Search Results (322)

To address the limitations of the existing cotton seedling growth prediction methods in field environments, specifically, poor representation of spatiotemporal features and low visual fidelity in texture rendering, this paper proposes an algorithm for the prediction of cotton seedling growth from images based on FCA-STNet. The model leverages historical sequences of cotton seedling RGB images to generate an image of the predicted growth at time t + 1 and extracts 37 phenotypic traits from the predicted image. A novel STNet structure is designed to enhance the representation of spatiotemporal dependencies, while an Adaptive Fine-Grained Channel Attention (FCA) module is integrated to capture both global and local feature information. This attention mechanism focuses on individual cotton plants and their textural characteristics, effectively reducing the interference from common field-related challenges such as insufficient lighting, leaf fluttering, and wind disturbances. The experimental results demonstrate that the predicted images achieved an MSE of 0.0086, MAE of 0.0321, SSIM of 0.8339, and PSNR of 20.7011 on the test set, representing improvements of 2.27%, 0.31%, 4.73%, and 11.20%, respectively, over the baseline STNet. The method outperforms several mainstream spatiotemporal prediction models. Furthermore, the majority of the predicted phenotypic traits exhibited correlations with actual measurements with coefficients above 0.8, indicating high prediction accuracy. The proposed FCA-STNet model enables visually realistic prediction of cotton seedling growth in open-field conditions, offering a new perspective for research in growth prediction. Full article

This study presents an advanced framework for fabric texture classification by leveraging macro- and micro-texture extraction techniques integrated with deep learning architectures. Co-occurrence histograms, local binary patterns (LBPs), and albedo-dependent feature maps were employed to comprehensively capture the surface properties of fabrics. A late fusion approach was applied using four state-of-the-art convolutional neural networks (CNNs): InceptionV3, ResNet50_V2, DenseNet, and VGG-19. Excellent results were obtained, with the ResNet50_V2 achieving a precision of 0.929, recall of 0.914, and F1 score of 0.913. Notably, the integration of multimodal inputs allowed the models to effectively distinguish challenging fabric types, such as cotton–polyester and satin–silk pairs, which exhibit overlapping texture characteristics. This research not only enhances the accuracy of textile classification but also provides a robust methodology for material analysis, with significant implications for industrial applications in fashion, quality control, and robotics. Full article

In response to the limited research on fire detection in cotton pickers and the issue of low detection accuracy in visual inspection, this paper proposes a computer vision-based detection method. The method is optimized according to the structural characteristics of cotton pickers, and a lightweight improved YOLOv11 algorithm is designed for cotton fire detection in cotton pickers. The backbone of the model is replaced with the MobileNetV2 network to achieve effective model lightweighting. In addition, the convolutional layers in the original C3k2 block are optimized using partial convolutions to reduce computational redundancy and improve inference efficiency. Furthermore, a visual attention mechanism named CBAM-ECA (Convolutional Block Attention Module-Efficient Channel Attention) is designed to suit the complex working conditions of cotton pickers. This mechanism aims to enhance the model’s feature extraction capability under challenging environmental conditions, thereby improving overall detection accuracy. To further improve localization performance and accelerate convergence, the loss function is also modified. These improvements enable the model to achieve higher precision in fire detection while ensuring fast and accurate localization. Experimental results demonstrate that the improved model reduces the number of parameters by 38%, increases the frame processing speed (FPS) by 13.2%, and decreases the computational complexity (GFLOPs) by 42.8%, compared to the original model. The detection accuracy for flaming combustion, smoldering combustion, and overall detection is improved by 1.4%, 3%, and 1.9%, respectively, with an increase of 2.4% in mAP (mean average precision). Compared to other models—YOLOv3-tiny, YOLOv5, YOLOv8, and YOLOv10—the proposed method achieves higher detection accuracy by 5.9%, 7%, 5.9%, and 5.3%, respectively, and shows improvements in mAP by 5.4%, 5%, 4.8%, and 6.3%. The improved detection algorithm maintains high accuracy while achieving faster inference speed and fewer model parameters. These improvements lay a solid foundation for fire prevention and suppression in cotton collection boxes on cotton pickers. Full article

