The fabric quality in textile industry is characterized by the texture (weave pattern) as it plays a vital role for the production and design of best quality fabric. The earlier proposed automated weave identification methods based on image processing techniques are highly dependent on the lighting conditions. The machine learning methods have been reported to show better accuracy. However, they require very large training datasets, very high processing power and computation time. This study proposes improved accuracy with smaller dataset and reduced computation time by proposing a modification of VGG16 model by adding two additional pooling layers. Using evaluation metrics of both models, the modified model results were analysed according to accuracy, balanced accuracy, and F1-score. On the basis of investigational outcomes, a comparison has been performed with earlier work. The results show that the proposed VGG-16 model is capable to achieve state-of-the-art accuracy and avoid unnecessary activation features by freezing the main convolutional base layers. Ultimately, as evidenced by the performance of the modified VGG-16 deep learning model, the proposed method demonstrated improved accuracy. The study results show that the proposed modified VGG16 algorithm is able to recognize the features of provided database with 90% accuracy and F1-Score ranging from 0.8 to1.

The growing demand for high-quality textile products requires efficient and intelligent fabric inspection systems. Convectional manual inspection procedure is not only time-consuming but also vulnerable to human errors. The proposed work in this study focused on optimizing a ResNet50 model with PSO (Particle Swarm Optimization) for real-time fabric classification that aligns well with the principles of Industry 5.0 and contributes meaningfully to several Sustainable Development Goals (SDGs). Using a custom dataset of fabric images (plain, satin, and twill) captured with a digital microscope, the proposed model leverages Particle Swarm Optimization (PSO) to fine-tune hyper parameters such as learning rate and momentum, enhancing the classification accuracy of ResNet50. This study claims the contribution of developing a comprehensive dataset providing a resource for training and assessing deep learning models for textile weave studies. The second contribution claimed is the integration of the PSO with ResNet50 model for hyper parameter adjustments, specifically improving learning rate and momentum in ResNet50. This integration significantly increases categorization precision, resulting in a highly efficient deep learning model tailored for textile applications. Our optimized model demonstrated a very high accuracy of 98.32% in correctly classifying the fabrics according to weave pattern. Thus, the proposed model could be used in real-time industrial applications for automatic fabric inspection and categorization. The proposed model also showed superior performance compared to CNN, ResNet201, MobileNet, VGG16 and Resnet50. Overall, the proposed inspection mechanism Moreover, it aligns well with Industry 5.0 principles and contributes to Sustainable Development Goals (SDGs) by offering a scalable, privacy-preserving, and intelligent textile quality control solution.

The influence of the bentonite content (1, 3, 5 wt%) on the mechanical properties of lightweightcotton (C), polyester (P) and polyester-cotton (P/C 50/50) fabrics was investigated. Starch was used asa water-insoluble binder for coating fabrics. Bentonite nanoparticles were obtained by repeated hydration,decantation and evaporation of the water dispersion. The bentonite particle size was determinedby the XRD method using the Debye-Scherrer equation. The diffraction of the laser beam was used todetermine particles size distribution. The addition of bentonite nanoclay significantly improved tensilestrength (26-61% and 99–118% in the warp and weft direction, respectively) and tear strength (4‒13%and 5–24% in the wrap and weft direction, respectively) of coated fabrics. Their abrasion resistancehas also slightly increased. The biggest changes were noted for the cotton fabric, the smallest for thepolyester fabric, which may result from the low compatibility between starch and the polyester fabric.

A simple, lightweight, and easy to develop Single Jersey Wearable Tribo-Electric Nano Generators (SJ-WTENG) were constructed using Cotton and Acrylic fabrics (Triboelectric series materials). Fabrics were also coated with Maghemite (γ-Fe₂O₃) nanoparticles (13 nm) to increase the electrical conductance of the samples. Compression and Vertical contact-separation modes were adopted for studying the performance of the developed samples. Along with a Single WTENG sample, the outputs of two samples connected in series were also measured. To study the effect of developed Maghemite (γ-Fe₂O₃) nanoparticle coating, non-coated fabrics WTENGs were also constructed and tested. The maximum voltage reached with the Maghemite (γ-Fe₂O₃) nanoparticle-coated SJ-WTENG samples was a time-varying signal of 7.68 volts peak to peak volts with an approximate frequency of 50.5 Hertz. A shotky diode-based full bridge rectifier was used to get the DC voltage. The rectified DC signal was observed to be 5 volts which was enough to light up an LED with a threshold voltage of 1.7 volts DC as well as charge 3.7 volts, 3.6Ah Li-ion battery pack. Results confirmed that the application of Maghemite (γ-Fe₂O₃) nanoparticles was useful in augmenting the output of the proposed SJ-WTENG design. The proposed system can be used to power the battery powered IOT (Internet of Things) devices, widely used in medical and body sensor network applications.

Fabric is an important part and parcel of our lives. In addition to fabric color and texture, fabric weave also determines the physical appearance and physical properties of the fabric. At times, it is necessary to determine the weave type of the fabric, for example, at sales outlets, or at customs for tax duty computations. Furthermore, the end-user may be required to confirm the fabric weave type. Conventionally, a manual invasive technique is used to determine the weave type of the fabric, which also requires extensive knowledge of the fabric weaving process. Therefore, in this study, we present an image-processing-based, non-invasive, low-cost fabric weave identification scheme. The experimental results are very encouraging results and show a 90% accuracy in detecting fabric weave type in real-time.

The farming of ground birds requires extensive labor for the timely feeding and watering of the birds, in addition to cleaning their manure. An autonomous farming system can not only reduce labor costs but can also ensure timely feeding and automatic watering. Moreover, IoT connectivity can help the farmer keep an eye on the birds while physically being away from the farming site. Inspired by this concept, this study presents the design and implementation of a smart autonomous cage for the farming of ground birds. A design of sensors based on ambient temperature and an air quality monitoring system, the mechatronic design of autonomous egg collection and a manure cleaning system are presented in this paper. A novel system control algorithm for the autonomous control of the smart cage is also presented.