## 3 Materials and Methods
### Data Description  
Bamboo domain expertise involves many different morphological attributes or features used to identify the species, and all of this is described as dataset 1 (**DS1**). Meanwhile, dataset 2 (**DS2**) is the texture feature dataset, created by applying various feature extraction strategies, such as LBP and GLCM methods, on the image dataset of commercial bamboo species (**IDCBS10**). **Dataset 1 (**DS1**): This includes morphological attributes. * Shoot: the characteristic of bamboo shoot's color and hair color. * Culm (pole): height (m), diameter (cm), internode length (cm), color, texture, wall thickness (mm)
* Branch: number of branches
* Sheaths: three types of sheath--the culm sheath, the rhizome sheath, and the leaf sheath
* Culm sheath: long (cm), broad (cm), hair color
* Leaves: length (cm), width (cm)  
Experts use all these attributes to classify bamboo species in traditional methods. In each species, the values of every attribute vary, but mostly we see that they have some common values. These common values can be used as mean values. Table 2 describes these attributes for all ten commercial species. The values are kept in the lower range to the upper range. Figure 3 shows the mean distribution of the ten commercial bamboo species by using their morphological attributes. The variations are due to the soil quality, local climate conditions, cultivation techniques, applied fertilizer, age, etc. The advantage of dataset DS1 is that it is in a structured form, so, when applying any ML model, feature selection is easy using the correlation approach. However, the disadvantages are that DS1 was prepared with handcrafted features or created by taking the opinions of many domain experts. Thus, multiple factors, including human error, may influence the data, causing ML model overfitting on DS1. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & \multirow{2}{*}{**Column**} & \multicolumn{2}{c}{**Flowering**} & \multicolumn{2}{c}{**No. of**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} \\ \cline{4-14}
**Species** & \multirow{2}{*}{**No. of Cluster**} & \multicolumn{2}{c}{**Claim**} & \multicolumn{2}{c}{**Cycle**} & \multicolumn{2}{c}{**(Years)**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} \\ \cline{4-14}
**Hight (m)** & \multicolumn{2}{c}{**Diameter**} & \multicolumn{2}{c}{**Intemode**} & \multicolumn{2}{c}{**Tickness**} & & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} & \multicolumn{2}{c}{**} \\ \cline{4-14}
**Runbank knnles** & 18-25 & 20-30 & 10-18 & 30-45 & 2-3 & 30-50 & 20-25 & 10-15 & 15-30 & 0.8-1.5 & 15-20 & 12-30 \\
**Runbank ntala** & 20-30 & 10-25 & 3-10 & 25-55 & 0.6–23 & 20–60 & 16–20 & 16–26 & 15–35 & 2–4 & 20–25 & 8–18 \\
**Runbank** & 25–35 & 10-15 & 5-8 & 10-20 & 1–3 & 80-150 & 10–18 & 10–15 & 15–20 & 2-2.5 & 20–30 & 12–23 \\
**traplay spider** & & & & & & & & & & & \\
**Runbank ntemin** & 30–40 & 2-5 & 2–12 & 7–15 & 0.8–25 & 50–80 & 20–25 & 10–12 & 15–30 & 2–3 & 10–15 & 10–20 \\
**  
**Dataset 2 (DS2):** This includes texture features. This dataset was obtained by applying LBP and GLCM [33, 34, 35] texture feature extraction techniques to the image dataset. It includes many attributes, like entropy, energy, contrast, homogeneity, mean, standard deviation, correlation coefficient, etc. All these attributes are different for all 200 samples of each species, so a total of 2000 samples with 131 attributes are included. The advantage of dataset DS2 is that it is in a structured form, so, when applying any ML model, feature selection is easy using the correlation approach. However, the disadvantage of DS2 is that it contains 131 attributes and features, so the training of the ML model could require more training time if we use all these attributes.