将生物物理参数与热和RGB指数结合起来,使用机器学习模型预测黄影响小麦作物的产量
R N Singh1,2, P Krishnan3, Vaibhav K Singh4
1Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi, India.
Scientific reports
|November 2, 2023
概括
黄显著影响小麦作物的健康和产量,降低了重要的生物物理参数. 机器学习模型通过整合遥感和植物数据,准确地预测早期的小麦产量,帮助作物管理.
科学领域:
- 农业学是一种农业学.
- 植物病理学 植物病理学
- 数据科学数据科学数据科学
背景情况:
- 早期作物健康评估和产量预测对于管理黄色生等生物压力至关重要.
- 黄会对小麦的生物物理参数产生负面影响,影响作物生产率.
研究的目的:
- 评估黄对不同品种小麦生物物理参数的影响.
- 开发精确的机器学习 (ML) 模型,以使用集成数据进行早期收益预测.
主要方法:
- 在两年时间 (2017 - 2019) 进行了24种小麦品种的实地实验.
- 测量了生物物理参数,并将它们与生严重程度相关联.
- 已经开发并验证了ML模型 (Cubist,PLS,SpikeSlab,KNN,RF等) 在各种增长阶段进行收益率预测.
主要成果:
- 增加的黄色生严重程度导致了更高的树冠温度和减少光合作用,透气和叶子含水量.
- 产量下降幅度从适度耐药品种的15.9%到敏感品种的61.1%.
- 随着收获的临近,ML模型对早期产量估计的准确性越来越高,特定模型在不同的生长阶段表现出色.
结论:
- 量化了不同度对小麦作物的生物物理特征的影响.
- 证明了将遥感和生物物理数据与ML集成为在压力下早期产量预测的有效性.
- 强调了特定的ML模型在不同作物生长阶段及时预测产量的实用性.
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