## 6 Conclusions  
In this study, we first found that while manual recognition is the traditional method of bamboo classification, it is suitable only for those who are experts in the bamboo domain. Second, this process is often laborious and time-consuming. Therefore, to perform this task in an automated manner, we created two bamboo datasets: the first one is morphological, and the second one is image-based. In this study, we applied ML and DL models with a combination of majority multiclass voting (MajMulVot) ensemble algorithms to this dataset. The ML classification model performed well on the bamboo morphological dataset DS1 compared to all the other ML and DL models on other bamboo datasets such as texture (DS2) and image (IDCBS1) datasets. ML gave the highest accuracy of 99.95% with K5-CV and 99.12% without K5-CV on DS1. For DL models, we used the transfer learning approach to improve the robustness of the end model. The proposed ML and DL methods with the MajMulVot approach enhanced the bamboo classification's performance. The average accuracy of all ML-MajMulVot models on DS1 was improved by 0.37%, 1.04%, 0.16%, 0.77%, and 0.62% and on DS2 by 0.35%, 0.44%, 0.11%, 0.36%, and 1.84%. Similarly, the average accuracy of all DL-MajMulVot models was improved on DS3 by 0.75%, 0.75%, 1.01%, 0.3%, and 0.8%. Furthermore, the proposed DL-based MajMulVot algorithm was validated on the rice image dataset [98], where improvements of 1%, 0.5%, 0.5%, 1%, and 1.5% were seen in the accuracy against AlexNet, VGG16, ResNet18, VGG19, and GoogleNet, respectively.  
The proposed DL-based MajMulVot model performed well on both datasets, i.e., the bamboo image dataset and rice image dataset, significantly improving the classification accuracy of the five DL models. This shows that the DL-MajMulVot method is better in classifying bamboo than the ML method.  
Conceptualization, A.D.S.; methodology, A.D.S.; data curation, A.D.S.; formal analysis, A.D.S., S.A. and A.A.; software, A.D.S., S.A., A.A. and D.D.S.; validation, D.D.S., S.A. and L.S.; resources, D.D.S. and L.S.; data creation, A.D.S.; writing--original draft preparation, A.D.S.; writing--review and editing, L.S. and D.D.S.; supervision, D.D.S. and L.S.; funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors would like to thank the Director, Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur and CSIR-National Environmental Engineering Research Institute, Nagpur for providing the necessary facilities for this work.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
ACC Accuracy
ANN Artificial Neural Network  
AUV Area Under the Curve  
BOF Bag-of-Features  
CBR Case-Based Reasoning  
CNN Convolutional Neural Network  
DMF Digital Morphological Features  
DL Deep Learning  
DS Dataset DT Decision Tree  
F1S F1-Score  
FAO Food and Agriculture Organization  
FC Fully Connected  
FN False Negative  
FP False Positive  
GLCM Gray-Level Co-Occurrence Matrix  
IDCBS10 Image Dataset of Commercial Bamboo Species  
ILSVRC ImageNet Large Scale Visual Recognition Challenge  
K5-CV K-Fold Cross-validation  
KNN K-Nearest Neighbors  
LBP Local Binary Patterns  
LR Logistic Regression  
MajMulVot Majority Multiclass Voting  
MHA Million Hectare Area  
ML Machine Learning  
MLP Multilayer Perceptron  
NIR Near-Infrared Reflectance  
NPV Negative Predictive Value  
NTFP Non-Timber Forest Products