| 基于SSA-LSTM的日光温室环境预测模型研究 |
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| 引用本文： | 祖林禄,柳平增,赵妍平,李天华,李辉.基于SSA-LSTM的日光温室环境预测模型研究[J].农业机械学报,2023,54(2):351-358. |
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| 作者姓名： | 祖林禄 柳平增 赵妍平 李天华 李辉 |
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| 作者单位： | 山东农业大学 |
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| 基金项目： | 山东省农业重大应用技术创新项目(SD2019ZZ019)、山东省科技型中小企业创新能力提升工程项目(2022TSGC2047)和山东省重大科技创新工程项目(2022CXGC020708) |
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| 摘 要： | 构建日光温室环境预测模型，准确预测温室环境变化有助于精准调控作物生长环境，促进果蔬生长。而温室小气候环境数据多参数并存、耦合关系复杂，且具有时序性和非线性，难以建立准确的预测模型。针对以上问题，提出一种基于麻雀搜索算法(SSA)优化的长短期记忆网络(LSTM)温室环境预测模型，实现了温室环境数据的精准预测。实验结果表明，采用SSA自动进行参数选优的方式，解决了LSTM模型参数手动选择的难题，大幅缩短模型训练时间，且最优的网络参数能够发挥模型的最佳性能。对日光温室内空气温湿度、土壤温湿度、CO2浓度和光照强度6种环境参数进行预测，SSA-LSTM平均拟合指数高达97.6%,相比BP、门控循环单元(GRU)、LSTM,其预测拟合指数分别提升8.1、4.1、4.3个百分点，预测精度明显提升。
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| 关 键 词： | 日光温室 物联网 麻雀搜索算法 长短期记忆网络 环境预测模型 |
| 收稿时间： | 2022/3/22 0:00:00 |
Solar Greenhouse Environment Prediction Model Based on SSA-LSTM |
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| ZU Linlu,LIU Pingzeng,ZHAO Yanping,LI Tianhu,LI Hui.Solar Greenhouse Environment Prediction Model Based on SSA-LSTM[J].Transactions of the Chinese Society of Agricultural Machinery,2023,54(2):351-358. |
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| Authors: | ZU Linlu LIU Pingzeng ZHAO Yanping LI Tianhu LI Hui |
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| Institution: | Shandong Agricultural University |
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| Abstract: | The accurate prediction of greenhouse environment variation based on the constructed prediction model is helpful to precisely regulate the crop environment, and promote the growth of fruits and vegetables. Due to the coexistence of multiple parameters, complex coupling with each other, temporality and nonlinearity of greenhouse microclimate environment, the accurate prediction model is difficult to establish. Based on above issues, a greenhouse environment prediction model was proposed based on the sparrow search algorithm (SSA) optimized-long short term memory (LSTM) neural network method, so as to realize the prediction of greenhouse environment data sequence with the Internet of things (IoT) collecting accurate multipoint environment data. The experimental results showed that the automatic parametric optimization process by SSA could deal with the time consuming problem of manual parameter selection for the LSTM model. The proposed SSA-LSTM method could lower the model training time, and the optimal parameters selection could make sure the model worked with the optimum capability. The trained SSA-LSTM model was used to predict six kinds of greenhouse environment data, including the air temperature, air humidity, soil temperature, soil humidity, CO2 concentration, and the illumination intensity. The proposed SSA-LSTM could realize a 97.6% average prediction fit index, compared with the back-propagation network, the gated recurrent unit neural network and the LSTM, the prediction fit index was elevated by 8.1 percentage points, 4.1 percentage points and 4.3 percentage points. Therefore, the prediction accuracy of SSA-LSTM was obviously improved. The research result could provide reference for the development of greenhouse environment control strategy and deal with the lag problem of environment control. |
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| Keywords: | solar greenhouse Internet of things sparrow search algorithm LSTM neural network environment prediction model |
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