Prediction Model of Evapotranspiration during the Fruit Period of Cucumber Grown in Substrate Based on Gated Recurrent Unit

ZHU Xin; LIN Qiong; HE Zhiqi; YI Zhigang

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ZHU X, LIN Q, HE Z Q, et al. Prediction Model of Evapotranspiration during the Fruit Period of Cucumber Grown in Substrate Based on Gated Recurrent Unit [J]. Fujian Journal of Agricultural Sciences，2024，39（X）：1−9

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ZHU X, LIN Q, HE Z Q, et al. Prediction Model of Evapotranspiration during the Fruit Period of Cucumber Grown in Substrate Based on Gated Recurrent Unit [J]. Fujian Journal of Agricultural Sciences，2024，39（X）：1−9

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Prediction Model of Evapotranspiration during the Fruit Period of Cucumber Grown in Substrate Based on Gated Recurrent Unit

ZHU Xin^{1, 2
,},
LIN Qiong^{2
,
,},
HE Zhiqi²,
YI Zhigang^{1, 3}

1.
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fujian Fuzhou　350002, China
2.
Institute of Resources, Environment and Soil Fertilizer, Fujian Academy of Agricultural Sciences, Fujian Fuzhou　350013, China
3.
College of Resources and Environment, Fujian Agriculture and Forestry University, Fujian Fuzhou　350002, China

Received Date: 2023-12-21
Accepted Date: 2024-05-22
Rev Recd Date: 2024-01-15

Available Online: 2024-06-26

Abstract

Abstract

:[ Objective ] Real time and accurate prediction of evapotranspiration during the fruiting period of substrate cultivated cucumber, guiding substrate cultivated cucumber irrigation. Method Real time greenhouse microclimate environmental data during the cucumber fruiting period is obtained through sensors. Cucumber evapotranspiration is measured using the weighing method, with transplanting time, air temperature, relative humidity, light intensity, and daily average irrigation amount for the first five days as inputs, using Back Propagation neural network (BPNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) to establish substrate cultivation cucumber evapotranspiration prediction models, and the prediction effects of different models are compared. The model dataset The time interval is set to 20 minutes. Result Compared to BPNN, CNN, and LSTM models, the GRU model has the best prediction performance, with a coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) of 0.8577, 2.3279 g, and 1.6744 g, respectively. When the measured daily real-time cumulative evapotranspiration of cucumbers exceeds 50 g, the relative error fluctuation between the predicted daily real-time cumulative evapotranspiration of cucumbers by the GRU model and the measured daily real-time cumulative evapotranspiration is the smallest, ranging from 0.11% to 10.01%. Conclusion The prediction model of evapotranspiration during the fruiting period of substrate cultivated cucumbers based on GRU has the best prediction effect, which can provide reference for irrigation system of substrate cultivated cucumbers.
- Evapotranspiration,
- Substrate cultivation,
- Gated recurrent unit,
- Prediction model

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References(28)

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