# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 6 Experimental Results and Discussions  
Plant classification using machine learning algorithms involves training algorithms to identify and categorize different species of plants based on features extracted from images, such as leaf shapes, textures, colors, and other morphological characteristics. This work applies the Cope Leaf Dataset to several feature selection and ML algorithms to achieve a suitable model to classify plant leaves. The Cope Leaf Dataset is a popular dataset used for leaf classification tasks in machine learning. It consists of images of leaves from various plant species, along with corresponding labels indicating the species of each leaf. Like other leaf classification datasets, the Cope leaf dataset may present challenges such as class imbalance, variations in image quality, and intra-class variability due to factors like leaf aging and environmental conditions. We aim to address these challenges by building robust classification models. The dataset consists of 1584 images with 99 labels. Each record has 64 attributes, including each label's characteristics for margin, shape, and texture [6]. In this work, both probabilistic and rule-based machine learning algorithms are applied to the raw dataset. Table 2 shows the accuracy and statistical analysis of classification with the plant leaf margin feature set. The mean absolute error (MAE), Root Mean Squared Error (RMSE), and Relative Absolute Error (RAE) are given in Table 2. Table 3 introduces the texture feature set, which results similarly to the shape feature set. Results show that classification using a margin feature set creates a more suitable model plant leaf dataset. The volume of the dataset and the number of classes (99) make the classification problem very challenging. The Naive Bayes classifier gives the highest accuracy for the raw data. Naive Bayes classifiers perform well because they make strong and naive independence assumptions about the features. It provides straightforward explanations of the classification decision based on the probabilities of different classes given the input features [74; 75; 76]. Figure 4 demonstrates the accuracy of ML algorithms with the classification of margin, texture, and all feature datasets. \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 & 79 & 0.0047 & 0.0575 & 23.361 & 0.79 & 0.02 & 80.37 & 79.79 & 80.18 \\ Naive Bayes Classifier & 85.18 & 0.0031 & 0.0529 & 15.311 & 0.852 & 0.002 & 86.73 & 86.03 & 86.88 \\ Multilayer Perception & 83.13 & 0.0049 & 0.0509 & 24.682 & 0.831 & 0.002 & 85.62 & 83.96 & 84.79 \\ Hoeffding Tree & 83.87 & 0.0034 & 0.0529 & 16.787 & 0.839 & 0.002 & 86.38 & 84.62 & 85.54 \\ J48 & 50.5 & 0.0104 & 0.0951 & 51.952 & 0.505 & 0.005 & 52.01 & 51.37 & 51.51 \\ Random Forest & 82.5 & 0.0132 & 0.0716 & 65.873 & 0.825 & 0.002 & 84.97 & 83.32 & 84.15 \\ CNN & 81.72 & 0.0057 & 0.0537 & 22.601 & 0.818 & 0.002 & 84.17 & 82.53 & 83.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy and error values for plant classification with margin feature set. Figure 4: Accuracy with margin features, texture features, and after-feature selection.