Performance Evaluation of Machine Learning and Deep Learning Models for Temperature Prediction in Poultry Farming

V. Goyal, Ajay Yadav, Rahul Mukherjee
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引用次数: 3

Abstract

Temperature is a commonly used environmental factor that directly impacts both health of chicks and production in poultry farming. The cold weather makes the environment more conductive for certain infection diseases like Newcastle and Avian influenza, whereas heat stress or high temperature cause poor food efficiency and decreased production. Hence, the prediction of temperature with the support of machine learning (ML) and deep learning (DL) models in advance is advantageous for poultry farming. Real-time temperature data is captured with the support of Internet of Things (IoT) nodes and sent to clouds by wireless communication; this data is analyzed using various machine learning models on clouds, and decisions are made based on the knowledge extracted from received data. In this article, different machine learning (Random Forest, Linear Regression) and deep learning models (LSTM, BiLSTM) are used to process the temperature and provide temperature predictions after every 10 mins. All the models are compared on the various performance evaluation factors like mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R2), and Spearman's correlation coefficient (SPCC). The comparison results show that the R2 value for random forest i.e., 0.992 is highest compared to other models. Such models with high prediction rates significantly impact the environment management decisions and production on the farm.
家禽养殖温度预测中机器学习和深度学习模型的性能评价
在家禽养殖中,温度是一个常用的环境因素,它直接影响雏鸡的健康和生产。寒冷的天气使环境更容易感染某些传染病,如新城疫和禽流感,而热应激或高温则导致食物效率低下和产量下降。因此,在机器学习(ML)和深度学习(DL)模型的支持下,提前预测温度对家禽养殖是有利的。在物联网(IoT)节点的支持下捕获实时温度数据,并通过无线通信发送到云端;这些数据使用云上的各种机器学习模型进行分析,并根据从接收到的数据中提取的知识做出决策。在本文中,使用不同的机器学习(随机森林,线性回归)和深度学习模型(LSTM, BiLSTM)来处理温度,并在每10分钟后提供温度预测。比较各模型的平均绝对误差(MAE)、均方根误差(RMSE)、决定系数(R2)、Spearman相关系数(SPCC)等性能评价因子。对比结果表明,随机森林的R2值为0.992,高于其他模型。这种具有高预测率的模型显著影响着农场的环境管理决策和生产。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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