| 基于RF-GRU的温室番茄结果前期蒸腾量预测方法 |
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| 引用本文： | 李莉,李伟,耿磊,李文军,孙泉,SIGRIMIS N A.基于RF-GRU的温室番茄结果前期蒸腾量预测方法[J].农业机械学报,2022,53(3):368-376. |
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| 作者姓名： | 李莉 李伟 耿磊 李文军 孙泉 SIGRIMIS N A |
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| 作者单位： | 中国农业大学;雅典农业大学 |
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| 基金项目： | 国家重点研发计划项目(2019YFD1001903)和中央高校基本科研业务费专项资金项目(2021TC031) |
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| 摘 要： | 针对温室番茄无法按需灌溉问题,提出了随机森林(Random forest,RF)结合门控循环单元(Gated recurrent unit,GRU)神经网络的温室番茄结果前期蒸腾量预测方法,并开发了一套基于番茄蒸腾量的智慧灌溉系统.基于物联网实时获取数据,采用RF算法对影响温室番茄蒸腾量的变量进行特征重要性排序,选取作...
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| 关 键 词： | 温室番茄 智慧灌溉 蒸腾量预测 随机森林 门控循环单元 |
| 收稿时间： | 2021/12/24 0:00:00 |
Prediction Method of Greenhouse Tomato Transpiration in Early Fruiting Stage Based on RF-GRU |
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| LI Li,LI Wei,GENG Lei,LI Wenjun,SUN Quan,SIGRIMIS N A.Prediction Method of Greenhouse Tomato Transpiration in Early Fruiting Stage Based on RF-GRU[J].Transactions of the Chinese Society of Agricultural Machinery,2022,53(3):368-376. |
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| Authors: | LI Li LI Wei GENG Lei LI Wenjun SUN Quan SIGRIMIS N A |
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| Institution: | China Agricultural University; Agricultural University of Athens |
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| Abstract: | Taking greenhouse tomatoes as the research object, a forecasting method of transpiration of greenhouse tomatoes was proposed based on the real-time data of the Internet of things and random forest (RF) combined with gated recurrent unit (GRU) neural network. Firstly, the main factors affecting transpiration change collected by the sensor were preprocessed and RF was used to order the characteristic importance of the variables affecting the transpiration of tomato in greenhouse. Crop phenotypic parameters, including relative leaf area index, ecological parameters in greenhouse and cultivation environment parameters, including air temperature, relative humidity, light intensity, photosynthetically active radiation, substrate moisture content and substrate temperature were chosen as the input variables of the model. On this basis, a prediction model based on GRU was established to predict the transpiration of tomato. Finally, this model was compared with other models. At the same time, based on this model, a set of intelligent irrigation equipment was developed, which took the substrate water as the irrigation starting point and the predicted transpiration as the irrigation amount. The experimental results fully showed that the RF-GRU model had accurate prediction effect in tomato transpiration prediction and showed good feature learning ability in agricultural big data mining. The determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) were 0.9490, 10.96g and 5.80g, respectively. Compared with RF-LSTM and RF-RNN methods, the R2 was increased by 1.46% and 3.78%, the root mean square error was decreased by 1.38g and 3.24g, and the mean absolute error was decreased by 1.77g and 0.14g, respectively. At the same time, compared with regular irrigation, the intelligent irrigation system designed based on this model reduced the irrigation amount by 20% when the tomato growth was basically the same. This study could provide a reference for the research of greenhouse crop water requirements and it can be applied to water-saving greenhouse irrigation. |
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| Keywords: | greenhouse tomato intelligent irrigation transpiration prediction random forest gated recurrent unit |
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