# A Novel Tool for Biodiversity Studies: Earthworm Classification via NGS and Neural Networks
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithm** & **Class** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F-Measure** & **MCC** & **ROC** & **PRC** \\ \hline \multirow{4}{*}{Naive Bayes (Bayes)} & _Eisenia fetida_ & 0.967 & 0.000 & 1.000 & 0.967 & 0.983 & 0.975 & 0.999 & 0.999 \\ \cline{2-10}  & _Dendrobaena_ & 1.000 & 0.017 & 0.968 & 1.000 & 0.984 & 0.976 & 0.999 & 0.999 \\ \cline{2-10}  & _Lumbricus terrestris_ & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline \multirow{4}{*}{Logistic (Functions)} & _Eisenia fetida_ & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \cline{2-10}  & _Dendrobaena_ & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \cline{2-10}  & _Lumbricus terrestris_ & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline \multirow{4}{*}{Simple Logistic (Functions)} & _Eisenia fetida_ & 0.933 & 0.017 & 0.966 & 0.933 & 0.949 & 0.925 & 0.997 & 0.993 \\ \cline{2-10}  & _Dendrobaena_ & 0.967 & 0.033 & 0.935 & 0.967 & 0.951 & 0.926 & 0.995 & 0.991 \\ \cline{2-10}  & _Lumbricus terrestris_ & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline \multirow{4}{*}{KStar (Lazy)} & _Eisenia fetida_ & 0.967 & 0.033 & 0.935 & 0.967 & 0.951 & 0.926 & 0.999 & 0.998 \\ \cline{2-10}  & _Dendrobaena_ & 0.933 & 0.050 & 0.903 & 0.933 & 0.918 & 0.876 & 0.988 & 0.960 \\ \cline{2-10}  & _Lumbricus terrestris_ & 0.933 & 0.000 & 1.000 & 0.933 & 0.966 & 0.950 & 0.999 & 0.998 \\ \hline \multirow{4}{*}{Random Forest (Trees)} & _Eisenia fetida_ & 1.000 & 0.017 & 0.968 & 1.000 & 0.984 & 0.976 & 1.000 & 1.000 \\ \cline{2-10}  & _Dendrobaena_ & 0.967 & 0.000 & 1.000 & 0.967 & 0.983 & 0.975 & 0.999 & 0.999 \\ \cline{2-10}  & _Lumbricus terrestris_ & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline \hline \end{tabular}  
* TP Rate—True Positive Rate, FP Rate—False Positive Rate, MCC—Matthews Correlation Coefficient, ROC Area—Receiver Operating Characteristic Area, PRC Area—Precision-Recall Area.  
\end{table} 
Table 2: The performance metrics of earthworm classification based on selected image textures using different machine learning algorithms. Filtered sequence reads were assigned to Operational Taxonomic Units (OTUs) by CCMetagen (Table 3). There were 279 reads obtained, 266 (95.3%) of which were assigned to _E. ftiida_. St 94-95% of identity. The rest of the reads were assigned to Hexaplex trunculus and Urechis unicinctus (7 and 6, respectively). The earthworm taxonomic identification is important for the assessment of earthworm biodiversity. Earthworm species diversity can be considered a common measure characterizing their populations and potential functions in the soil [53]. However, despite the importance of earthworms for ecosystems, there are difficulties in earthworm identification. The limitations of procedures hinder progress in the ecological and community-based research on earthworms, as species identification and classification can be the basis of ecological studies [54]. [PERSON] et al. [55] developed a non-destructive approach to phenotyping of earthworms using image analysis. A mathematical model to compute the length, surface area, and volume, and identify the anus-end, mouth-end, and clitellum of earthworms was proposed [55]. The taxonomic classification of earthworms can still be established using  
Figure 4: The confusion matrices of earthworm classification using the model built based on selected image textures using SVM (**a**), KNN (**b**), Ensemble (**c**), and Narrow Neural Network (**d**). morphological characteristics, e.g., shape, position, and segment number of clitellum, the arrangements of setae, spermathecae, and prostomium shape. However, the identification procedure requires qualified experts and is difficult for most of the species due to the simple and small earthworm body. Whereas molecular techniques such as 18S rDNA, 16S rDNA, and COI sequences can be successfully used for the classification of earthworm species.