# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 6 Experimental Results and Discussions
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **MAE** & **RMSE** & **RAE** & **TP** & **FP** & **Precision** & **F1** & **Recall** \\ \hline Bayes Net & 63.28 & 0.0078 & 0.0778 & 38.787 & 0.633 & 0.004 & 65.17 & 63.91 & 64.54 \\ Naive Bayes Classifier & 66.97 & 0.0067 & 0.0802 & 33.599 & 0.670 & 0.003 & 68.97 & 67.63 & 68.49 \\ Multilayer Perception & 73.13 & 0.0034 & 0.0502 & 44.614 & 0.731 & 0.003 & 75.32 & 73.86 & 74.59 \\ Hoeffding Tree & 70.73 & 0.006 & 0.0747 & 29.772 & 0.707 & 0.003 & 72.85 & 71.43 & 71.89 \\ J48 & 70.25 & 0.0144 & 0.0777 & 71.786 & 0.703 & 0.003 & 71.49 & 70.36 & 72.04 \\ Random Forest & 72.5 & 0.014 & 0.0757 & 69.786 & 0.725 & 0.003 & 71.13 & 72.59 & 72.47 \\ CNN & 72.82 & 0.0042 & 0.0526 & 41.148 & 0.728 & 0.003 & 73.92 & 73.11 & 72.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy and error values for plant classification with texture feature set. Accuracy helps assess the quality and effectiveness of the model in solving the classification task. A high level of accuracy indicates that the model has learned meaningful patterns and relationships in the data, leading to accurate predictions. On the other hand, low accuracy may indicate issues such as underfitting, overfitting, or inadequate feature representation. In this work, several feature selection and machine learning algorithms are proposed to increase the accuracy of the classification. In this part of the experiments, we move from a classification task with a small number of classes (2 classes) to one with a high number (99 classes) of classes. Classification analysis of classes in the dataset with 2, 5, 10, 25, 50, and 75 is demonstrated in Tables 4-9. The classification problem is typically less complex with fewer classes, and the decision boundaries between classes may be more distinct. Simple models like Logistic Regression or Naive Bayes classifiers may perform well on such tasks due to their ability to capture linear or simple relationships between features and classes. However, as the number of classes increases, the problem becomes more complex, requiring more sophisticated models like neural networks or ensemble methods to accurately capture the intricate relationships between features and classes. Additionally, addressing challenges like class imbalance, data sparsity, and feature relevance becomes increasingly important as the number of classes in the classification task grows. The classification accuracy with a 2-class, 5-class, and 10-class problem is 100% with the Naive Bayes classifier and more than 96% with the multilayer perception algorithm. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **MAE** & **RMSE** & **RAE** & **TP** & **FP** & **Precision** & **F1** & **Recall** \\ \hline Bayes Net & 98.11 & 0.0039 & 0.0578 & 2.1841 & 0.981 & 0.001 & 99.17 & 98.71 & 99.64 \\ Naive Bayes Classifier & 100 & 0.0001 & 0.0012 & 0.0409 & 1 & 0 & 100 & 100 & 100 \\ Multilayer Perception & 96.22 & 0.0165 & 0.077 & 9.109 & 0.962 & 0.004 & 96.17 & 97.84 & 96.27 \\ Hoeffding Tree & 98.11 & 0.0038 & 0.0608 & 2.1115 & 0.981 & 0.001 & 99.72 & 99.35 & 99.47 \\ J48 & 81.13 & 0.0432 & 0.1869 & 23.9149 & 0.811 & 0.025 & 82.67 & 82.46 & 81.37 \\ Random Forest & 96.22 & 0.0557 & 0.1183 & 30.7785 & 0.962 & 0.002 & 97.34 & 97.37 & 98.04 \\ CNN & 97.83 & 0.0041 & 0.0517 & 2.379 & 0.978 & 0.001 & 98.82 & 98.26 & 98.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy and error values for plant classification (10 class).