## 4 Conclusions  
This study developed a CNN-based transfer learning model for butterhead lettuce (_Lactuca sativa_ L.) growth stage classification applicable to industrial plant factories, and analyzed the impact of GrabCut-Watershed preprocessing on model performance and inference efficiency. To achieve this, models were trained on both raw and GrabCut-Watershed preprocessed images, and their classification performance was compared. Additionally, cross-classification experiments were conducted to evaluate the adaptability of models trained on different image types. The results showed that models trained and tested within the same domain achieved high classification accuracy (-98-99%), confirming the potential of CNN-based transfer learning for practical applications in industrial plant factories. However, cross-domain evaluations revealed a significant performance drop, with the raw image-trained model achieving 82.77% accuracy on GrabCut-Watershed images, while the GrabCut-Watershed-trained model achieved only 34.15% accuracy on raw images. These discrepancies were further substantiated by class-wise metrics, where Model 2-CDE showed complete misclassification in Week1 and Week2 (F1 = 0), in contrast to Model 1-CDE, which retained balanced performance across classes. Visual interpretability analysis using Grad-CAM revealed that raw image-trained models consistently attended to meaningful plant structures such as leaf centers and contours, whereas models trained on segmented images often misdirected attention toward background artifacts in unseen domains. These findings highlight the importance of domain consistency and rich visual diversity in training data for robust generalization. These performance trends were also reflected in inference efficiency. Although Model 2 showed the fastest model-only inference time (0.037 s/image), this measurement excludes the time-consuming GrabCut-Watershed preprocessing step, which would significantly increase overall latency in practice. In contrast, Model 1 required 0.055 s/image without any additional preprocessing, making it more favorable for real-time or near-real-time applications. Notably, Model 1-CDE achieved the fastest overall inference speed (0.040 s/image) while maintaining reasonable accuracy, whereas Model 2-CDE was both the slowest (0.053 s/image) and the least accurate. These results emphasize not only the superior generalization of raw image-based models but also their computational efficiency, making them suitable for real-time deployment in cross-domain settings. From an industrial perspective, inference speeds in the range of 0.04 to 0.06 s per image are generally acceptable for systems where image acquisition occurs at several-second or minute-level intervals, such as automated nutrient control or environmental monitoring. These findings support the practical deployment of raw image-based models in time-sensitive, vision-driven plant factory systems. Future research should focus on optimizing preprocessing techniques to retain essential plant features while minimizing processing time, broadening the diversity of training data through broader environmental sampling, and applying robust data augmentation strategies to enhance model generalization. Additionally, adopting lightweight CNN architectures such as MobileNet or EfficientNet-Lite may further reduce inference time and facilitate real-time deployment in automated crop monitoring systems. Overall, this study provides practical insights into the implementation of CNN-based growth stage classification models in industrial plant factories and contributes to the advancement of smart agriculture and intelligent crop management technologies. Conceptualization, J.-S.G.K. ; methodology, J.-S.G.K. and M.K. ; software, J.-S.G.K. and M.K. ; validation, J.-S.G.K. and M.K. ; formal analysis, J.-S.G.K. ; investigation, J.-S.G.K. ; resources, M.K. and S.C.; data curation, J.-S.G.K. and S.C.; writing--original draft preparation,J.-S.G.K., M.K., J.S. and S.H.S. ; writing--review and editing, J.-S.G.K., M.K., J.S., S.H.S.