Jang Kyeong Min, Lee Myeong Bae, Lim Jong Hyun, Oh Han Byeol, Shin Chang Sun, Park Jang Woo

KIPS Transactions on Software and Data Engineering, Vol. 12, No. 3, pp. 125-132, Mar. 2023
https://doi.org/10.3745/KTSDE.2023.12.3.125,   PDF Download:
Keywords: Machine Learning, Vapor Pressure Deficit(VPD), Prediction Model, Smart Farm, Light Gradient Boosting Machine(LGBM), RandomForest
Abstract

In this study, we compared the performance of machine learning models for predicting Vapor Pressure Deficits (VPD) in greenhouses that affect pore function and photosynthesis as well as plant growth due to nutrient absorption of plants. For VPD prediction, the correlation between the environmental elements in and outside the greenhouse and the temporal elements of the time series data was confirmed, and how the highly correlated elements affect VPD was confirmed. Before analyzing the performance of the prediction model, the amount and interval of analysis time series data (1 day, 3 days, 7 days) and interval (20 minutes, 1 hour) were checked to adjust the amount and interval of data. Finally, four machine learning prediction models (XGB Regressor, LGBM Regressor, Random Forest Regressor, etc.) were applied to compare the prediction performance by model. As a result of the prediction of the model, when data of 1 day at 20 minute intervals were used, the highest prediction performance was 0.008 for MAE and 0.011 for RMSE in LGBM. In addition, it was confirmed that the factor that most influences VPD prediction after 20 minutes was VPD (VPD_y__71) from the past 20 minutes rather than environmental factors. Using the results of this study, it is possible to increase crop productivity through VPD prediction, condensation of greenhouses, and prevention of disease occurrence. In the future, it can be used not only in predicting environmental data of greenhouses, but also in various fields such as production prediction and smart farm control models.

Statistics
Cite this article