# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 7 Discussion  
During the classification process, some algorithms struggled to accurately identify certain plant leaf samples due to various factors. These included class imbalance, high \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 & 81.75 & 0.0041 & 0.0513 & 21.2208 & 0.815 & 0.02 & 83.47 & 82.39 & 83.51 \\ Naive Bayes Classifier & 89.63 & 0.0023 & 0.042 & 14.492 & 0.896 & 0.01 & 89.17 & 88.24 & 88.67 \\ Multilayer Perception & 89.48 & 0.004 & 0.0621 & 10.12 & 0.897 & 0.001 & 87.91 & 88.34 & 88.09 \\ Hoeffding Tree & 89.92 & 0.0030 & 0.0424 & 15.7 & 0.899 & 0.01 & 91.27 & 91.35 & 90.82 \\ J48 & 57.87 & 0.0152 & 0.1251 & 51.75 & 0.581 & 0.008 & 60.18 & 59.67 & 60.36 \\ Random Forest & 86.81 & 0.0037 & 0.0584 & 14.33 & 0.859 & 0.002 & 87.26 & 86.54 & 86.19 \\ CNN & 88.72 & 0.0034 & 0.0526 & 13.42 & 0.887 & 0.02 & 89.92 & 89.97 & 88.72 \\ \hline \hline \end{tabular}
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Table 10: Accuracy and error values after feature selection and structural data. Figure 8: Sample of incorrectly classified leaves. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **MAE** & **TP** & **FP** & **Precision** & **F1** & **Recall** \\ \hline Proposed Model & 89.92 & 0.0030 & 0.899 & 0.01 & 91.27 & 91.35 & 90.82 \\ CNN & 88.72 & 0.0034 & 0.887 & 0.002 & 89.92 & 89.97 & 88.72 \\ ResNet-50 & 87.24 & 0.0042 & 0.869 & 0.03 & 88.17 & 88.39 & 89.02 \\ DenseNet-121 & 87.38 & 0.0051 & 0.872 & 0.02 & 87.62 & 88.36 & 86.57 \\ Random Forest & 86.81 & 0.0037 & 0.887 & 0.02 & 87.26 & 86.54 & 86.19 \\ Support Vector Machine & 88.27 & 0.0031 & 0.885 & 0.002 & 87.64 & 88.02 & 86.61 \\ \hline \hline \end{tabular}
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Table 11: Comparison table with the proposed algorithm with state-of-the-art models. Figure 7: Samples of correctly classified leaves. variability within the same class, similarities between different classes, complexity and redundancy in features, overfitting, and inconsistencies in image quality. A key challenge is class imbalance, where some species are overrepresented while others have very few samples. Algorithms like Naive Bayes or Multilayer Perceptron may have difficulty with underrepresented classes, leading to misclassifications, especially for rare species. Hoeffding Trees, which adaptively learn, manage imbalance better but still face difficulties in extreme cases. For example, if one species has 200 samples and another has only 5, the algorithm tends to favor the more common species, making it harder to correctly classify the rarer ones. Additionally, leaves from the same species may appear different due to factors like aging, lighting, or the season in which they were collected, while leaves from different species might look very similar. This creates challenges for the model. For instance, two species may have similar textures but different shapes, and if the algorithm focuses too much on texture, it may misclassify the species. Conversely, young and old leaves of the same species can look quite different, causing issues with intra-class variability. Even though advanced techniques like PCA and LDA were used to reduce feature dimensionality, some irrelevant features may remain, adding noise to the model and complicating classification. For example, models like Random Forest may struggle to differentiate between species if certain features, like shape, dominate the classification process when texture should also be considered. One of the reasons why the Hoeffding Tree algorithm outperforms others, such as J48, is its ability to handle high-dimensional, imbalanced datasets effectively. The Hoeffding Tree's incremental learning approach allows it to adapt dynamically to the data as they are streamed, making it particularly well-suited for datasets with a large number of classes, such as the 99-class Cope Leaf Dataset.