摘要
土壤含氧量(Soil oxygen content, SOC)是影响作物生长的重要土壤环境因素之一,具有时序性、不稳定性和非线性等特点,精确预测土壤环境中含氧量的变化趋势,有助于制定更加合理的土壤通气增氧方案。本研究提出基于麻雀搜索算法(Sparrow search algorithm, SSA)和长短时记忆(Long and short-term memory, LSTM)神经网络预测模型,利用国家土壤质量湛江观测实验站记录玉米种植期间的气象环境和土壤环境数据,基于SSA-LSTM模型对SOC变化进行预测及相关性分析,并与传统的BP预测模型、LSTM预测模型、GA-LSTM预测模型及PSO-LSTM预测模型进行对比。试验结果表明,SOC与降雨量、土壤含水率、土壤温度、土壤充气孔隙度相关性极显著,相关系数高于0.8,与大气温度和风速相关性显著,与大气湿度和土壤呼吸速率相关性较弱。SSA-LSTM模型预测精度明显高于其他4组对照预测模型,R^(2)达到0.959 79,RMSE仅为0.491 7%,MAPE为3.733 1%,MAE为0.362 0%,预测值与试验值之间的拟合程度高。本研究可为土壤含氧量变化的精准预测及土壤通气增氧技术的应用推广提供理论支撑与科学依据。
Soil oxygen content(SOC) is one of the important soil environmental factors that affect crop growth. It has the characteristics of time series, instability and nonlinearity. It can accurately predict the change trend of oxygen content in the soil environment, which is helpful to formulate a more reasonable soil aeration and oxygenation program. A prediction model based on the sparrow search algorithm(SSA) and long and short-term memory(LSTM) neural network was proposed, the meteorological environment and soil environment record data during the corn planting period were to recorded by using the equipment at the National Soil Quality Zhanjiang Observation and Experimental Station. The SSA-LSTM model predicted and analyzed the SOC changes, and it was compared with the traditional BP prediction model, LSTM prediction model, GA-LSTM prediction model and PSO-LSTM prediction model. The test results showed that the correlation between SOC and rainfall, soil water content, soil temperature and air-filled porosity was extremely significant, the correlation coefficient was higher than 0.8, the correlation with atmospheric temperature and wind speed was significant, and the correlation with atmospheric humidity and soil respiration rate was relatively significant. The prediction accuracy of the SSA-LSTM model was significantly higher than that of the other four groups of control prediction models. The R^(2) reached 0.959 79, the RMSE was only 0.491 7%, the MAPE was 3.733 1%, and the MAE was 0.362 0%. The degree of fit between the predicted value and the experimental value was high. The research result can provide theoretical support and scientific basis for the accurate prediction of soil oxygen content changes and the application and promotion of soil aeration and oxygenation technology.
作者
于珍珍
邹华芬
于德水
汪春
刘天祥
张欣悦
YU Zhenzhen;ZOU Huafen;YU Deshui;WANG Chun;LIU Tianxiang;ZHANG Xinyue(College of Engineering,Heilongjiang Bayi Agricultural University,Daqing 163319,China;South Subtropical Crops Research Institute,Chinese Academy of Tropical Agricultural Sciences,Zhanjiang 524003,China;School of Management,Huazhong University of Science and Technology,Wuhan 430074,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2022年第11期360-368,411,共10页
Transactions of the Chinese Society for Agricultural Machinery
基金
海南省自然科学基金面上项目(322MS118)
海南省自然科学基金青年基金项目(322QN375)
关键词
玉米
土壤
含氧量预测
麻雀搜索算法
长短时记忆网络
BP神经网络
maize
soil
oxygen content prediction
sparrow search algorithm
long and short-term memory network
BP neural network
