## 3 Materials and Methods
### Methodology  
The techniques in the realm of artificial intelligence include both DL and ML. They are often used for plant image analysis because they can automatically identify useful patterns from the provided examples. The selection of features is an essential step in plant imaging. This step is equally as significant as the pixel value, edge width, and variance in pixel values throughout an area. A complex problem and crucial component of ML approaches is the selection of a suitable feature from the image. The performance of the system will suffer significantly from improper feature selection. As a result, there is a probability that ML approaches may choose incorrect features as a result of human perception and mistakes. When using DL methods, particularly CNNs, it is feasible to automatically extract useful information from whole images that the human eye cannot perceive [74; 75]. As a consequence of this, the DL method lowers the likelihood of making an incorrect choice regarding the features that should be used. Therefore, this research employed the aforementioned hypothesis and compared DL to ML to demonstrate the validity of this theory. The architecture and the functional mechanism of the bamboo plant classification system with k-fold cross-validation are illustrated in Figure 6. The methodology known as K5-CV, or five-fold cross-validation [76], was used for these studies. As seen in Figure 6, this procedure included randomly splitting each dataset into five subsets, with 80% of the input data samples utilized for training purposes and the remaining 20% for testing. Different species' samples were present in each fold or training and test set. Each fold or set used for training and testing included data from a different set of species. Figure 6: The model architecture for bamboo classification system with five-fold cross-validation. The classification performance of different learning models is measured using a confusion matrix. This matrix allows correlations between the classifier's efficiency and the tests' outcomes to be more easily identified. The confusion matrix reveals the proper and improper categorization of positive samples and the proper and improper categorization of negative samples. The bamboo dataset in this study consisted of ten bamboo classes C1, C2,..., C10 and the classification output was in the form of a confusion matrix, which consisted of the prediction in terms of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN), reflecting the expected positive and negative classes of all input datasets. The representation of the confusion matrix is given in Table 4.  
They served as the fundamental criteria in assessing the system's performance by calculating the accuracy (ACC), precision (PR), recall (RC), F1-score (F1S), sensitivity (SEN), and specificity (SPC); all these were measured in the form of percentages only. Some other attributes, like the positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) of the receiver operator characteristic (ROC), are also helpful for performance evaluation purposes [39; 77]. The parameters are expressed mathematically in Table 5.