## 2 Materials and Methods
### Dataset  
A total of 1589 _D. mobile_ leaf images were collected by Samsung Galaxy J7 smartphone for the study. The image was captured with a reference scale and the ImageJ software was used for estimation of the length, width, and area of the individual leaf [75]. The color threshold function of ImageJ was used to demarcate the captured leaf area from its background, thus automatically calculating the region of interest, i.e., measured leaf area (LA). The dataset contains one continuous dependent variable, i.e., leaf area (LA) and two independent variables--leaf length (L) and leaf width (W). The measurement of the leaf length (L) was taken from the tip of the lamina to the intersection point between the leaf and petiole. The leaf width (W) measurement was taken from end-to-end point between the broadest part of the lamina exactly perpendicular to the midrib of _D. mobile_ lamina. All the variables were measured in cm scale. The values of three variables, length (L), width (W), and leaf area (LA) were stored in csv format for further model development processing and submitted to the dataset repository Mendeley data [[[http://doi.org/10.17632/8](http://doi.org/10.17632/8) tk2 sc4 tyg.1]([http://doi.org/10.17632/8](http://doi.org/10.17632/8) tk2 sc4 tyg.1) (published on 28 June 2021)] and KRISHI, ICAR research data repository for knowledge management [[[http://krishi.icar.gov.in/jspui/handle/123456789/71908](http://krishi.icar.gov.in/jspui/handle/123456789/71908)]([http://krishi.icar.gov.in/jspui/handle/123456789/71908](http://krishi.icar.gov.in/jspui/handle/123456789/71908)) (published on 6 May 2022)] (Supplementary S1). Table 1 depicts the different combination of inputs of L and W along with their ML models. In this experiment three orders of L and W were taken, so L, L\({}^{2}\), and L\({}^{3}\) were combined with W, W\({}^{2}\), and W\({}^{3}\). Each L and its higher order were combined with W and its higher order. The total number of input combination thus can be calculated as 3 \(\times\) 3, i.e., 9. These nine combinations of inputs were used for MLR, SVR, and GBR techniques. The higher orders of L and W were taken to observe their effect on model building. For ANN, four types of architectures were designed and the total number of models were 4 \(\times\) 9 = 36. Finally, the nine best ANN models were selected based on R\({}^{2}\), MAE, and RMSE values (Figure 3 and Table 2).  
Figure 2: Images of individual leaf of _D. mobile_ captured through smartphone with a reference scale for measurement of leaf length (L) and leaf width (W).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Input Variables** & **Output Variable** &
\begin{tabular}{c} **Layers** \\ **(Input-Hidden-Output)** \\ \end{tabular} \\ \hline ANN1 & L, W & LA & 2-3-1 \\ ANN2 & L, W & LA & 2-5-1 \\ ANN3 & L, W & LA & 2-10-1 \\ ANN4 & L, W & LA & 2-3-3-1 \\ ANN5 & L, W\({}^{2}\) & LA & 2-3-1 \\ ANN6 & L, W\({}^{2}\) & LA & 2-5-1 \\ ANN7 & L, W\({}^{2}\) & LA & 2-10-1 \\ ANN8 & L, W\({}^{2}\) & LA & 2-3-3-1 \\ ANN9 & L, W\({}^{3}\) & LA & 2-3-1 \\ ANN10 & L, W\({}^{3}\) & LA & 2-5-1 \\ ANN11 & L, W\({}^{3}\) & LA & 2-10-1 \\ ANN12 & L, W\({}^{3}\) & LA & 2-3-3-1 \\ ANN13 & L\({}^{2}\),W & LA & 2-3-1 \\ ANN14 & L\({}^{2}\),W & LA & 2-5-1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Combination of different inputs for ANN techniques with different ANN architectures.  
Figure 3: ANN network structures used in this study includes three types of layers. Input layer (with input neuron i\({}_{1}\) and i\({}_{2}\)) taking values from leaf length (L) and leaf width (W), hidden layer (H\({}_{1}\), H\({}_{2}\)) (with hidden neuron h\({}_{k}\), where k = 3 or 5 when architecture has single hidden layer and k = 3 when architecture has double hidden layer) and output layer (with output neuron O\({}_{1}\)) were utilized to predict the leaf area (LA). Furthermore, 4 \(\times\) 9 = 36 models of ANN were developed. W and f depict the weights and sigmoid activation function, respectively.