# 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 & 92.42 & 0.0059 & 0.067 & 7.6407 & 0.924 & 0.003 & 93.84 & 92.57 & 93.91 \\ Naive Bayes Classifier & 96.21 & 0.0031 & 0.055 & 3.9692 & 0.962 & 0.001 & 96.17 & 96.71 & 96.53 \\ Multilayer Perception & 94.70 & 0.0091 & 0.056 & 11.8256 & 0.947 & 0.001 & 95.83 & 95.04 & 94.68 \\ Hoeffding Tree & 93.93 & 0.0045 & 0.064 & 5.8138 & 0.939 & 0.001 & 95.61 & 94.13 & 95.82 \\ J48 & 72.72 & 0.0233 & 0.144 & 30.2762 & 0.727 & 0.01 & 72.59 & 72.83 & 72.64 \\ Random Forest & 96.21 & 0.0358 & 0.105 & 46.5241 & 0.962 & 0.001 & 96.82 & 96.73 & 95.08 \\ CNN & 96.35 & 0.0391 & 0.121 & 41.3724 & 0.963 & 0.001 & 96.82 & 97.03 & 97.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy and error values for plant classification (25 class). \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 & 87.13 & 0.0057 & 0.0644 & 14.46 & 0.871 & 0.003 & 88.53 & 87.86 & 87.37 \\ Naïve Bayes Classifier & 89.38 & 0.0042 & 0.0614 & 10.67 & 0.894 & 0.002 & 90.82 & 90.27 & 89.67 \\ Multilayer Perception & 87.70 & 0.0051 & 0.0601 & 11.03 & 0.871 & 0.002 & 88.91 & 88.12 & 88.53 \\ Hoeffding Tree & 88.88 & 0.0045 & 0.0606 & 11.60 & 0.889 & 0.002 & 89.91 & 89.12 & 89.82 \\ J48 & 64.50 & 0.0151 & 0.1150 & 38.58 & 0.645 & 0.007 & 66.19 & 65.92 & 67.01 \\ Random Forest & 88.38 & 0.0206 & 0.0839 & 52.41 & 0.884 & 0.002 & 89.95 & 89.81 & 90.04 \\ CNN & 88.14 & 0.0062 & 0.0561 & 10.59 & 0.881 & 0.002 & 90.09 & 89.27 & 89.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy and error values for plant classification (50 class). \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 & 80.59 & 0.0056 & 0.0633 & 21.2627 & 0.806 & 0.003 & 82.41 & 82.14 & 81.96 \\ Naïve Bayes Classifier & 85.93 & 0.0038 & 0.0582 & 14.3344 & 0.859 & 0.002 & 85.79 & 86.17 & 86.61 \\ Multilayer Perception & 83.12 & 0.0051 & 0.0601 & 11.0300 & 0.832 & 0.003 & 84.92 & 84.27 & 85.06 \\ Hoeffding Tree & 85.85 & 0.0040 & 0.0572 & 15.1881 & 0.859 & 0.002 & 87.28 & 88.14 & 86.47 \\ J48 & 52.79 & 0.0129 & 0.1057 & 49.0242 & 0.528 & 0.007 & 54.61 & 55.07 & 54.62 \\ Random Forest & 85.39 & 0.0161 & 0.0776 & 61.2607 & 0.854 & 0.002 & 85.67 & 85.94 & 84.37 \\ CNN & 83.64 & 0.0047 & 0.0571 & 10.3729 & 0.836 & 0.003 & 84.93 & 85.07 & 84.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy and error values for plant classification (75 class). Figure 5 shows the accuracy of classification for 2, 5, 10, 25, 50, and 75 labels. Figure 6 demonstrates MAE, RAE, RMSE, and TP rates for the same number of labels in the leaf classification problem [77]. Several feature selection and preprocessing methods are applied to increase plant leaf classification accuracy, such as data cleaning, transformation, feature engineering, and splitting the data into appropriate sets. Experiments show in Table 10 that MP algorithms give 89.48% accuracy, NBC gives 89.63% accuracy, and the HT algorithm gives 89.92% accuracy for the classification of the 99 label problems. Fewer classes make classification easier and more accurate from a theoretical standpoint, but in practice, algorithms J48 can behave differently depending on the nature of the data and specific model hyperparameters. Therefore, J48 has lower accuracy than other classification algorithms in this dataset. CNN achieved 88.72% accuracy, slightly lower than Hoeffding Tree (89.92%), but higher than other models like Random Forest (86.81%). Figure 7 shows examples of plant leaves that were accurately classified by the proposed model used in this study.