# 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} \hline \hline
**Algorithm** & **Accuracy** & **MAE** & **RMSE** & **RAE** & **TP** & **FP** & **Precision** & **F1** & **Recall** \\ \hline Bayes Net & 100 & 0 & 0 & 0 & 1 & 0 & 100 & 100 & 100 \\ Naive Bayes Classifier & 100 & 0 & 0 & 0 & 1 & 0 & 100 & 100 & 100 \\ Multilayer Perception & 100 & 0.0076 & 0.0086 & 1.514 & 1 & 0 & 100 & 100 & 100 \\ Hoeffding Tree & 100 & 0.0076 & 0.0086 & 1.514 & 1 & 0 & 100 & 100 & 100 \\ J48 & 93.75 & 0.0625 & 0.25 & 12.328 & 0.938 & 0.049 & 94.12 & 94.01 & 93.82 \\ Random Forest & 100 & 0.0609 & 0.0833 & 12.09 & 1 & 0 & 100 & 100 & 100 \\ CNN & 100 & 0.0063 & 0.0081 & 1.428 & 1 & 0 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy and error values for plant classification (2 class). \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 & 96.25 & 0.0145 & 0.0952 & 4.5204 & 0.963 & 0.009 & 97.43 & 97.27 & 98.01 \\ Naive Bayes Classifier & 100 & 0.0001 & 0.0015 & 0.0382 & 1 & 0 & 100 & 100 & 100 \\ Multilayer Perception & 98.75 & 0.0177 & 0.0733 & 5.5091 & 0.998 & 0.003 & 98.67 & 98.42 & 99.03 \\ Hoeffding Tree & 96.15 & 0.0135 & 0.1087 & 4.1312 & 0.962 & 0.009 & 97.62 & 97.34 & 98.04 \\ J48 & 77.5 & 0.0911 & 0.2929 & 28.4124 & 0.775 & 0.056 & 78.92 & 78.19 & 77.61 \\ Random Forest & 97.5 & 0.0847 & 0.1476 & 26.4045 & 0.975 & 0.006 & 98.91 & 98.96 & 97.16 \\ CNN & 99.26 & 0.0162 & 0.0649 & 5.2344 & 0.992 & 0.002 & 99.74 & 99.61 & 99.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy and error values for plant classification (5 class). The mean absolute error (MAE) calculates the average of the absolute differences between predicted and actual values. Root Mean Squared Error (RMSE) is a frequently used measure of the differences between values predicted by a model, or an estimator and the actual values observed. RMSE is particularly useful in machine learning-based classification modeling to evaluate the performance of a model. Relative Absolute Error (RAE) measures the total absolute error in prediction relative to the total absolute error of a simple baseline model, usually the mean of the observed data. Precision is the ratio of true positive predictions to all positive predictions. It tells us how many of the positive predictions made by the model are correct. Another metric recall, also known as sensitivity or true-positive rate, is the ratio of true-positive predictions to all actual positive cases. F1 metric provides a single metric that balances both precision and recall. All these metrics demonstrate the absolute and relative performance of the plant leaf classification model. True-positive (TP) and false-positive (FP) metrics are critical metrics to measure the success of the developed models in machine learning. Because they offer detailed insights into the performance of classification models, they enable the calculation of essential evaluation metrics, help understand model bias and behavior, support cost-sensitive decision-making, and aid in fine-tuning and comparing models for optimal performance. The average values of TP and FP after classification are given in the tables. Our plant leaf classification methods have high TP and low FP values, indicating that the developed models perform well. On the other hand, accuracy is lower in 25-class, 50-class, and 75-class problems. NBC gives 96.21% accuracy with the 25-class classification, and MP gives 94.70% accuracy. Tables demonstrate other statistical evaluation measurements, such as MAE, RMSE, RAE, TP, and FP ratios.