## 3 Results and Discussion
### Classification Error Patterns: Confusion Matrix Analysis  
Figure 5 presents the confusion matrices for all four evaluation scenarios, illustrating how each model predicted the three growth stages (Week1, Week2, Week3).  
In the same-domain settings, Model 1 (Figure 5a) and Model 2 (Figure 5b) showed strong diagonal dominance, indicating excellent classification performance. Model 1 achieved near-perfect predictions across all classes, with only minimal misclassification (e.g., one sample in Week1 misclassified as Week2). Model 2 also demonstrated high precision and recall,though a slightly larger number of misclassifications occurred, particularly for Week1 samples misclassified as Week2.  
In contrast, cross-domain evaluations revealed notable degradation in performance. Model 1-CDE (Figure 5c) retained a moderate level of accuracy, especially in predicting Week1 and Week3, although Week2 exhibited more confusion, being misclassified across adjacent stages. Model 2-CDE (Figure 5d) exhibited severe degradation, with the model predicting all samples as Week3 regardless of the actual class. This resulted in a complete failure to distinguish among stages and reflects the model's inability to generalize beyond the domain it was trained on.  
These results reinforce the importance of domain consistency in training and deployment. They also demonstrate that while segmentation-based preprocessing may simplify visual features and aid within-domain accuracy (as seen in Model 2), it may hinder generalization when real-world variability is introduced (as in Model 2-CDE).  
In addition to the confusion matrices, detailed performance metrics for each growth stage are provided in Table 3. These detailed metrics offer a more comprehensive view of model behavior beyond overall accuracy, highlighting differences in prediction reliability by class. In the same-domain setting, both Model 1 and Model 2 demonstrated strong performance (F1-scores > 0.97), with Model 1 achieving perfect scores for Week1 and F1-scores close to 1.000 for Week2 and Week3, confirming its robustness and consistency. Model 2 achieved F1-scores of 0.971 and 0.975 for Week2 and Week3, respectively.  
Cross-domain evaluations, however, revealed significant divergence: Model 1-CDE maintained moderate performance (F1-scores ranging from 0.857 to 0.871) with reason  
Figure 5: Confusion matrices for the growth stage classification models under four training-testing scenarios: (**a**) Model 1 (trained and tested on original images), (**b**) Model 2 (trained and tested on GrabCut-Watershed preprocessed images), (**c**) Model 1-CDE (cross-domain evaluation: trained on original images, tested on preprocessed images), and (**d**) Model 2-CDE (cross-domain evaluation: trained on preprocessed images, tested on original images).  
able class-wise balance, while Model 2-CDE entirely failed to classify Week1 and Week2 (precision and recall = 0) and only partially identified Week3 (F1 = 0.510). This collapse in classification confirms the poor generalizability of models trained exclusively on segmented inputs when applied to unsegmented, real-world images--a pattern consistently observed in the confusion matrices (Figure 5) and Grad-CAM visualization (Figure 6). These findings underscore the importance of domain consistency and support the conclusion that models trained on original images provide more stable and transferable performance across diverse input conditions.