通过随机森林模型和元启发算法的集成来估计Ross 308肉的体重
Erdem Küçüktopçu1, Bilal Cemek1, Didem Yıldırım1
1Department of Agricultural Structures and Irrigation, Ondokuz Mayıs University, Samsun 55139, Türkiye.
Animals : an open access journal from MDPI
|November 9, 2024
概括
使用粒子群优化 (PSO) 和殖民地优化 (ACO) 的新混合方法显著提高肉体重预测的准确性. 这些算法增强了随机森林模型,为家禽养殖提供了高效和精确的估计.
科学领域:
- 农业科学 农业科学
- 计算智能是一种计算智能.
- 机器学习 机器学习
背景情况:
- 准确的肉体重 (CW) 估计对于优化家禽生产至关重要.
- 传统方法可能缺乏动态农场条件所需的精度.
- 超参数调整对于最大限度地提高机器学习模型的性能至关重要.
研究的目的:
- 开发和评估一种新的混合方法,以准确估计肉的体重.
- 将优化的随机森林 (RF) 模型与基准算法的性能进行比较.
- 评估CW预测的不同优化技术的效率和准确性.
主要方法:
- 开发了一种混合方法,集成粒子优化 (PSO) 和殖民地优化 (ACO) 来调整随机森林 (RF) 超参数.
- 收集了来自土耳其六家农场 (2014-2021) 的肉数据,包括温度,湿度,料消耗和CW.
- 与PSO和ACO优化的RF模型与标准RF和线性回归 (LR) 的预测性能进行了比较.
主要成果:
- 与标准RF和LR相比,RF-PSO和RF-ACO模型在CW预测准确度方面取得了显著的改进.
- RF-PSO将平均绝对误差 (MAE) 降低了5.081%至60.707%,而RF-ACO则实现了MAE降低3.066%至43.399%.
- 无论是RF-PSO还是RF-ACO都表现出相当大的计算效率,需要很短的训练时间.
结论:
- 将RF与PSO或ACO相结合的混合方法对于准确估计肉体重非常有效.
- PSO和ACO是计算效率高的算法,用于优化精准农业中的机器学习模型.
- 开发的混合方法为提高家禽养殖场管理和生产率提供了一个有希望的解决方案.
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