# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 4 Results and Discussion

Table 5: Summary after the SMOTE. Figure 8: After the SMOTE. A comparison of all of the classifiers by considering the accuracy rate as a prime parameter before and after the SMOTE is shown in Table 6.  
In the above-mentioned Tables 4 and 5, we observed some of the key points, and they are shown below. The performance parameter recall is improved after applying the SMOTE, which signifies that the performance metrics performed well in identifying minority class instances. It has been observed that some of the classifier performance metrics, especially the F1-score, and specificity decrease leading to potential misclassifications. However most of the cases accuracy of the performance parameter remains stable. The (SVCs) performance about the accuracy shows only slight changes. Similarly, the performance metrics and ROC AUC values show minor fluctuations. This indicates that the classifiers can distinguish between the remaining classes, which exhibit consistent characteristics. Here are minimal fluctuations observed in performance before and after applying SMOTE. Our objective was to handle the imbalanced dataset using the SMOTE, which improves the recall for most of the classifiers. It was also observed that some of the classifier's performance had a high precision and recall but after the SMOTE, it became stable. The reason behind this is that after the SMOTE, the performance increased, i.e., its ability to detect the minority class instances that reduce the bias towards the majority class, which enhances the balanced performance in terms of measurement. The model performances are enhanced so that the minority classes are recognized, which is an important factor for crop recommendations. Those classifier performance metrics (precision and recall in most cases) are high before applying the SMOTE because of the data imbalance nature. After using the SMOTE, we found overall performance improvement, which signifies that those classifiers are better suggested for crop recommendation in different conditions. In the polar graph shown in Figure 9, it has been observed that the classifier LR and accuracy remain the same before and after some techniques, i.e., 76%. The obtained result does not impact performance. Similarly, the same effect on the DT and the obtained result is 95%. In KNN, the performance decreased after implementing the SMOTE, demonstrating that the SMOTE technology produces a negative impact on the K-NN classifier. In the same way as the SVC, the performance slightly decreased from 1.00 to 0.74.RF. In the SGDC, these classifiers increased after the SMOTE. However, some of the classifiers have performances that decreased after the SMOTE, like Ada Boost, Cat Boost, and MNV. It has been also observed that some of the classifier's performance remains the same before the SMOTE and after the SMOTE, and these are Bagged Tree, HGB, etc. The provided radar graph \begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier Name** & **Accuracy before the SMOTE** & **Accuracy after the SMOTE** \\ \hline Logistic Regression & 0.76 & 0.76 \\ \hline Decision Tree & 0.95 & 0.95 \\ \hline K-Neighbors & 0.95 & 0.94 \\ \hline SVC & 1.00 & 0.74 \\ \hline Random Forest & 0.94 & 0.95 \\ \hline Gradient Boosting & 0.95 & 0.94 \\ \hline Bagged Tree & 0.96 & 0.96 \\ \hline XGB Classifier & 0.97 & 0.97 \\ \hline Ada Boost Classifier & 0.92 & 0.90 \\ \hline Cat Boost & 0.98 & 0.96 \\ \hline HGB & 0.96 & 0.96 \\ \hline SGDC & 0.95 & 1.00 \\ \hline MNB & 0.97 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison based on the accuracy rate. demonstrates that the classifier RF and SGDC provide a positive impact and no impact (logistic regression, decision tree, Bagged Tree, XGB Classifier, HGB), as well as a negative impact (K-Neighbors, SVC, Gradient Boosting, Ada Boost Classifier, Cat Boost, MNV), on the classifiers. The classifier SGDC improved performance, and the SVC provided a negative impact. Figure 10 demonstrates the ROC curve applied before the SMOTE and after the SMOTE.