## 2 Materials and Methods
### Regression Models  
Eight regression models were trained to predict fresh biomass: Linear Regression (LR), Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO) Regression, Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), and k-Nearest Neighbours (kNN). LR is one of the simplest and most widely used methods for assessing the relationship between predictive variables and targets. This technique assumes that the relation is linear, and consequently, a change in one variable corresponds to a proportional change in the other [38; 39]. Being a simple method, Linear Regression is often the preferred method for analyzing small datasets, and its results are also relatively easy to interpret. However, this approach may become ineffective when there are many predictive variables because the model has difficulty managing complexity or a situation in which the relationship between predictive and target variables is strongly non-linear. Ridge Regression is used to address multicollinearity, high correlation between predictor variables, and overfitting in Linear Regression models [40]. It is a form of regularized regression that adds a penalty term to the loss function to constrain or regularize the coefficients of the model, making them smaller and more stable [41]. LASSO aims to identify the variables and corresponding regression coefficients, leading to a model that minimizes the prediction error [42; 43; 44]. This is achieved by imposing a constraint on the model parameters, which \"shrink\" the regression coefficients towards zero. Variables with a regression coefficient of zero after shrinkage are excluded from the model. SVR is employed to identify a function that best represents the relationship between input features and the target variable [10]. It is a machine learning technique focused on learning the importance of each feature in capturing the relationship between inputs and outputs [45]. SVR achieves this by mapping the original non-linear input space into a higher-dimensional feature space through the use of a kernel function [46]. This transformation enables the handling of non-linear relationships, with the resulting feature space, represented by the kernel matrix, being utilized to solve the regression task. DT is a powerful machine learning algorithm for identifying relevant features and uncovering patterns in large datasets, making it valuable for both classification and predictive modelling [47; 48; 49]. DT iteratively divides a dataset based on decision rules, selecting the feature that best separates the data. Each subdivision reduces the impurity and createsinternal nodes until the stop criteria are met, with the final nodes containing the predictions [50]. Its ability to provide clear and interpretable decision rules has contributed to its widespread use in exploratory data analysis and predictive analytics for over twenty years. RF is a robust regression method that leverages an ensemble of Decision Trees [51]. For each input, the predictions from the individual trees are aggregated to produce a final output [52]. To enhance the diversity of the trees and reduce their correlation, RF employs bagging, a technique that generates distinct subsets of the data for training each tree. This approach effectively boosts both the model's robustness and predictive accuracy [53]. GB is an ensemble learning technique applicable to both regression and classification tasks [54]. It constructs a model iteratively by sequentially fitting weak learners and combining their predictions. The underlying principle of boosting is that it incrementally refines the model by considering all features in each iteration, thereby learning a functional mapping for each feature in the process [55]. The kNN algorithm is a non-parametric method that estimates the output for a given input based on the average value of its closest neighbours [10; 56].