In order to meet the needs for the rapid detection of crop growth and support variable management in farmland, an intelligent chlorophyll content in cotton leaves (CCC) detection system based on hyperspectral imaging (HSI) technology was designed and developed. The system includes a near-infrared (NIR) hyperspectral image acquisition module, a spectral extraction module, a main control processor module, a model acceleration module, a display module, and a power module, which are used to achieve rapid and non-destructive detection of chlorophyll content. Firstly, spectral images of cotton canopy leaves during the seedling, budding, and flowering-boll stages were collected, and the dataset was optimized using the first-order differential algorithm (1D) and Savitzky–Golay five-term quadratic smoothing (SG) algorithm. The results showed that SG had better processing performance. Secondly, the sparrow search algorithm optimized backpropagation neural network (SSA-BPNN) and one-dimensional convolutional neural network (1DCNN) algorithms were selected to establish a chlorophyll content detection model. The results showed that the determination coefficients $R_p^2$ of the chlorophyll SG-1DCNN detection model during the seedling, budding, and flowering-boll stages were 0.92, 0.97, and 0.95, respectively, and the model performance was superior to SG-SSA-BPNN. Therefore, the SG-1DCNN model was embedded into the detection system. Finally, a CCC intelligent detection system was developed using Python 3.12.3, MATLAB 2020b, and ENVI, and the system was subjected to application testing. The results showed that the average detection accuracy of the CCC intelligent detection system in the three stages was 98.522%, 99.132%, and 97.449%, respectively. Meanwhile, the average detection time for the samples is only 20.12 s. The research results can effectively solve the problem of detecting the nutritional status of cotton in the field environment, meet the real-time detection needs of the field environment, and provide solutions and technical support for the intelligent perception of crop production. Full article

Infertility is a significant global health concern, and incorporating antioxidants into sperm preparation media is one strategy to enhance sperm quality and decrease infertility rates. This study aimed to investigate the phytochemical compounds of red cotton stamen extracts and their effects as antioxidants in improving the quality of bull frozen semen. Among the extracts, RCU contained the highest levels of total phenolics, total tannins, and total monomeric anthocyanins along with the strongest ABTS free radical scavenging activity and protein denaturation inhibition. Exposing sperm to FeSO₄-induced oxidative stress resulted in significantly reduced motility, viability, and normal morphology. However, treatment with RCD, RCU, and RCM improved these parameters. Additionally, the FeSO₄-induced group showed elevated levels of reactive oxygen species (ROS) and advanced glycation end products (AGEs) compared to the normal control, whereas all red cotton stamen extracts effectively reduced these levels. In conclusion, red cotton stamen extracts, rich in phenolic bioactive compounds, demonstrated strong free radical scavenging capacity and improved sperm motility, viability, and morphology by neutralizing free radicals and enhancing antioxidant defenses. These findings suggest that the red cotton stamen extracts, particularly RCD and RCU, offer benefits for sperm preservation. Full article

Cotton leaf diseases can lead to substantial yield losses and economic burdens. Traditional detection methods are challenged by low accuracy and high labor costs. This research presents the ACURS-YOLO network, an advanced cotton leaf disease detection architecture developed on the foundation of YOLOv11. By integrating a medical image segmentation model, it effectively tackles challenges including complex background interference, the missed detection of small targets, and restricted generalization ability. Specifically, the U-Net v2 module is embedded in the backbone network to boost the multi-scale feature extraction performance in YOLOv11. Meanwhile, the CBAM attention mechanism is integrated to emphasize critical disease-related features. To lower the computational complexity, the SPPF module is substituted with SimSPPF. The C3k2_RCM module is appended for long–range context modeling, and the ARelu activation function is employed to alleviate the vanishing gradient problem. A database comprising 3000 images covering six types of cotton leaf diseases was constructed, and data augmentation techniques were applied. The experimental results show that ACURS-YOLO attains impressive performance indicators, encompassing a mAP_0.5 value of 94.6%, a mAP_0.5:0.95 value of 83.4%, 95.5% accuracy, 89.3% recall, an F1 score of 92.3%, and a frame rate of 148 frames per second. It outperforms YOLOv11 and other conventional models with regard to both detection precision and overall functionality. Ablation tests additionally validate the efficacy of each component, affirming the framework’s advantage in addressing complex detection environments. This framework provides an efficient solution for the automated monitoring of cotton leaf diseases, advancing the development of smart sensors through improved detection accuracy and practical applicability. Full article

