## 4 Results and Discussion
### Experimental Results  
The classification reports of the hybrid models are presented in Table 3. The proposed TL+SVM and TL+LDA hybrid models demonstrated remarkable results, achieving high test accuracy rates of 94.4% and 94%, respectively. In the training set, the TL+LDA model provided the highest classification accuracy at 94.8%. Considering seed purity as the main target, the most successful model is the TL+LDA model (precision of 94.9%) on the training data set and the TL+SVM model (precision of 94.4%) on the test set. The TL+KNN model achieved high precision (89.6%), recall (89.6%), F1-score (89.5%), and accuracy (89.5). The weighted precision of the TL+NB model is 86.5% and that of TL+KNN is 89.6%. As can be observed, the TL+SVM and TL+LDA models outperformed the other hybrid models in terms of training and test accuracy rates. Figure 4 presents four hybrid models' \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Parameter(s)** \\ \hline KNN & Distance: Euclidean \\  & Distance Weight: SquaredInverse \\  & Number of Neighbors: 10 \\  & Standardize: true \\ SVM & Kernel Function: polynomial \\  & Polynomial Order: 2 \\  & Kernel Scale: auto \\  & Box Constraint: 1 \\  & Coding: onevsone \\  & Standardize: true \\ NB & Distribution Names: kernel \\ LDA & Gamma: 0 \\  & Discrim Type: Linear \\  & Fill Coeffs: off \\ ReliefF & \(k=10\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters used in the models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Weighted**} & \multicolumn{2}{c}{**Weighted**} & \multicolumn{2}{c}{**Weighted**} \\  & & \multicolumn{2}{c}{**F1-score**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} \\ \cline{2-9}
**Model** & **Training** & **Test** & **Training** & **Test** & **Training** & **Test** & **Training** & **Test** \\ \hline TL+KNN & 0.9007 & 0.8951 & 0.9012 & 0.8950 & 0.9035 & 0.8959 & 0.9007 & 0.8951 \\ TL+SVM & 0.9356 & 0.9438 & 0.9360 & 0.9438 & 0.9365 & 0.9442 & 0.9356 & 0.9438 \\ TL+NB & 0.8453 & 0.8653 & 0.8445 & 0.8644 & 0.8449 & 0.8651 & 0.8453 & 0.8653 \\ TL+LDA & 0.9476 & 0.9403 & 0.9480 & 0.9407 & 0.9488 & 0.9415 & 0.9476 & 0.9403 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The training and test results of the hybrid models. performance on training and test data in terms of AUC values. In the training phase, the AUC value of TL+SVM is the highest, reaching about 0.997, followed by TL+LDA (0.995), TL+KNN (0.9898), and TL+NB (0.946). In the test phase, again, the highest AUC value was produced by TL+SVM (about 0.998) and the lowest AUC value by TL+NB (0.952). These results indicate that TL+SVM has the highest predictive ability. Tables 4 and 5 provide a detailed class-by-class analysis of the other performance metrics. The TL+SVM model obtained better classification results for all the varieties of chickpea seeds compared to the TL+LDA model. Hisar exhibits nearly perfect precision, recall, F1-score, and AUC values for these two models. Akca, Inci, and Aslanbey also stand out with high performance across every metric. TL+SVM exhibited considerably high training and test AUC values for the Hisar and Inci classes. The Akca class demonstrated a higher level of performance in terms of AUC. These results also show that both models have low AUC (0.9854 and above) for Nihatbey. Ciftci and Nihatbey have relatively lower precision and recall compared to the other classes. This may involve examining the data distribution, feature selection, and also further model tuning.