基于放牧生产系统的牛群水平牛生长模式与增重预测的混合机器学习框架
Hybrid Machine Learning Framework for Herd-Level Cattle Growth Pattern and Weight Gain Forecasting in Grazing-Based Production Systems
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中文总结 AI 辅助
本研究针对放牧系统牛生长预测的不规则观测问题，开发混合机器学习框架，采用级联GB到RF到NN架构取得最优预测性能，可支持饲料分配等放牧相关决策。
中文摘要 AI 辅助
商业化放牧系统会产生不规则的牲畜观测数据，这对牛生长预测构成挑战。本研究开发了一种牛群水平牛体重预测的混合机器学习框架，使用2022年至2024年在澳大利亚东南部收集的自动传感观测数据。将每周活体体重观测值、人口统计学变量和滞后环境预测因子整合到结构化预测数据集中。通过对个体水平预测进行时间聚合生成牛群水平预测轨迹。评估了四类混合架构系列，包括残差框架、堆叠框架、级联框架和集成辅助框架。以ARIMA、LSTM和GRU模型作为比较基线。独立测试显示在多个预测区间内具有较强的预测一致性。级联GB到RF到NN架构取得了最佳性能，测试集R²为0.889，RMSE为21.319千克，MAE为15.462千克。在稀疏观测条件下，混合架构比循环序列模型保持更高的鲁棒性。随着预测区间延长，预测误差逐渐增大。特征重要性分析确定动物年龄、降雨量和温度是影响牛群水平生长预测的主要预测因子。所提出的框架可支持异构传感环境下的饲料分配、放牧管理和牲畜营销决策。
英文摘要
Commercial grazing systems yield irregular livestock observations, which challenge cattle growth forecasting. This study developed a hybrid machine learning framework for herd level cattle weight forecasting using automated sensing observations collected between 2022 and 2024 in southeastern Australia. Weekly live weight observations, demographic variables, and lagged environmental predictors were integrated into structured forecasting datasets. Herd level forecasting trajectories were generated through temporal aggregation of animal level predictions. Four hybrid architecture families were evaluated, including residual, stacked, cascade, and ensemble assisted frameworks. ARIMA, LSTM, and GRU models were used as comparative baselines. Independent testing demonstrated strong predictive agreement across multiple forecasting horizons. The cascade GB to RF to NN architecture achieved the best performance, with a test R^2 of 0.889, RMSE of 21.319 kg, and MAE of 15.462 kg. Hybrid architectures maintained greater robustness than recurrent sequential models under sparse observation conditions. Forecasting error increased progressively across extended prediction horizons. Feature importance analysis identified animal age, rainfall, and temperature as dominant predictors influencing herd level growth forecasting. The proposed framework may support feed allocation, grazing management, and livestock marketing decisions under heterogeneous sensing environments.
