基于门控循环单元的基质栽培黄瓜结果期蒸散量预测模型

朱鑫; 林琼; 何淽琦; 易志刚

doi:10.19303/j.issn.1008-0384.2024.05.004

基于门控循环单元的基质栽培黄瓜结果期蒸散量预测模型

doi: 10.19303/j.issn.1008-0384.2024.05.004

朱鑫^{1, 2,},
林琼^2, ,,
何淽琦²,
易志刚^{1, 3}

1.
福建农林大学机电工程学院，福建　福州　350002
2.
福建省农业科学院资源环境与土壤肥料研究所，福建　福州　350013
3.
福建农林大学资源与环境学院，福建　福州　350002

基金项目: 福建省科技计划区域发展项目（2021N3008）；福建省科技计划公益类专项（2020R1025006）

详细信息

作者简介:
朱鑫（1997 —），男，硕士研究生，主要从事机电一体化研究，E-mail：18438606867@163.com

通讯作者:
林琼（1972 —），男，副研究员，主要从事植物营养与无土栽培研究，E-mail：linqiong@163.com

中图分类号: S161.4+2;TP312
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- 文章访问数: 132
- HTML全文浏览量: 70
- PDF下载量: 44
- 被引次数: 0
出版历程
- 收稿日期: 2023-12-21
- 录用日期: 2024-05-22
- 修回日期: 2024-01-15
- 网络出版日期: 2024-06-26
- 刊出日期: 2024-05-28

Models for Predicting Evapotranspiration of Fruiting Cucumber Plants in Greenhouse

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

摘要

摘要: 目的实时、准确地预测基质栽培黄瓜结果期蒸散量，指导基质栽培黄瓜灌溉。方法通过传感器实时获取黄瓜结果期的温室小气候环境数据，用称量法测量黄瓜蒸散量，以移栽时间、空气温度、空气相对湿度、光照强度及前5天的日均灌溉量为输入变量，利用BP神经网络（Back propagation neural network, BPNN）、卷积神经网络（Convolutional neural networks, CNN）、长短期记忆网络（Long short-term memory, LSTM）和门控循环单元（Gated recurrent unit, GRU）分别建立基质栽培黄瓜蒸散量预测模型，比较不同模型的预测效果，模型数据集的时间间隔设为20 min。结果相较于BPNN、CNN及LSTM模型，GRU模型的预测效果最好，其决定系数（R²）、均方根误差（RMSE）、平均绝对误差（MAE）分别为0.8577、2.3279 g和1.6744 g。当实测的黄瓜每日实时累积蒸散量超过50 g时，GRU模型预测的黄瓜每日实时累积蒸散量与实测每日实时累积蒸散量之间的相对误差最小，在0.11%～10.01%。结论基于GRU的基质栽培黄瓜结果期蒸散量预测模型预测效果最好，可为基质栽培黄瓜的灌溉系统提供参考。
- 蒸散量 /
- 基质栽培 /
- 门控循环单元 /
- 预测模型
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

HTML全文

图 1 黄瓜叶面积、黄瓜日均蒸散量与黄瓜日均灌溉量的关系

Figure 1. Relationships among leaf area, daily average evapotranspiration, and daily average irrigation of cucumber plants

下载: 全尺寸图片幻灯片

图 2 不同模型预测值与实测值的相关曲线

Figure 2. Correlation between predicted and measured values by different models

下载: 全尺寸图片幻灯片

图 3 黄瓜每日实时累积蒸散量曲线

Figure 3. Daily real-time cumulative evapotranspiration of cucumber plants

下载: 全尺寸图片幻灯片

表 1 GRU不同网络参数下的RMSE

Table 1. RMSE of GRU under different network parameters　　　　　　　　　　　　　　　　　　（单位：g）

时间步长 Time step	隐含层节点数 Number of hidden layer nodes
时间步长 Time step	5	10	15
12	2.4318	2.6083	2.6658
24	2.3279	2.3469	2.3374
36	2.4995	2.3547	2.4304

下载: 导出CSV

表 2 GRU不同网络参数下的MAE

Table 2. MAE of GRU under different network parameters 　　　　　　　　　　　　　　　　　　（单位：g）

时间步长 Time step	隐含层节点数 Number of hidden layer nodes
时间步长 Time step	5	10	15
12	1.6457	1.8546	1.9906
24	1.6744	1.6997	1.6951
36	1.8922	1.8152	1.8680

下载: 导出CSV

表 3 不同模型预测黄瓜每日实时累积蒸散量相对误差

Table 3. Relative error of daily real-time cumulative evapotranspiration of cucumber plants　（单位：%）

模型 Model	日期 Date
模型 Model	2023-05-12	2023-05-13	2023-05-14
BP神经网络	23.50±20.03	36.69±28.72	17.51±16.35
CNN	8.73±8.73	7.74±7.59	14.27±14.01
LSTM	3.56±3.43	4.85±4.63	6.41±6.35
GRU	2.04±1.93	5.63±4.38	3.24±2.52

下载: 导出CSV

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