## 2 Materials and Methods
### Training Strategy and Hyperparameter Settings  
In this study, the batch size was set to 32 and the number of epochs to 10. According to previous studies, extensive experiments on various datasets such as CIFAR-10, CIFAR-100, and ImageNet have shown that optimal performance--in terms of training stability, convergence speed, and test accuracy--is often achieved with batch sizes of 32 or less, and sometimes even with very small batch sizes such as 2 or 4. These findings indicate that smaller batch sizes can capture the most recent gradient information, thereby reducing the variance in weight updates and providing a more stable learning rate range [39].  
For SGD (Stochastic gradient descent)-based optimization, an initial learning rate of 0.01 has been shown to facilitate stable convergence without significant fluctuations, particularly on small datasets [40]. Therefore, in our experiments, the learning rate was initially set to 0.01 and then reduced by a factor of 10 using a step decay method every 3 epochs, ensuring stable convergence as the number of epochs increased. The step decay method effectively reduces the noise in SGD and promotes stable convergence due to its simplicity and clear decay intervals [41].  
As previously mentioned, the dataset of original images shown in Figure 2 was divided into three datasets: training, validation, and test sets. Subsequently, the model was trained using the training dataset and validated on the validation dataset, with the changes in the loss function and accuracy visualized over the epochs. Finally, the model corresponding to the highest validation accuracy was saved, and its test accuracy was measured.  
Figure 3: Workflow of the CNN-based growth stage classification model for butterhead lettuce using transfer learning and cross-domain evaluation. The top section (outlined in red) illustrates the fine-tuning process of a pre-trained feature extractor with a new classifier for growth stage classification. The bottom section (outlined in green) represents the cross-domain evaluation, where Model 1 and Model 2 are tested on different datasets to analyze their generalization performance across original and GrabCut–Watershed preprocessed images.  
Next, using the GrabCut-Watershed images shown in Figure 2, we replicated the same experimental procedure previously applied to the original plant images to build the growth stage classification model (Model 2). We then saved the model parameters that achieved the highest validation accuracy and measured the corresponding test accuracy. Subsequently, we tested the GrabCut-Watershed image test set on Model 1 (which was saved at its highest validation accuracy from the original image experiments) and tested the original image test set on Model 2 (which was saved at its highest validation accuracy from the GrabCut image experiments) to evaluate and compare their classification accuracies (Figure 3; Cross-Domain Evaluation). Since the two test sets--the one from the original images and the other from the GrabCut-Watershed images--are composed differently, the results of the cross-domain evaluation are more reliable in a practically random situation.