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Integration of Deep Learning and Sparrow Search Algorithms to Optimize Greenhouse Microclimate Prediction for Seedling Environment Suitability

文献类型： 外文期刊

作者： Shi, Dongyuan 1 ; Yuan, Pan 1 ; Liang, Longwei 1 ; Gao, Lutao 3 ; Li, Ming 1 ; Diao, Ming 1 ;

作者机构： 1.Shihezi Univ, Coll Agr, Key Lab Special Fruits & Vegetables Cultivat Physi, Xinjiang Prod & Construct Crops, Shihezi 832003, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr, Meteorol Serv Ctr Urban Agr, Natl Engn Lab Agriprod Qual Traceabil,Res Ctr Info, Beijing 100097, Peoples R China

3.Yunnan Agr Univ, Coll Big Data, Kunming 650201, Peoples R China

关键词： CNN; greenhouse microclimate; LSTM; sparrow search algorithm; time series prediction

期刊名称：AGRONOMY-BASEL （ 影响因子：3.7； 五年影响因子：4.0 ）

ISSN：

年卷期： 2024 年 14 卷 2 期

页码：

收录情况： SCI

摘要： The climatic parameters within greenhouse facilities, such as temperature, humidity, and light, exert significant influence on the growth and yield of crops, particularly seedlings. Therefore, it is crucial to establish an accurate predictive model to monitor and adjust the greenhouse microclimate for optimizing the greenhouse environment to the fullest extent. To precisely forecast the greenhouse microclimate and assess the suitability of nursery environments, this study focuses on greenhouse environmental factors. This study leveraged open-source APIs to acquire meteorological data, integrated a model based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM), and utilized the sparrow search algorithm to optimize model parameters, consequently developing a time series greenhouse microclimate prediction model. Furthermore, Squeeze-and-Excitation (SE) Networks were employed to enhance the model's attention mechanism, enabling more accurate predictions of environmental factors within the greenhouse. The predictive results indicated that the optimized model achieved high precision in forecasting the greenhouse microclimate, with average errors of 0.540 degrees C, 0.936%, and 1.586 W/m2 for temperature, humidity, and solar radiation, respectively. The coefficients of determination (R2) reached 0.940, 0.951, and 0.936 for temperature, humidity, and solar radiation, respectively. In comparison to individual CNN or LSTM models, as well as the back-propagation (BP) neural network, the proposed model demonstrates a significant improvement in predictive accuracy. Moreover, this research was applied to the greenhouse nursery environment, demonstrating that the proposed model significantly enhanced the efficiency of greenhouse seedling cultivation and the quality of seedlings. Our study provided an effective approach for optimizing greenhouse environmental control and nursery environment suitability, contributing significantly to achieving sustainable and efficient agricultural production.

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