Prediction of Carbon Dioxide Concentrations in Strawberry Greenhouse by Using Time Series Models

Seung Hyun Shin, Nibas Chandra Deb, Elanchezhian Arulmozhi, Niraj Tamrakar, Oluwasegun Moses Ogundele, Junghoo Kook, Dae Hyun Kim and Hyeon Tae Kim ()
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Seung Hyun Shin: Department of Smart Farm, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea
Nibas Chandra Deb: Department of Biosystems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea
Elanchezhian Arulmozhi: Department of Biosystems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea
Niraj Tamrakar: Department of Biosystems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea
Oluwasegun Moses Ogundele: Department of Biosystems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea
Junghoo Kook: Department of Smart Farm, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea
Dae Hyun Kim: Department of Biosystems Engineering, Kangwon National University, Chuncheon 24341, Republic of Korea
Hyeon Tae Kim: Department of Biosystems Engineering, Institute of Smart Farm, Gyeongsang National University, Jinju 52828, Republic of Korea

Agriculture, 2024, vol. 14, issue 11, 1-23

Abstract: Carbon dioxide (CO 2 ) concentrations play an important role in plant production, as they have a direct impact on both plant growth and yield. Therefore, the objectives of this study were to predict CO 2 concentrations in the greenhouse by applying time series models using five datasets. To estimate the CO 2 concentrations, this study was conducted over a four-month period from 1 December 2023 to 31 March 2024, in a strawberry-cultivating greenhouse. Fifteen sensors (MCH-383SD, Lutron, Taiwan) were installed inside the greenhouse to measure CO 2 concentration at 1-min intervals. Finally, the dataset was transformed into intervals of 1, 5, 10, 30, and 60 min. The time-series data were analyzed using the autoregressive integrated moving average (ARIMA) and the Prophet Forecasting Model (PFM), with performance assessed through root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2 ). The evaluation indicated that the best model performance was achieved with data collected at 1-min intervals, while model performance declined with longer intervals, with the lowest performance observed at 60-min intervals. Specifically, the ARIMA model outperformed across all data collection intervals while comparing with the PFM. The ARIMA model, with data collected at 1-min intervals, achieved an R 2 of 0.928, RMSE of 7.359, and MAE of 2.832. However, both ARIMA and PFM exhibited poorer performances as the interval of data collection increased, with the lowest performance at 60-min intervals where ARIMA had an R 2 of 0.762, RMSE of 19.469, and MAE of 11.48. This research underscores the importance of frequent data collection for precise environmental control in greenhouse agriculture, emphasizing the critical role of short-interval data collection for accurate predictive modeling.

Keywords: ARIMA model; carbon dioxide; Prophet Forecasting Model; strawberry (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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