The third generation smart farm represents an intelligent robot farm with the capability of autonomously managing the entire cultivation process. This includes complex energy management and various robotic farm operations, all accomplished without human intervention. This approach is anticipated as a strategy to address the current rural challenges of aging and decreasing populations, offering the potential to overcome these issues [12]. However, a report, _The Analysis of the Status and Future Development of Smart Farming Projects_, released by the Korean National Assembly Budget Office in 2022, presents a breakdown of the proportion of Korean smart farms in 2020. According to the analysis, the  
Figure 1: Adoption of Smart Farm in South Korea. first generation of smart farms makes up 84.2% of the total, whereas the second generation comprises 15.8% [13]. It is evident that the first generation, characterized by relatively low technological levels, is primarily distributed in small-scale operations, and the utilization of advanced technologies such as artificial intelligence, big data, and cloud computing remains limited. Therefore, in this paper, we conducted a study in a greenhouse where paprika was grown hydroponically in the first generation of smart farms. The paprika (_Capsicum annum_ L.) cultivated in greenhouses is classified under the Solanaceae family and is considered an economically important crop in most regions of the world [14]. Paprika plants prefer mild climates and are sensitive to high air temperatures and intense sunlight, with the optimal air temperature for their growth being approximately 20-25 \({}^{\circ}\)C [15]. In regions with distinct seasons like South Korea, traditional cultivation practices have historically deemed paprika cultivation unsuitable. However, with the advancement of agricultural technology, various methods such as greenhouses, hydroponics, aquaponics, and aeroponics have made it possible to cultivate special crops like paprika [16; 17]. This technological introduction has led to a significant increase in paprika production in South Korea, from 7500 tons in 2000 to approximately 78,000 tons in 2017, making it one of the major export vegetables in the horticultural industry. It has also become a significant import commodity in the Japanese paprika market [18; 19]. Nonetheless, these specialty crops are highly sensitive to environmental changes, and unexpected shifts in their growth environment can lead to hindered growth, pest outbreaks, and decreased yields. Furthermore, recent abrupt climate changes have caused extensive agricultural damage worldwide [20; 21]. In response, recent research aims to enhance productivity by modernizing agricultural practices and analyzing and understanding the growth environment. In this paper, we aim to accurately predict environmental factors that impact crop growth in the greenhouse where paprika is cultivated in a similar vein to recent work. To achieve this goal, we established a testbed in the greenhouse for paprika cultivation and collected sensor data. Following that, we conducted kernel density estimation analysis, data analysis, and correlation heatmap analysis based on the collected data. Subsequently, we utilized LSTM, BI-LSTM, and GRU as base models to construct a stacking ensemble model. To assess the performance of the proposed model based on the analyzed results, we utilized LSTM, BI-LSTM, and GRU as the existing models for predicting one of the sensor data variables, air temperature. This research signifies a transition from traditional agricultural methods relying on experience to a modern approach employing data analysis and artificial intelligence. It lays the foundation for moving from the first generation of smart farms to the second generation, making it a valuable reference in the field of agriculture.