## 3 Experiments and Results
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Methods** & **AUC** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{3}{*}{Scenario 1} & SVM & 0.87 & 0.87 & 0.87 & 0.87 \\  & DT & 0.87 & 0.87 & 0.87 & 0.87 \\  & RF & **0.95** & **0.91** & **0.95** & **0.93** \\ \hline \multirow{3}{*}{Scenario 2} & SVM & 0.73 & 0.74 & 0.73 & 0.72 \\  & DT & 0.66 & 0.67 & 0.66 & 0.66 \\  & RF & **0.83** & **0.84** & **0.83** & **0.82** \\ \hline \multirow{3}{*}{Scenario 3} & SVM & 0.57 & 0.70 & 0.57 & 0.63 \\  & DT & 0.60 & 0.56 & 0.60 & 0.58 \\ \cline{1-1}  & RF & **0.75** & **0.76** & **0.75** & **0.75** \\ \hline \end{tabular}
\end{table} 
Table 3: Performance of the evaluated ML classification methods in the estimation of locust presence without applying SMOTE (Scenarios 1, 2 and 3). observed for Scenarios 2 and 3. The SVM goes from an F-score value of 0.72 in Scenario 2, without applying SMOTE, to a value of 0.73 when the SMOTE technique is used. Likewise, in Scenario 3, for the SVM, we go from an F-score value of 0.56 without SMOTE to 0.57 with SMOTE. The same occurs with the DT algorithm, with an F-score value of 0.66 in Scenario 2 without SMOTE and 0.67 with SMOTE. The same situation is found for Scenario 3, going from an F-score value without SMOTE of 0.56 to a value of 0.60 with SMOTE. The cause of this improvement in the ML algorithms seems to be due to these learning machines requiring a large number of samples, as well as other more descriptive predictive variables. By applying the SMOTE technique, we overcome in part the issue of the small number of samples, adding other synthetic ones that contribute to better learning in the algorithms. Other descriptive variables could be used to improve the results in Scenario 1, both for the SVM and DT algorithms, because in this case we have a sufficient number of samples applying the SMOTE technique, but it is not enough to obtain a better performance. Once we are able to determine the presence or absence of locusts, it would be of great interest to predict the number of individuals at each sighting location. For this reason, we combine the classification part of this paper with the prediction of the number of locusts, using different ML regression algorithms. The ones used in this study are LR, SVR, DT, RF and MLP. Table 5 shows the results obtained for each of these machines for the different scenarios considered. It is possible to see that the RF algorithm is again the approach which obtains the best result in this regression problem, improving the results of the other ML approaches in all metrics considered. Important improvements in terms of accuracy are obtained by applying the RF approach versus LR, which is the worst algorithm tested in all the scenarios considered. In Scenario 1, the second-best approach is SVR, close to the result obtained by the RF algorithm. The third and fourth approaches are MLP and DT, respectively. In Scenario 2, the second-best approach is MLP, then DF and the SVR. In Scenario 3, DT obtains better result in terms of MAE and MAER, though RF is better in terms of MSE, MSER and \(R^{2}\). The third-best approach is SVR in this case, and the fourth is MLP. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Methods** & **AUC** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{3}{*}{Scenario 1} & SVM & 0.88 & 0.83 & 0.87 & 0.85 \\  & DT & 0.86 & 0.83 & 0.86 & 0.84 \\  & RF & **0.92** & **0.89** & **0.92** & **0.90** \\ \hline \multirow{3}{*}{Scenario 2} & SVM & 0.73 & 0.73 & 0.73 & 0.73 \\  & DT & 0.67 & 0.68 & 0.67 & 0.67 \\  & RF & **0.81** & **0.82** & **0.81** & **0.81** \\ \hline \multirow{3}{*}{Scenario 3} & SVM & 0.66 & 0.61 & 0.67 & 0.64 \\  & DT & 0.69 & 0.63 & 0.69 & 0.66 \\ \cline{1-1}  & RF & **0.85** & **0.77** & **0.85** & **0.81** \\ \hline \end{tabular}
\end{table} 
Table 4: Performance of the evaluated ML classification methods in the estimation of locust presence when applying SMOTE (Scenarios 1, 2 and 3).