The environmental impact of petroleum-based plastics has driven a global shift toward sustainable alternatives like biodegradable polymers, including polylactic acid (PLA), polybutylene succinate (PBS), and polycaprolactone (PCL). Yet, these bioplastics often face limitations in mechanical and thermal properties, hindering broader use. Reinforcement with cellulose nanofibrils (CNFs) has shown promise, yet most research focuses on conventional sources like wood pulp and cotton, neglecting agricultural residues. This review addresses the potential of maize husk, a lignocellulosic waste abundant in South Africa, as a source of CNFs. It evaluates the literature on the structure, extraction, characterisation, and integration of maize husk-derived CNFs into biodegradable polymers. The review examines the chemical composition, extraction methods, and key physicochemical properties that affect performance when blended with PLA, PBS, or PCL. However, high lignin content and heterogeneity pose extraction and dispersion challenges. Optimised maize husk CNFs can enhance the mechanical strength, barrier properties, and thermal resistance of biopolymer systems. This review highlights potential applications in packaging, biomedical, and agricultural sectors, aligning with South African bioeconomic goals. It concludes by identifying research priorities for improving compatibility and processing at an industrial scale, paving the way for maize husk CNFs as effective, locally sourced reinforcements in green material innovation. Full article

Accurate detection of cotton pests and diseases is essential for agricultural productivity yet remains challenging due to complex field environments, the small size of pests and diseases, and significant occlusions. To address the challenges presented by these factors, a novel cotton disease and pest detection method is proposed. This method builds upon the YOLOv8 baseline model and incorporates a Multi-Scale Sliding Window Attention Module (MSFE) within the backbone architecture to enhance feature extraction capabilities specifically for small targets. Furthermore, a Depth-Separable Dilated Convolution Module (C2f-DWR) is designed to replace the existing C2f module in the neck of the network. By employing varying dilation rates, this modification effectively expands the receptive field and alleviates the loss of detailed information associated with the downsampling processes. In addition, a Multi-Head Attention Detection Head (MultiSEAMDetect) is introduced to supplant the original detection head. This new head utilizes diverse patch sizes alongside adaptive average pooling mechanisms, thereby enabling the model to adjust its responses in accordance with varying contextual scenarios, which significantly enhances its ability to manage occlusion during detection. For the purpose of experimental validation, a dedicated dataset for cotton disease and pest detection was developed. In this dataset, the improved model’s mAP50 and mAP50:95 increased from 73.4% and 46.2% to 77.2% and 48.6%, respectively, compared to the original YOLOv8 algorithm. Validation on two Kaggle datasets showed that mAP50 rose from 92.1% and 97.6% to 93.2% and 97.9%, respectively. Meanwhile, mAP50:95 improved from 86% and 92.5% to 87.1% and 93.5%. These findings provide compelling evidence of the superiority of the proposed algorithm. Compared to other advanced mainstream algorithms, it exhibits higher accuracy and recall, indicating that the improved algorithm performs better in the task of cotton pest and disease detection. Full article

Objective: This study aimed to evaluate the effects of local use of Brazilian Green Propolis (BGP), either as an ethanolic extract (the most common formulation) or incorporated into lipid-based nanostructures, as an adjuvant therapy for non-surgical periodontal treatment in managing experimental periodontitis (EP) in ovariectomized rats. Methods: Fifty-six female Wistar rats underwent bilateral ovariectomies. After 10 weeks, a cotton ligature was placed around the lower first molar and remained in place for two weeks to induce EP. The ligature was removed, and the rats were randomly assigned in the groups NLT (n = 14), SRP (n = 14), SRP-BGPee (n = 14), and SRP-BGPlns (n = 14). In the NLT group, no local treatment was performed. The SRP group received scaling and root planing (SRP), along with irrigation using a physiological saline solution. The SRP-BGPee group underwent SRP and irrigation with ethanolic extract of BGP. The SRP-BGPlns group underwent SRP and irrigation with BGP-loaded lipid nanostructure (BGPlns). Each group received one SRP session followed by four irrigation sessions with the specified solutions, which were conducted immediately after SRP and subsequently after 2, 4, and 6 days. Euthanasia was performed at 7 and 28 days following the removal of the ligatures. The hemimandibles were processed for the following analyses: microtomographic analysis; histological analysis; histometric analysis of the percentage of bone tissue in the furcation region (PBT); and immunohistochemical analysis for tartrate-resistant acid phosphatase activity (TRAP), transforming growth factor beta 1 (TGFβ1), and osteocalcin (OCN). Results: The SRP-BGPlns group demonstrated superior periodontal tissue repair, reduced alveolar bone loss, fewer TRAP-positive cells (at 7 days), and higher levels of immunolabeling for TGFβ1 (at both 7 and 28 days) and OCN (at 28 days) compared to the other experimental groups. Conclusions: The irrigation with BGP is an effective adjuvant therapy for non-surgical periodontal treatment in managing EP in ovariectomized rats. Its application in lipid-based nanostructures proved to be more effective than the ethanolic extract form. Full article

