基于特征的优化,以防止家禽养殖场的生长和死亡率
Suhendra1, Hao-Ting Lin2, Vincentius Surya Kurnia Adi3
1Department of International Doctoral Program in Agriculture, Bio-Industrial Mechatronics Engineering, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan; Department of Agro-Industrial Technology, Universitas Bengkulu, 38112 Bengkulu, Indonesia.
Poultry science
|September 24, 2025
概括
一个新的深度学习模型使用每日数据准确预测家禽的死亡率和平均体重. 这种智能系统为家禽行业的精准农业提供了实际的决策支持.
科学领域:
- 精准农业 精准农业 精准农业
- 在动物科学中的机器学习
- 禽类生产系统的生产系统
背景情况:
- 禽业在降低死亡率和优化生长表现方面面临重大挑战.
- 环境和运营变量引入影响生产结果的变化.
- 有效的预测模型对于在家禽管理中做出明智决策至关重要.
研究的目的:
- 开发和实施一个特征驱动的优化模型来预测家禽的死亡率和平均体重.
- 评估各种机器学习模型对此预测任务的有效性.
- 为家禽管理人员提供实用,智能的决策支持工具.
主要方法:
- 利用了超过2万只台湾原生肉的88天数据集.
- 预处理的数据包括异常值的删除,规范化和插值.
- 开发和完善一个神经网络 (NN) 模型,特别是一个集团NN,用于多输出预测.
主要成果:
- 组合NN实现了0.45的死亡率和0.02的平均体重的低根平均平方误差 (RMSE).
- 功能重要性分析确定"日"是死亡率的主要预测因素,其次是料和水消耗.
- 该系统被集成到基于MATLAB的应用程序中,以便在实践中使用.
结论:
- 开发的Ensemble NN模型为家禽精密农业提供了一个可扩展和准确的工具.
- 智能系统为优化家禽生产提供了有价值的决策支持.
- 将深度学习与畜牧管理相结合,提高了效率并减少了损失.
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