整合气象和育种数据以使用机器学习算法预测玉米产量
Shaoqiang Wang1, Guangcai Wang2, Yuchen Wang3
1School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China.
Frontiers in plant science
|December 8, 2025
概括
一个新的机器学习模型使用遗传和天气数据准确预测玉米杂交产量. 随机森林算法为农民和育种者提供了一种具有成本效益的工具,以提高作物生产和粮食安全.
科学领域:
- 农业科学 农业科学
- 遗传学 是一个遗传学.
- 数据科学数据科学数据科学
背景情况:
- 准确的农作物产量预测对于全球粮食安全至关重要,特别是在气候变化的影响下.
- 用于产量预测的深度学习模型通常需要大量的数据和计算能力.
研究的目的:
- 开发一种机器学习模型,用于预测玉米杂交产量.
- 将气象数据与遗传信息 (繁殖值) 结合起来,以提高预测准确度.
主要方法:
- 评估了四种机器学习算法:随机森林 (RF),XGBoost,支持向量回归 (SVR) 和高斯过程回归 (GPR).
- 使用超参数调优化模型.
- 综合气象数据与最佳线性无偏预测 (BLUP) 估计的繁殖值.
主要成果:
- 随机森林 (RF) 算法表现出卓越的性能.
- 射频实现了0.64.2的确定系数 (R2).
- 关键绩效指标包括RMSE为1010.59公斤/公,MAPE为8.3%.
结论:
- 基于射频的模型为特定品种和环境提供了准确的玉米产量预测.
- 该框架支持农民选择适应的杂交品种,并帮助育种者识别高产品种.
- 这促进了有效的育种策略和精确的种植建议,以提高农业生产率.
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