结合机器学习和遥感集成作物建模,用于模拟大米和大豆作物
Jonghan Ko1, Taehwan Shin1, Jiwoo Kang1
1Department of Applied Plant Science, Chonnam National University, Gwangju, Republic of Korea.
Frontiers in plant science
|February 27, 2024
概括
机器学习使用近距离感应准确估计作物中的叶面积指数 (LAI). 将其集成到作物模型中,可以显著提高生长预测和监测能力.
科学领域:
- 农业科学 农业科学
- 遥感技术 遥感技术 遥感技术
- 机器学习应用 机器学习应用
背景情况:
- 准确的作物增长预测对于粮食安全至关重要.
- 叶面积指数 (LAI) 是作物建模的一个关键变量.
- 将遥感数据与作物模型集成,可以提高预测准确度.
研究的目的:
- 开发一种机器学习 (ML) 方法,使用近距离传感数据估计大米和大豆叶面积指数 (LAI).
- 用ML算法评估远程传感集成作物模型 (RSCM) 的性能.
- 确定最佳的ML算法,以从植被指数来估计LAI.
主要方法:
- 收集并分析了大米和大豆数据集.
- 采用了各种ML回归模型:,拉索,支向量机,随机森林和额外的树木.
- 模拟了LAI和植被指数之间的关系,这些指数来自树冠反射率.
主要成果:
- 额外树木回归模型显示了LAI估计的最佳表现 (测试得分:大米0.86,大豆0.89).
- 该ML集成模型在不同的处理下准确地复制了观察到的LAI值 (纳什 - 苏特克利夫效率:大米0.93,大豆0.97).
- 在不同管理实践中有效捕捉季节性LAI变化.
结论:
- 机器学习技术显著提高了远程传感数据与作物模型的整合.
- 开发的基于ML的LAI估计方法提高了作物生长预测的准确性.
- 这种方法为先进的作物监测和生产率评估提供了巨大的潜力.
关键词:
农作物 农作物 农作物叶面积指数 叶面积指数机器学习是机器学习.建模建模模型是什么远程传感是一种遥感技术.米米饭 米饭 米饭 米饭.豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆植物生长指数 植物生长指数更多相关视频
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