基于不同种植方法的可见光图像,米中的叶绿素逆转
He Jing1, Wang Bin2, He Jiachen3
1School of Geography and Planning, Chengdu University of Technology, Chengdu, China.
PloS one
|March 24, 2025
概括
这项研究使用无人飞行器 (UAV) 的可见光图像来逆转大米中的叶绿素含量. 最好的模型,B-变量决策树和g-AdaBoost,在不同的种植方法下精确监测作物健康.
科学领域:
- 农业遥感 农业遥感
- 植物生理学 植物生理学
- 机器学习用于农业
背景情况:
- 叶绿素含量对于作物光合作用,生长和健康至关重要.
- 无人机可见光成像为作物监测和健康诊断提供了一种方法.
- 了解叶绿素变异对于优化农业实践至关重要.
研究的目的:
- 用无人机可见光图像来逆转大米中的叶绿素含量.
- 在各种机器学习模型中评估不同植被指数和纹理特征的有效性.
- 为了比较手工和机械栽培的田之间的模型性能.
主要方法:
- 从手工和机械耕种的田间收集的无人机可见的米正处于化阶段的正视图.
- 使用相关系数方法构建和分析了14个植被指数和15个纹理特征.
- 开发并比较K-邻近 (KNN),决策树 (DT) 和AdaBoost模型用于叶绿素含量逆转.
主要成果:
- 具有B-变异的决策树模型在手工种植面积 (A) 中实现了R2=0.840.
- 使用g的AdaBoost模型在机械耕种面积 (B) 中实现了R2=0.879.
- 选定的植被指数和纹理特征有效地预测了不同模型和种植方法的叶绿素含量.
结论:
- B-变量决策树和g-AdaBoost模型提供了准确和快速的米叶含量的反转.
- 这些模型为监测不同种植技术下的作物生长和健康提供了理论基础.
- 基于无人机的遥感与机器学习相结合,对于农业健康评估是有效的.
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