## 1 Introduction
### Literature Review  
Machine learning has emerged as a powerful tool for optimizing natural fiber-production processes. These algorithms have been successfully implemented across various stages, from the initial characterization [19] of yarn and fabric properties to the critical tasks of fiber identification and quality assessment [20]. By leveraging spectral data and other relevant features, sophisticated machine-learning models can accurately classify fibers, enabling automated and efficient quality-control measures [21]. This technological advancement holds significant potential to revolutionize the natural fiber industry by enhancing productivity, reducing costs, and improving the overall quality of final products. Machine learning (ML) has the potential to revolutionize fiber development by significantly reducing the time [10] and cost associated with traditional laboratory testing. By analyzing vast amounts of data on fiber properties, ML models can predict fiber behavior with increasing accuracy, enabling the rapid design and optimization of fibers with tailored hygroscopic propertiesfor specific applications [22]. This data-driven approach can accelerate the development of innovative fiber-based materials for various industries, such as textiles, composites, and filtration [23]. The capacity of machine-learning algorithms to draw conclusions and patterns from massive, intricate datasets has drawn a lot of interest in recent years [24]. For instance, a study by [PERSON] et al. proposed a deep-learning model for cotton-fiber identification based on hyperspectral imaging, achieving an accuracy of 99.8% [25]. The commercial viability of natural fibers is significantly influenced by their quality attributes. A multitude of factors contribute to fiber quality, including but not limited to length, fineness, strength, and uniformity. To expedite and enhance quality assessment, researchers have increasingly employed machine-learning techniques. These models have demonstrated potential in predicting fiber quality based on a range of parameters, such as color, texture, and morphological characteristics [26; 27; 28]. A study by [PERSON] et al. used SVMs to predict the tensile strength and stiffness of jute fibers based on their length, diameter, and orientation [29]. Gradient Boosting is widely acknowledged as one of the most efficient approaches to developing predictive models [30]. It is an ensemble machine-learning approach that is typically used to solve regression and classification problems [31]. It creates a forecasted model, usually a decision tree, from an ensemble of brittle anticipated models [32]. Decision Tree Regression (DTR) is a subset of the supervised machine-learning (ML) technique that can be used for regression and classification issues [30]. This method solves the problem by using a tree representation to create a model that can forecast the desired variable [31]. Table 1 presents the various ML algorithms used with their key findings during the utilization of various natural fibers and their composites. Metrics are indispensable for assessing the efficacy of machine-learning models. These quantitative measures provide crucial insights into a model's performance on a particular dataset. By quantifying various aspects of model output, metrics enable rigorous comparisons between different models and inform decisions about model selection and optimization. A variety of metrics are commonly employed to assess model performance. For classification tasks, precision, recall, F1 score, and accuracy are frequently utilized to evaluate a model's ability to correctly classify instances. Precision quantifies the proportion of positive predictions that are truly positive, while recall measures the proportion of actual positives that are correctly identified. The F1 score offers a harmonic means of precision and recall, providing a balanced assessment. Accuracy represents the overall correct predictions relative to the total number of instances.