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一个综合的特征选择方法高水应力产量预测预测

Zongpeng Li¹, Xinguo Zhou¹, Qian Cheng¹

¹Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang, China.

Frontiers in plant science

|December 19, 2023

概括

准确的冬季小麦产量预测对于粮食安全至关重要. 使用植被指数的综合特征选择方法 (PCRF-RFE) 改善了预测模型,立体模型表现出卓越的性能.

科学领域:

农业科学农业科学
遥感遥感遥感遥感
数据科学数据科学数据科学

背景情况:

准确的冬季小麦产量预测对于全球粮食安全至关重要.
无人机空中遥感和植被指数已经提升了产量预测.
选择最佳的植被指数对于强大的产量预测模型至关重要.

研究的目的:

为植被指数开发一个综合特征选择方法 (PCRF-RFE).
为了提高冬季小麦产量预测模型的性能.
将PCRF-RFE方法的有效性与现有方法进行比较.

主要方法:

在开花和谷物填充阶段获得的多光谱图像.
确定了35个产量敏感的多谱指数.
应用了皮尔森相关系数 (PC) 和随机森林重要性 (RF) 来进行特征选择,其次是递归特征消除 (RFE).
使用选定的特征构建立体和循环神经网络 (RNN) 模型.

主要成果:

综合PCRF-RFE方法确定了植被指数特征的最佳子集.
使用PCRF-RFE选定的特征的立体模型,与RNN模型相比,显示出更高的精度.
PCRF-RFE方法的性能优于使用所有特征或单一方法特征选择的模型.

结论:

PCRF-RFE方法有效地选择了最佳的植被指数特征,用于产量预测.
用PCRF-RFE优化的立体模型提供了非常准确的冬季小麦产量预测.
这项研究为未来的特征选择和农业产量预测研究提供了宝贵的见解.

关键词:

立体主义的立体主义者.这就是PCRF-RFE.一个RNN RNN 多光谱的多光谱的业绩表现表现的表现表现是什么

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