Background: This study presents an innovative approach to developing bioactive natural fabrics for healthcare and medical applications. Methods: An ethanol extract of Origanum vulgare L. (in further text: OE), exhibiting exceptional antioxidant (100%) and antibacterial activity (>99% against E.coli and S.aureus), was employed to biofunctionalize cotton, wool, and silk fabrics. Results: All biofunctionalized fabrics demonstrated strong antioxidant activity (>99%), while antibacterial efficacy varied by fabric: cotton > 54%, wool > 99%, and silk > 89%. OE-biofunctionalized wool possessed the highest release of OE’s bioactive compounds, followed by silk and cotton, indicating substrate-dependent release behavior. This tunable fabrics’ OE release profile, along with their unique bioactivity, supports targeted applications: OE-functionalized silk for luxury or prolonged therapeutic use (skin-care textiles, post-surgical dressings, anti-aging products), cotton for disposable or short-term use (protective wipes, minor wound coverings), and wool for wound dressings. The biocompatibility and cytotoxicity of OE-biofunctionalized wool were evaluated via in vitro assays using healthy human keratinocytes and in vivo testing in Wistar albino male rats. The obtained results revealed that OE-functionalized wool significantly accelerated wound closure (97.8% by day 14), enhanced collagen synthesis (6.92 µg/mg hydroxyproline), and improved tissue and systemic antioxidant defense while reducing oxidative stress markers in skin and blood samples of rats treated with OE-biofunctionalized wool. Conclusions: OE-biofunctionalized wool demonstrates strong potential as an advanced natural solution for managing chronic wounds. Further clinical validation is recommended to confirm its performance in real-world healthcare settings. This work introduces an entirely new application of OE in textile biofunctionalization, offering alternatives for healthcare and medical textiles. Full article

Cotton is one of the most valuable non-food agricultural products in the world. However, cotton production is often hampered by the invasion of disease. In most cases, these plant diseases are a result of insect or pest infestations, which can have a significant impact on production if not addressed promptly. It is, therefore, crucial to accurately identify leaf diseases in cotton plants to prevent any negative effects on yield. This paper presents a hybrid deep learning approach based on Bidirectional Encoder Representations from Transformers with Residual network and particle swarm optimization (BERT-ResNet-PSO) for detecting cotton plant diseases. This approach starts with image pre-processing, which they pass to a BERT-like encoder after linearly embedding the image patches. It results in segregating disease regions. Then, the output of the encoded feature is passed to ResNet-based architecture for feature extraction and further optimized by PSO to increase the classification accuracy. The approach is tested on a cotton dataset from the Plant Village dataset, where the experimental results show the effectiveness of this hybrid deep learning approach, achieving an accuracy of 98.5%, precision of 98.2% and recall of 98.7% compared to the existing deep learning approaches such as ResNet50, VGG19, InceptionV3, and ResNet152V2. This study shows that the hybrid deep learning approach is capable of dealing with the cotton plant disease detection problem effectively. This study suggests that the proposed approach is beneficial to help avoid crop losses on a large scale and support effective farming management practices. Full article

Fluridone is a pyrrolidone soil-sealing herbicide that has been widely used in cotton fields in Xinjiang in recent years. The purpose of this study was to establish a method for determining fluridone residues in cotton fields and to perform residue digestion tests, final residue analysis, and dietary risk assessment. Samples were extracted with acetonitrile, purified with primary secondary amine (PSA) and multi-walled carbon nanotubes (MWCNTs), and analyzed by high-performance liquid chromatography (HPLC). The results showed that in a certain concentration range, the concentration and peak area of fluridone showed a good linear relationship (R² > 0.99), with limit of detection (LOD) and limit of quantification (LOQ) values of 0.00090–0.00108 mg·kg⁻¹ and 0.0030–0.0033 mg·kg⁻¹, respectively. The relative standard deviation (RSD) values of fluridone were 0.46% to 4.57% at the spiked level of 0.1, 0.5, and 1.0 mg·kg⁻¹, respectively. The average daily recovery rate of fluridone was 85.08% to 95.07%. The residual levels of fluridone in cottonseed oil were below the safety threshold, indicating no significant dietary risk to consumers. Full article

