## 1 Introduction  
Concerns about global food production are increasing due to population growth, climate change, and the reduction in arable land [1,2]. By 2050, the global population is expected to reach approximately 9.7 billion. As urbanization and climate change accelerate the decline of agricultural land, the issue of food shortages is expected to worsen [3,4]. To address these challenges, Controlled-Environment Agriculture (CEA) has emerged as a promising alternative to traditional open-field farming. Among CEA technologies, plant factories are automated crop production systems that precisely regulate environmental factors such as light, temperature, humidity, CO\({}_{2}\) concentration, and nutrients [5,6]. Plant factories are considered an effective solution to the challenges of climate change and land reduction, as they allow for year-round crop production in urban areas while improving food self-sufficiency and conserving resources [7,8]. Furthermore, plant factories provide precise environmental control, ensuring high reproducibility in experiments and reliable data collection. While previous studies have mainly been conducted in small-scale laboratory environments, this study aims to enhance practical applicability by utilizing empirical data collected from industrial-scale plant factories [9,10]. Accurate prediction of crop growth stages is essential for harvest scheduling, disease prevention, and quality control [11,12]. In CEA environments, integrating growth stage classification models with automated systems enables real-time, precise growth monitoring, significantly improving crop management efficiency. Previous studies have primarily focused on biomass, yield, and leaf area index prediction [13,14,15], whereas growth stage classification remains relatively underexplored. In particular, biomass prediction models require continuous numerical outputs, making them less intuitive for direct decision-making in industrial environments, and data collection can be complex and impractical for real-world plant factories. Conversely, CNN (Convolutional Neural Network)-based supervised learning models can directly process plant images, learning morphological characteristics and enabling more precise and consistent growth stage classification [16,17]. CNN-based classification models offer clear categorical outputs, facilitating interpretation and seamless integration with automated environmental control systems. Additionally, CNN models can extract growth stage-related morphological features (e.g., leaf size, structure) and maintain stable performance across different environments, making them more suitable for industrial applications than biomass prediction models. This study selects butterhead lettuce (_Lactuca sativa_ L.) as the target crop due to its short growth cycle (approximately 45-55 days) and relatively low space requirements, making it ideal for capturing images across multiple growth stages [13,18,19]. Furthermore, butterhead lettuce is a highly consumed salad vegetable with increasing global demand, adding to its research value. Previous studies have explored non-destructive weight estimation, classification of lettuce varieties, and automated quality grading systems [13,20,21]. Building on these findings, this study aims to develop a CNN-based growth stage classification model to optimize its applicability in smart agriculture and industrial environments. Image preprocessing is a crucial factor affecting the performance of CNN-based plant growth classification models. The GrabCut algorithm is widely used for background removal due to its high computational efficiency and accurate object segmentation capabilities [22,23]. However, it requires manual Region of Interest (ROI) selection and struggles with complex backgrounds, which can degrade performance in uncontrolled environments [24,25]. To overcome these limitations, hybrid approaches combining GrabCutwith other segmentation techniques have been studied.