Lee, Junseo Im, Seongil Jeong, Jae-Seung Lee, Taek Sung Park, Soo Hyun Shin, Changhwan Ju, Hyunsu Kim, Hyung-Jun 2025-04-25T06:00:19Z 2025-04-25T06:00:19Z 2025-04-25 2025-08 0168-1699 https://pubs.kist.re.kr/handle/201004/152297 Climate change poses a significant threat to agricultural sustainability and food security. Automated greenhouse systems, which provide stable and controlled environments for crop cultivation, have emerged as a promising solution. However, traditional rule-based greenhouse control algorithms struggle to determine optimal control variables due to the complex relationships between environmental variables. In response, we propose a Transformer-based model, Trans-Farmer, which predicts the control variables by considering the complex interactions among environmental variables. Trans-Farmer leverages the attention mechanism to learn the intricate relationships among the environmental variables. The encoder-decoder structure enables the translation of the environmental variables into the corresponding control variables, analogous to language translation. Experimental results demonstrate that Trans-Farmer outperforms baseline models across all the evaluation metrics, achieving superior accuracy and predictive performance. The attention maps of the encoder visualize how Trans-Farmer comprehends the complex interactions among the environmental variables. Additionally, the compact size of Trans-Farmer is suitable for application in general greenhouses with constrained microcontroller units. This approach contributes to the development of automated greenhouse management systems and emphasizes the potential of artificial intelligence applications in agriculture. English Elsevier BV Learning hidden relationship between environment and control variables for direct control of automated greenhouse using Transformer-based model Article 10.1016/j.compag.2025.110335 1 Computers and Electronics in Agriculture, v.235 Computers and Electronics in Agriculture 235 Y scie scopus 001458892500001 2-s2.0-105000729069 Agriculture, Multidisciplinary Computer Science, Interdisciplinary Applications Agriculture Computer Science Article Automated Greenhouse Deep Learning Transformer Control variable Language Translation