Structural Equation Model (used to evaluate the causal relationships among multiple variables) revealed that both irrigation depth and soil texture indirectly affect yield by affecting soil inorganic nitrogen and plant N uptake and that soil nitrogen management is critical in resisting yield decline caused by constraints. An optimized irrigation schedule, improved uniformity of the drip irrigation network and adjusted drip fertilization strategies could be used for site-specific management to address the yield decline due to the main constraints and improve water and fertilizer use efficiency under drip irrigation management. Full article