Models for Predicting Evapotranspiration of Fruiting Cucumber Plants in Greenhouse

ZHU Xin; LIN Qiong; HE Zhiqi; YI Zhigang

doi:10.19303/j.issn.1008-0384.2024.05.004

Volume 39 Issue 5

May 2024

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ZHU X, LIN Q, HE Z Q, et al. Models for Predicting Evapotranspiration of Fruiting Cucumber Plants in Greenhouse [J]. Fujian Journal of Agricultural Sciences，2024，39（5）：532−539 doi: 10.19303/j.issn.1008-0384.2024.05.004

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ZHU X, LIN Q, HE Z Q, et al. Models for Predicting Evapotranspiration of Fruiting Cucumber Plants in Greenhouse [J]. Fujian Journal of Agricultural Sciences，2024，39（5）：532−539 doi: 10.19303/j.issn.1008-0384.2024.05.004

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Models for Predicting Evapotranspiration of Fruiting Cucumber Plants in Greenhouse

doi: 10.19303/j.issn.1008-0384.2024.05.004

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, Fuzhou, Fujian　350002, China
2.
College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
3.
College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China

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

Available Online: 2024-06-26

Publish Date: 2024-05-28

Abstract

Abstract

Objective Mathematic models for accurate real-time prediction on evapotranspiration of greenhouse cucumber plants during fruiting period were evaluated to optimize the irrigation operation. Method Cucumber plants were cultivated in a greenhouse. During the fruiting period, microclimate conditions were automatically monitored by sensors and recorders, and plant evapotranspiration determined by weighing the fruits. Using transplanting time, air temperature, relative humidity, light intensity, and daily average irrigation amount of previous 5d as inputs, models including the Back Propagation Neural Network (BPNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were evaluated according to the cucumber evapotranspiration prediction. A data collection interval of 20min was applied. Result Of the tested models, GRU performed with the highest coefficient of determination (R²) of 0.8577, root mean square error (RMSE) of 2.3279 g, and mean absolute error (MAE) of 1.6744 g. It also yielded the lowest relative error fluctuation between the predicted and the measured data ranging from 0.11% to 10.01% when the daily real-time cumulative evapotranspiration of cucumbers exceeded 50 g. Conclusion The GRU-based model could best predict the greenhouse cucumber evapotranspiration at fruiting stage. The information could aid better water management for cucumber cultivation.
- evapotranspiration,
- substrate cultivation,
- gated recurrent unit,
- prediction model

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References(27)

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