## 5 Discussion  
Although bamboo classification is best performed by domain experts or traditional methods using real morphological attributes [96], this requires more time and expert knowledge in the bamboo field. In addition, the classification of species belonging to the same genera and the different environmental conditions are major challenges in morphological classification. To overcome this, some researchers use DNA barcoding and machine learning techniques for bamboo species classification [97], but it has the limitation of requiring a huge public dataset. Therefore, in this research paper, we present a novel method for bamboo classification using different real datasets of bamboo that were created by visiting many different locations where bamboo is grown. In earlier research, firstly, it was observed that, for plant classification, only the leaf and flower were used, but, in the case of bamboo, there is not a single public dataset for either the leaf or culm. Second, it was found that the performance of several CNN architectures was inconsistent regarding the number of deep layers and the parameters used. With this motivation, we first created a bamboo database for ten commercial species, each having 200 images. Second, we devised MajMul-Vot, an ensemble approach that uses the combined expertise of many models to improve \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Dataset with**} & \multicolumn{8}{c}{**Performance Metric (\%)**} \\ \cline{3-8}  & & **All/DL Classifier** & **ACC** & **F1S** & **SEN** & **SPC** & **PPV** & **NPV** \\ \hline \multirow{5}{*}{Without (K5-CV)} & DS1-ML-KNN & 99.12 & 99.00 & 99.02 & 99.48 & 99.00 & 99.38 \\  & DS2-ML-SVM & 82.27 & 88.79 & 90.55 & 97.67 & 81.04 & 71.74 \\  & DS4-ML-KNN & 98.04 & 99.01 & 99.01 & 71.42 & 99.01 & 71.42 \\  & IDCB810-DL-VGG16 & 88.80 & 91.66 & 92.80 & 93.05 & 91.25 & 86.84 \\  & DS4-DL-VGG16 & 98.50 & 100 & 100 & 100 & 100 & 100 \\ \hline \multirow{5}{*}{With (K5-CV)} & DS1-ML-MajMulVot & 99.95 & 99.24 & 99.45 & 99.97 & 99.95 & 99.91 \\  & DS2-ML-MajMulVot & 86.96 & 85.93 & 84.05 & 95.08 & 87.54 & 91.74 \\ \cline{1-1}  & IDCB810-DL-MajMulVot & 92.80 & 93.75 & 100 & 90.47 & 93.23 & 100 \\ \cline{1-1}  & DS4-ML-MajMulVot & 98.81 & 99.40 & 99.40 & 81.44 & 99.40 & 82.48 \\ \cline{1-1}  & DS4-DL-MajMulVot & 99.50 & 100 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance of best ML/DL model on DS1, DS2, IDCB810, and DS4 with/without K5-CV and MajMulVot. Figure 14: Performance comparison of ML/DL models with and without MajMulVot algorithm. the classification accuracy by a large margin. The proposed MajMulVot approach was applied to five deep learning models with different types of architecture, such as AlexNet, VGG16, ResNet18, VGG19, and GoogleNet; in the same way, we applied this MajMulVot algorithm to five machine learning models, namely LR, SVM, KNN, DT, and RF. We also adopted a 5-fold CV protocol that applied to both ML and DL models for training purposes. Compared to existing DL models, the DL-based MajMulVot method has demonstrated a notable improvement in mean performance on bamboo image datasets: ACC: 4.05%, F1S: 2.09%, SE: 7.2%, SP: 2.58%, PPV: 1.98%, and NPV: 13.16%. Similarly, ML-MajMulVot also enhanced the mean performance on two datasets as follows: DS1--ACC: 99.59, F1S: 99.24, SE: 99.45, SP: 99.97, PPV: 99.95, and NPV: 99.91; DS2--ACC: 86.96, F1S: 85.93, SE: 84.05, SP: 95.08, PPV: 87.54, and NPV: 91.74. In association with the number of layers in the DL models, inconsistencies in accuracy were detected; this leads to the idea that a large number of layers in the CNN model is not always needed for outstanding performance. Moreover, the DL models were compared to the ML models in terms of their overall performance; for this, we used four different datasets, of which DS1, DS2, and IDCBS10 were bamboo datasets, and one was a benchmark dataset.