# Integrating Root Morphology Based on Whole-Pullout Test of Model Roots: A Case Study
[24] noted that in root-soil composites, the pullout resistance of plant root systems is linearly correlated with the root crown width, the taproot length, and the number of lateral roots, with the primary influencing factors for vertical pullout resistance being the taproot length and the number of lateral roots. [PERSON] et al. [25] and [PERSON] et al. [26] considered that in root-soil composites, the pullout resistance of root systems increases in a power function relationship with the increase in root diameter and burial depth. The aforementioned studies demonstrate that the morphological characteristics of plant root systems such as the number of lateral roots, the taproot length, the root diameter, and the branching angle of the lateral root, all influence the pullout resistance of the root system. Contemporary research methodologies suggest that mathematical models can be established by employing machine learning techniques, multiple regression analyses, and 3D response surface analyses [27; 28; 29; 30]. In these models, different morphological characteristics of the plant root system are used as independent variables and the vertical pullout resistance of the root system is used as the output response. Continuous training and optimization of these models allow for the identification of the most sensitive factors and the best combination of these factors. This approach enables the prediction of the vertical pullout resistance of the root system under different combinations of factors. Toestablish these models, extensive experimental research is required to first understand the relationship between the morphological characteristics of the plant root system and vertical pullout resistance, thereby providing input samples for the model. The number of lateral roots, the taproot length, the branching angle of the lateral roots, and the unit weight of the soil around the root are representative factors that significantly affect this resistance. Existing studies predominantly employed real plant root systems obtained through field sampling when conducting laboratory experiments. However, the plant roots' properties decreased with the decrease in water content in the roots after excavation, leaving the soil [21]. Additionally, individual variations due to site conditions and human factors cause uncertainties, leading to poor experimental controllability. Consequently, this results in issues of high experiment variability and low precision. To address these challenges, this paper employs PC plastic model roots to simulate plant roots. An \(L_{16}\)(45) indoor pullout orthogonal experiment was conducted by considering the number of lateral roots, taproot length, the branching angle of the lateral roots, and the unit weight of soil around the root as four influencing factors. This study aims to investigate the sensitivity and significance of factors influencing the vertical pullout resistance of plant root systems, to analyze the effects of each factor, and to discuss the experimental results using a sensitivity index and a comprehensive \(\mu_{X}\) theoretical analysis method based on the whole root system pullout test in hopes of providing reliable samples for establishing models using machine learning techniques. The findings of this research can also guide the selection of root morphologies in vegetation slope protection projects.