用基于梯度的算法预测作物的产量
Pavithra Mahesh1, Rajkumar Soundrapandiyan1
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India.
PloS one
|August 26, 2024
概括
使用机器学习的准确作物产量预测有助于农民. 分类提升 (CatBoost) 机器学习模型在预测作物产量方面实现了99.123%的准确性,表现优于LightGBM和XGBoost.
科学领域:
- 农业科学 农业科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 准确的作物产量评估对于农民收入,损失最小化和战略农业规划至关重要.
- 预测作物产量是农业的一个重大挑战,影响决策和政策.
- 环境和经济因素影响作物选择和产量.
研究的目的:
- 评估和比较各种机器学习算法用于作物产量预测的性能.
- 根据关键参数确定最准确的机器学习模型来预测作物产量.
主要方法:
- 使用机器学习算法开发了预测模型:分类增强 (CatBoost),光梯度增强机器 (LightGBM) 和极端梯度增强 (XGBoost).
- 使用的参数包括农药,降雨量和模型训练的平均温度.
- 计算了根平均平方误差 (RMSE) 和R平方 (R2) 值,以根据观察到的水产量评估预测的准确性.
主要成果:
- CatBoost实现了最高的精度,准确率为99.123%.
- 对于CatBoost的RMSE和R2值为800 (0.24),LightGBM为737 (0.33),XGBoost为744 (0.31),而这些值均为800 (0.24),LightGBM为737 (0.33),而XGBoost则为744 (0.31).
- 与其他算法相比,CatBoost,LightGBM和XGBoost在作物产量预测方面表现出卓越的准确性.
结论:
- 机器学习算法,特别是CatBoost,对准确的作物产量预测有很大的前景.
- 该研究框架提供了一种可靠的方法来评估ML模型在农业中的性能.
- 准确的产量预测可以支持农民和政策制定者在农业商品管理.
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