Thread-based analytical devices are low-cost, portable, and easy to use, making them ideal for detecting various biomolecules like glucose and DNA with minimal sample requirements, while also offering environmental benefits through their biodegradability. This study explores the potential of zwitterionic poly(sulfobetaine methacrylate) brushes modified cotton thread (PSBMA@threads) as an innovative substitute for DNA solid-phase extraction. The PSBMA polymer brushes were synthesized on cotton threads via surface-initiated atom transfer radical polymerization (SI-ATRP). The usability of the PSBMA@threads for DNA extraction from cell lysates containing cell debris, proteins, and detergents was evaluated. Characterization using SEM, FTIR, and EDS confirmed the successful functionalization with PSBMA polymer brushes. The antifouling properties of PSBMA@threads, including resistance to non-specific protein adsorption and underwater oil repellency, were assessed. The results demonstrated selective DNA capture from protein and lipid-rich lysates. Optimized extraction parameters improved DNA yield, enabling efficient extraction from tumor cells, which successfully underwent PCR amplification. Comparative experiments with commercial silica membrane-based columns revealed that PSBMA@threads exhibited comparable DNA extraction capability. The PSBMA@threads maintained extraction capability after six months of ambient storage, highlighting its stability and cost-effectiveness for nucleic acid isolation in analytical applications. Full article

Classical and modern methods are used to release curcumin by degrading the polysaccharides found in the turmeric powder matrix. Classical methods use chemicals as acids (HCl, H₂SO₄, CH₃COOH), oxidants (H₂O₂, kojic acid), and enzymes (amylase type) that can degrade amylose and amylopectin from starch. The modern applied methods consist of the degradation of the polysaccharides in the turmeric powder during eco-friendly processes assisted by ultrasound or microwaves. The extraction medium can consist of only water, water with a solvent, and/or an oxidizing agent. The presence of curcumin in turmeric powder is confirmed by FTIR analysis. The UV–VIS analysis of the extracts allows the determination of the efficiency of modern extraction processes. The release of curcumin from turmeric is highlighted quantitatively by colorimetric measurements for the obtained extracts, using a portable DataColor spectrophotometer. The comparison of the results leads to the conclusion that microwave-assisted extractions are the most effective. These extracts are able to dye many types of textile fibers: wool, cotton, hemp, silk, polyacrylonitrile, polyamide, polyester, and cellulose acetate. CIELab and color strength (K/S) measurements indicate that the most intense yellow colors are obtained on polyacrylonitrile (b* = 86.32, K/S = 15.14) and on cellulose acetate (b* = 90.40, K/S = 14.17). Full article

Irrigation areas in arid regions are vital production areas for grain and cash crops worldwide. Grasping the temporal and spatial evolution of planting configurations across several years is crucial for effective regional agricultural and resource management. In view of problems such as insufficient optical images caused by cloudy weather in arid regions and the unclear spatiotemporal evolution patterns of the planting structures in irrigation areas over the years, in this study, we took the Santun River Irrigation Area, a typical arid region in Xinjiang, China, as an example. By leveraging long time-series remote sensing images from Sentinel-1 and Sentinel-2, the spectral, index, texture, and polarization features of the ground objects in the study area were extracted. When analyzing the index characteristics, we considered several widely used global vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and Global Environment Monitoring Index (GEMI). Additionally, we integrated the vertical–vertical and vertical–horizontal polarization data obtained from synthetic aperture radar (SAR) satellite systems. Machine learning algorithms, including the random forest algorithm (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM), were employed for planting structure classification. The optimal classification model selected was subjected to inter-annual transfer to obtain the planting structures over multiple years. The research findings are as follows: (1) The RF classification algorithm outperforms CART and SVM algorithms in terms of classification accuracy, achieving an overall accuracy (OA) of 0.84 and a kappa coefficient of 0.805. (2) The cropland area classified by the RF algorithm exhibited a high degree of consistency with statistical yearbook data (R² = 0.82–0.91). Significant differences are observed in the estimated planting areas of cotton, maize, tomatoes, and wheat, while differences in other crops are not statistically significant. (3) From 2019 to 2024, cotton remained the dominant crop, although its proportional area fluctuated considerably, while the areas of maize and wheat tended to remain stable, and those of tomato and melon showed relatively minor changes. Overall, the region demonstrates a cotton-dominated, stable cropping structure for other crops. The newly developed framework exhibits exceptional precision in categorization while maintaining impressive adaptability, offering crucial insights for optimizing agricultural operations and sustainable resource allocation in irrigation-dependent arid zones. Full article