## 2 Materials and Methods  
### Dataset and Image Preprocessing  
The experimental data used in this study consist of butterhead lettuce images collected over approximately two months, from 23 September 2022, to 26 November 2022. These images were acquired and selected separately during three cultivation cycles using the data acquisition system from previous research [13] in an industrial plant factory (Figure 1). Since the optimal range of environmental conditions for butterhead lettuce are 18-25 \({}^{\circ}\)C for temperature, 60-80% for relative humidity, and 788-917 ppm for carbon dioxide concentration, the industrial plant factory maintained conditions of 21.2 \(\pm\) 3 \({}^{\circ}\)C, 75.6 \(\pm\) 15%, and 827 ppm on average, respectively. The original images used in this study (Figure 2) are RGB images captured under normal lighting conditions of the industrial plant factory. To define the growth stages of butterhead lettuce for image classification, this study followedthe temporal categorization period into weekly intervals. Importantly, Week 1 was defined as the first week after transplanting, which includes the early nursery phase. This labeling scheme is consistent with previous studies on lettuce growth monitoring using machine vision and deep learning models. As a previous study divided growth development into three stages during the cultivation period [32], the plant growth stages were classified into three categories based on the time of capture: **Stage 1** for the first week, **Stage 2** for the second week, and **Stage 3** for the third week. Each cultivation cycle lasted three weeks. The dataset comprises a total of 476 original images, including **163 images in Stage 1, 160 images in Stage 2, and 153 images in Stage 3.**  
In addition, for each growth stage of the original images shown in Figure 2, butterhead images preprocessed using the GrabCut-Watershed algorithm were also employed in the experiments for efficient manual segmentation. The GrabCut-Watershed was applied to each original image using Python's OpenCV library, and similarly, the GrabCut-Watershed images were classified into three stages, as depicted in Figure 2. The GrabCut-Watershed image dataset comprised a total of 445 images, with 139 images in growth stage 1, 171 images in growth stage 2, and 135 images in growth stage 3 (Table 1). Some images were excluded due to incomplete edge preprocessing caused by overlapping neighboring leaves.  
In this study, GrabCut-Watershed was selected as the segmentation approach to generate preprocessed image datasets for model comparison. Given the limited dataset size and the overlapping nature of lettuce leaves, fully automated deep learning-based segmentation models such as U-Net or DeepLabV3+ were intentionally excluded due to their high annotation cost and relatively poor boundary precision under these conditions. In contrast, the GrabCut-Watershed method allowed targeted, user-guided object separation with better control over morphological detail. This choice enabled a controlled investigation of how segmentation affects classification performance, without the added complexity of training a separate segmentation network.  
Figure 1: Environment (**a**), data acquisition system (**b**), and cameras on the rail (**c**) for unstructured data collection in an industrial plant factory.