## 1 Introduction
This superior property makes it very useful and there are more than 1500 documented applications from different sectors, such as food [21], pulp and paper [22], construction and building materials [23], engineering and industry [24], fuel, raw materials, etc. [25]. It plays a crucial role in maintaining the social, economic, and environmental development of nations, and its identification has been a transdisciplinary priority in both botanical taxonomy and computer vision, with researchers collaborating across disciplines. The automated and accurate identification of commercial bamboo species helps a wide range of stakeholders, including farmers, foresters, pharmacologists, taxonomists, biologists, technical workers of environmental agencies, and ordinary individuals [21; 22; 24; 25]. All these groups use their expert skills and knowledge accumulation for accurate species identification or classification. Nonetheless, the manual identification and recognition procedure is often laborious, tedious, and time-consuming [26]. Therefore, we need an automated system that can identify the bamboo species quickly and without human involvement, relieving all stakeholders in the bamboo industry. Hence, a significant number of researchers have carried out investigations in favor of the automated categorization of plants according to their different characteristics in images [27; 28; 29]. Consequently, this has fueled the interest in developing automated systems for the identification of different bamboo plant species. A completely automated system for the recognition of commercial plants using computer vision and deep learning (DL) techniques has been presented. The automated identification of plants has improved considerably in the last few years, particularly thanks to the recent advances in ML algorithms such as random forest, logistic regression, convolutional neural networks (CNN), ResNet, and other deep learning techniques [30, 31]. However, we anticipate a CNN trained on image datasets to obtain greater absolute accuracy levels. In this research article, we describe and propose a comprehensive and automated bamboo species categorization and classification system, especially for commercial bamboo, using its morphological and texture attributes. We use the CNN pre-trained model as a transfer learning (TL) method [29, 32] to train our DL model, which classifies ten commercial bamboo species. The methodology used for classification is based on ML and DL with a majority multiclass voting (MajMulVot)-based ensemble algorithm. The most significant findings and contributions of this study are as follows:  
* To the best of our knowledge, a morphological and image database of bamboo is not available from any other source. Thus, we describe the creation of a novel morphological and image dataset for ten commercial species by visiting different sites around Maharashtra where bamboo is growing. * We also create a texture feature dataset for all ten species using local binary patterns (LBP) and gray-level co-occurrence matrix (GLCM)-based methods [33, 34, 35]. * We examine the possibility of distinguishing bamboo species without the appearance of blossoms. * We seek to find the most precise combination of morphological attributes that play a vital role in determining the commercial value and can be used for species classification [36, 37]. * Extensive experimentation is done using feature creation and selection and ML classifiers such as logistic regression (LR), decision trees (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) [31, 38]. * We use the TL method to build our DL model using a pre-trained CNN model, i.e., AlexNet, VGG16, ResNet18, VGG19, and GoogleNet, for bamboo classification [2, 39]. * We analyze a variety of CNN architectures to extract features and compare their accuracy with the help of the ensemble MajMulVot algorithm. It can be applied not only to our dataset but also to a benchmark rice image open-source dataset [2, 39, 40, 41].