通过将多源数据与大米 (Oryza sativa L.) 的机器学习相结合,提高了叶子含水量的预测性能
Xuenan Zhang1, Haocong Xu1, Yehong She1
1Agricultural College, Anhui Agricultural University, Hefei, 230036, Anhui, People's Republic of China.
Plant methods
|March 24, 2024
概括
精确监测大米叶水含量 (LWC) 对于高产量至关重要. 整合多来源数据与光谱指数显著提高了LWC估计的准确性,有助于精确的灌管理.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 植物生理学 植物生理学
背景情况:
- 叶子中的水含量 (LWC) 极大地影响了米的生长,产量和用水效率.
- 实时监测大米LWC对于农业的精密灌至关重要.
- 超光谱遥感可以快速,非破坏性地监测作物水的状态.
研究的目的:
- 开发和验证一个模型,使用综合多源数据准确估计米的LWC.
- 评估光谱指数模型的性能,结合用于LWC监测的生态和生理参数.
- 调查机器学习算法在预测大米LWC的有效性.
主要方法:
- 进行了为期两年的实地实验,采用了不同的灌方案和水品种.
- 收集了包括树冠生态因素和生理参数在内的多来源数据.
- 开发了包含多源数据和应用机器学习算法 (GBDT) 的植被指数模型.
主要成果:
- 结合多个来源的数据,与单个光谱指数相比,LWC估计的准确性提高了6-44%.
- 渐变增强决策树 (GBDT) 模型,使用ND{1287,1673) 和CWSI,实现了最佳的预测准确性 (R2=0.86,RMSE=0.01).
- 综合模型在估计大米LWC时表现优异.
结论:
- 集成多源数据的机器学习模型增强了用于米LWC监测的光谱技术.
- 开发的模型有效地利用了光谱信息,并考虑到环境变化.
- 研究结果支持改善水状况诊断和精确的灌管理,用于米种植.
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