基于机器学习的奶牛饲料消耗状态预测模型  

Prediction model for dairy cow feed consumption status based on machine learning

作　　者：张博 罗维平 ZHANG Bo;LUO Weiping(School of Mechanical Engineering and Automation,Wuhan Textile University,Wuhan 430200,China;Hubei Digital Textile Equipment Key Laboratory,Wuhan 430200,China)

机构地区：[1]武汉纺织大学机械工程与自动化学院,武汉430200 [2]湖北省数字化纺织装备重点实验室,武汉430200

出　　处：《农业工程学报》2024年第9期164-172,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(62103309);湖北省数字化纺织装备重点实验室开放课题(DTL2022001)。

摘　　要：饲料作为奶牛重要的营养来源,预测饲料消耗状态对于保障奶牛的健康和提高生产管理效率具有重要意义。然而,由于饲料消耗状态数据呈现出非线性和非平稳的特点,导致预测精度较低。为解决此问题,该研究基于经验模态分解(empirical mode decomposition,EMD)和长短期记忆网络(long short-term memory,LSTM),提出了组合改进的自适应噪声完全集成经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)、随机森林(random forest,RF)与改进的LSTM(improved LSTM,ILSTM)的模型,即ICEEMDANRF-ILSTM,来预测饲料消耗状态。其通过调整遗忘门的输出值范围以增强模型的特征学习能力。首先,使用ICEEMDAN对饲料消耗状态数据进行分解,得到多个相对平稳的分量。其次,考虑到每个分量具有不同的特性,采用不同的方法来建模不同的分量,以进一步提升预测效果。具体而言,为了提升模型的精度以及泛化能力,使用RF建模频率最高的分量;同时,使用ILSTM建模其余分量,以捕获序列数据中的长期依赖性。最后,将所有分量的预测结果相加得到最终的预测结果。基于自建数据集的试验结果表明,ICEEMDAN-RF-ILSTM对于饲料消耗状态预测具有较高的准确度,其决定系数R2、平均绝对百分比误差与均方根误差分别为0.993、2.576%和0.596%,表明其能有效预测饲料消耗状态,同时其性能优于ICEEMDAN-LSTM模型。该研究为评估饲料消耗状态提供了可行的方法,可为制定调度决策提供了科学的技术支持,并为牧业智能化建设提供借鉴。Feed is essential to provide nutrients to dairy cows.An accurate prediction of feed consumption can greatly contribute to ensuring the health of dairy cows and improving production efficiency.However,the feed consumption status often exhibits nonlinear and non-stationary patterns,resulting in low prediction accuracy.In this study,based on the empirical mode decomposition(EMD)and long short-term memory(LSTM),a prediction model of feed consumption was proposed to combine the improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN),random forest(RF),and improved LSTM(ILSTM),namely ICEEMDAN-RF-ILSTM.Among them,ILSTM,an enhancement of LSTM,was used to adjust the output range of the forgetting gate to retain more data features,thereby improving feature learning.This model decomposed the original data into multiple relatively stationary components.Then,the different features of each component were considered to predict.The first stage was data decomposition.Since ICEEMDAN can decompose nonlinear and nonstationary time sequences into several relatively stationary components,the model decomposed the original data into multiple intrinsic mode function(IMF)components using ICEEMDAN.Each component was arranged from the high to the low frequency.The component with the highest frequency showed complex fluctuation patterns,whereas,the component with the lowest frequency represented the overall change trend of the data,regarded as the trend component.The remaining components reflected the periodic pattern of the data,regarded as the periodic components.The second stage was data prediction for the different components.RF,an ensemble learning method,was used to predict the component with the highest frequency,benefiting from its ability to construct and integrate multiple models.Additionally,the robustness and generalization of the RF model were improved by randomly selecting features.Meanwhile,ILSTM was used to predict the periodic and trend pattern components.The final stage was data integration.The

关 键 词：机器学习 饲料 状态预测 ICEEMDAN 长短期记忆网络 奶牛 

分 类 号：S24[农业科学—农业电气化与自动化] TP391[农业科学—农业工程]