使用具有有限植物质量参数的数据挖掘技术预测新鲜草产量
Şenol Çelik1, Halit Tutar2, Erdal Gönülal3
1Biometry and Genetic Unit, Department of Animal Science, Faculty of Agriculture, Bingol University, 12000, Bingöl, Turkey. senolcelik@bingol.edu.tr.
Scientific reports
|September 13, 2024
概括
在干旱地区,MARS算法最好预测-苏丹草新鲜草的产量. 这种数据挖掘技术为农业生产提供了准确的预测.
科学领域:
- 农业科学 农业科学
- 数据挖掘 数据挖掘
- 料生产 料生产
背景情况:
- Sorghum-Sudangrass杂交物是干旱和半干旱地区的重要料作物.
- 优化新鲜草产量对于畜牧业的可持续性至关重要.
- 了解植物特征及其与产量的关系对于有效管理至关重要.
研究的目的:
- 为了评估新鲜草的产量,肥料效果和麦-苏丹草的植物特征.
- 评估数据挖掘技术对草产量的预测性能.
- 确定最适合在干旱环境中预测新鲜草产量的模型.
主要方法:
- 采用了包括CHAID,CART,MARS和Bagging MARS在内的数据挖掘技术.
- 植物特征和产量数据是在2021-2022年从科尼亚和桑利乌尔法收集的.
- 使用R2,调整后的R2,RMSE,MAPE,SD比率和AIC来评估模型性能.
主要成果:
- 马尔斯算法证明了卓越的预测准确性.
- 火星获得了最高的R2 (0.993) 和调整后的R2 (0.989).
- 在火星上,RMSE (246),MAPE (1.926),SD比率 (0.085) 和AIC (845) 是最低的.
结论:
- 马尔思算法是最有效的模型来表征-苏丹草新鲜草的产量.
- 对于其他数据挖掘方法来说,MARS为产量预测提供了一个强大的替代方案.
- 准确的产量预测支持干旱地区优化农业实践.
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