# Ontology with Deep Learning for Forest Image Classification
## 10 Conclusions  
The proposed model for classifying images in this study uses features extracted by an ensemble deep learning technique to train classifiers, and the training is based on the taxonomic relationships between categories. Metrics such as accuracy, RMSE, confusion matrix, and ROC AUC curves were used to evaluate the model's performance.  
Concepts related to image categories and the associated taxonomic relationship between them were both used to build the ontology. The ontology provided the graphical  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Test Images** & **Correctly Classified** & **Misclassified** & **ROC\_AUC** & **RMSE** & **Accuracy** \\ \hline kNN & 152 & 124 & 28 & 0.97 & 1.530 & 0.816 \\
**Ontological Bagging** & **152** & **146** & **6** & **0.99** & **0.532** & **0.961** \\ RF & 152 & 131 & 21 & 0.99 & 1.094 & 0.862 \\ Decision Tree & 152 & 98 & 54 & 0.81 & 2.090 & 0.645 \\ SVM & 152 & 135 & 17 & 0.98 & 1.048 & 0.888 \\ GaussianNB & 152 & 97 & 55 & 0.79 & 1.678 & 0.638 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantitative comparison of models.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Accuracy** \\ \hline Ontology and Hmax model [11] & 63\% \\ Ontological random forest [1] & 55\% \\ Ontology and CNN [35] & 67.27\% \\ Deep learning model [36] & 93.73\% \\ Ontology and bag of visual words [39] & 59\% \\ Ontological-based model [37] & 98\% \\ Efficient deep learning combined with SVM [38] & 99\% \\
**Ontological bagging algorithm based on linear SVM** & **96\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy obtained from other models.  
semantic information that describes the training images. Hypernym classifiers were trained recursively using features obtained from each super-image category. Lastly, the test images were classified into their respective classes by using both the hypernym and hyponym classifiers. It is noteworthy that the proposed model of harmonizing deep learning models and ontology obtained superior performance when compared to baseline methods. The ontological bagging approach can be used in the forestry domain to classify trees according to their species and to classify vegetation into different types and categories. Ontological bagging classification can also be used to categorize fruits into their respective classes in situations such as supermarkets and factories. In the future, it is recommended to employ high-resolution networks (HRNets) as an alternative to Xception, VGG16, and Resnet50. In fact, because of their ability to convert low-resolution representation to high-level representation, which is associated with efficient block architectures developed according to new standards, they are excellent for vision tasks, such as feature extraction, semantic segmentation, and object detection [40].  
Introduction and related work, J.V.F.-D. Model design, M.G. Implementation and discussion section, C.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available on request from the corresponding author ([PERSON]). The forest image dataset that was generated through the data augmentation process was obtained from [30; 31]. The authors confirm that the data supporting the findings of this study are available within the article.  
The authors thank the University of KwaZulu Natal for providing financial assistance in accessing all resources and tools required to undertake this study.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.