Jung, Dae-Hyun Lee, Taek Sung Kim, KangGeon Park, Soo Hyun 2024-01-19T11:03:47Z 2024-01-19T11:03:47Z 2022-10-11 2022-09 https://pubs.kist.re.kr/handle/201004/114571 The greenhouse industry achieves stable agricultural production worldwide. Various information and communication technology techniques to model and control the environment have been applied as data from environmental sensors and actuators in greenhouses are monitored in real time. The current study designed data-based, deep learning models for evapotranspiration (ET) and humidity in tomato greenhouses. Using time-series data and applying long short-term memory (LSTM) modeling, an ET prediction model was developed and validated in comparison with the Stanghellini model. Training with 20-day and testing with 3-day data resulted in RMSEs of 0.00317 and 0.00356 kgm(-2) s(-1), respectively. The standard error of prediction indicated errors of 5.76 and 6.45% in training and testing, respectively. Variables were used to produce a feature map using a two-dimensional convolution layer which was transferred to a subsequent layer and finally connected with the LSTM structure for modeling. The RMSE in humidity prediction using the test dataset was 2.87, indicating a performance better than conventional RNN-LSTM models. Irrigation plans and humidity control may be more accurately conducted in greenhouse cultivation using this model. English MDPI AG A Deep Learning Model to Predict Evapotranspiration and Relative Humidity for Moisture Control in Tomato Greenhouses Article 10.3390/agronomy12092169 1 Agronomy, v.12, no.9 Agronomy 12 9 Y scie scopus 000857440300001 Agronomy Plant Sciences Agriculture Plant Sciences Article NEURAL-NETWORK MODELS CROP EVAPOTRANSPIRATION NATURAL VENTILATION PENMAN-MONTEITH TEMPERATURE SIMULATION STRATEGY intelligent modeling for crops and their environment multi-factor control for greenhouse environment deep learning in agriculture