| 基于LightGBM-SSA-ELM的新疆羊舍CO2浓度预测 |
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| 引用本文： | 尹航,吕佳威,陈耀聪,岑红蕾,李景彬,刘双印.基于LightGBM-SSA-ELM的新疆羊舍CO2浓度预测[J].农业机械学报,2022,53(1):261-270. |
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| 作者姓名： | 尹航 吕佳威 陈耀聪 岑红蕾 李景彬 刘双印 |
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| 作者单位： | 仲恺农业工程学院信息科学与技术学院,广州510225;仲恺农业工程学院信息科学与技术学院,广州510225;北京市农林科学院信息技术研究中心,北京100097;仲恺农业工程学院信息科学与技术学院,广州510225;广东省农产品安全大数据工程技术研究中心,广州510225;石河子大学机械电气工程学院,石河子832003;仲恺农业工程学院信息科学与技术学院,广州510225;仲恺农业工程学院广州市农产品质量安全溯源信息技术重点实验室,广州510225 |
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| 基金项目： | 国家自然科学基金项目(61871475)、广东省自然科学基金项目（2021A1515011605）、现代农业机械兵团重点实验室开放项目（BTNJ2021002）、广州市创新平台建设计划项目（201905010006）、广州市重点研发计划项目（20210300003）和广东省科技厅重点领域研发计划项目（2020B0202080002） |
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| 摘 要： | 为减少肉羊集约化养殖过程中因环境恶化产生的应激反应,精准调控CO2质量浓度,提出了基于分布式梯度提升框架(LightGBM)、麻雀搜索算法(SSA)融合极限学习机(ELM)的CO2质量浓度预测模型.首先利用LightGBM筛选出与CO2质量浓度相关的重要特征,降低预测模型的输入维度;然后选择Sigmoid为激活函数,使...
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| 关 键 词： | 羊舍 集约化养殖 CO2质量浓度预测 极限学习机 麻雀搜索算法 分布式梯度提升框架 |
| 收稿时间： | 2021/7/15 0:00:00 |
Prediction of CO2 Concentration in Xinjiang Breeding Environment of Mutton Sheep Based on LightGBM-SSA-ELM |
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| YIN Hang,L Jiawei,CHEN Yaocong,CEN Honglei,LI Jingbin,LIU Shuangyin.Prediction of CO2 Concentration in Xinjiang Breeding Environment of Mutton Sheep Based on LightGBM-SSA-ELM[J].Transactions of the Chinese Society of Agricultural Machinery,2022,53(1):261-270. |
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| Authors: | YIN Hang L Jiawei CHEN Yaocong CEN Honglei LI Jingbin LIU Shuangyin |
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| Institution: | Zhongkai University of Agriculture and Engineering;Zhongkai University of Agriculture and Engineering;Beijing Academy of Agricultural and Forestry Sciences;Shihezi University |
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| Abstract: | Air quality plays an important role in mutton sheep breeding environment, in order to reduce the stress response of CO2 to the growth of large-scale mutton sheep and ensure the healthy growth of mutton sheep in the appropriate environment, the key is to accurately control the CO2 in the mutton sheep breeding environment. A CO2 prediction model of mutton sheep breeding environment was proposed based on light gradient boosting machine (LightGBM), sparrow search algorithm (SSA) and extreme learning machine (ELM). Firstly, LightGBM was used to screen out the important characteristics of carbon dioxide concentration and reduce the input dimension of the prediction model. Then, ELM neural network algorithm with single hidden layer with strong nonlinear processing ability was used to build the CO2 prediction model. Finally, through the sparrow intelligent optimization algorithm, the super parameters needed in ELM model were optimized to obtain the best prediction model. The prediction model was applied to a large-scale mutton sheep breeding base in Manas County, Changji Hui Autonomous Prefecture, Xinjiang Uygur Autonomous Region, and good prediction results were obtained. The experimental results showed that the prediction model had good prediction effect, and the root mean square error (RMSE) of ELM was higher than that of SVR, BPNN, LSTM, GRU and LightGBM. The RMSE, mean absolute error (MAE) and R2 were 0.0213mg/L, 0.0136mg/L and 0.9886, respectively. The results showed that the combined model can not only achieve accurate control of carbon dioxide in sheep house, but also meet the needs of fine decision-making for mutton sheep breeding. It also can help farmers make decisions and reduce farming risks. |
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| Keywords: | sheep house intensive culture CO2 concentration prediction extreme learning machine sparrow search algorithm light gradient boosting machine |
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