## 1 Introduction
The regression problem depicts the relationship between one continuous dependent variable and multiple independent variables. LA modeling in the present study underwent regression problem analysis as a continuous dependent variable leaf area (LA) was predicted by a numerical value using two independent variables leaf length (L) and leaf width (W). Multiple linear regression (MLR), the most common form of linear regression analysis, computes a weighted sum of the input features and a bias term, the intercept. Support vector regression (SVR) is a supervised ML algorithm to predict the dependent variable in the model. In principle, SVR is same as support vector machine (SVM) [25] which is capable of computing linear and non-linear regression problems. The gradient boosting regression (GBR) [26] algorithm is a decision tree-based ensemble technique for solving both linear and non-linear regression problems. The high flexibility to construct new base-learners which correlates maximally with the negative gradient of the loss function makes it a highly customizable data driven ML technique. Boosting algorithms are also very easy for various model design implementations, thus imparting gradient boosting machine applications in various fields of both practical and data mining and machine learning studies [27; 28; 29]. It is faster and has better model performance due to its principle of hypothesis boosting; the requirement of minimum data pre-processing has outperformed Artificial Neural Network (ANN) and SVM in many reports [30; 31; 32]. ANN techniques have been used in a limited number of crops which are summarized in the paper [33]. These models are self-adaptive, data driven, and non-linear in nature which helps to find the relationship between the predictor and predicted [34]. Performance metrices have been used for evaluation in multiple machine learning algorithms, but a unified and single standard metric is difficult to find. Another drawback of these metrices is since regression problem may have multiple values ranging from zero to infinity, merely a single metric cannot solve the problem of regression with respect to the ground truth elements in the study. Thus, in our study three performance evaluation metrices were used to assess the results of regression viz., coefficient of determination (R-squared or R\({}^{2}\)[35]) which determines the proportion of variance in the dependent variable which can be predicted from the independent variables; mean absolute error (MAE [36]) depicting the quality of fit in terms of the distance of the regressor to the actual training points; and Root mean Square Error (RMSE) for the detection of outliers [37]. We also used another statistical ranking method Average Rank (AR) that follows [PERSON]'s M statistic [38] to select the best model for _Dendrobium_ orchid. LA modeling has been reported for many crops including apple [39], walnut [13], apricot [40], onion [41], jatropha [42], cacao [43], cherry cultivars [14], chestnut [44], grapes [45,46], green and black peppers [33,47,48], ginger [49], medicinal and aromatic plants [50], niagara and grave vines [51], som [52], mango [53], tomato [54], cotton [55,56,57], multiple crops [58], kiwi fruit [59], durian [60], pecan [61], forest tree [62], and hazelnut [63]. Ornamentals received meagre attention; among them LA modeling was performed in zinnia [64], sunflower [65], rose [66], Euphorbia \(\times\) lomi Thai hybrids [67], bedding plants [68], and bougainvillea [69]. The development of leaf dry weight and leaf area models in the four cultivars of Phalaenopsis orchids [70] was reported by considering the length and width of leaves and linear regression analysis, but a major limitation is the non-utilization of the progressively utilized ML techniques. Advanced ML techniques to predict leaf area are reported in a very limited number of crops and the existing LA models are mostly based on linear regression analysis.