Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery

作者：Xiangxiang Su, Ying Nian, Hiba Shaghaleh, Amar Hamad, Hu Y, Yongji Zhu, Jun Li, Weiqiang Wang, Hong Wang, Qiang Ma, Jikai Liu, Xinwei Li, Yousef Alhaj Hamoud · 发表于：Frontiers in Plant Science · 年份：2024 · DOI：10.3389/fpls.2024.1404238 · 被引用次数：28 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Smart Agriculture and AI

The Soil Plant Analysis Development (SPAD) is a vital index for evaluating crop nutritional status and serves as an essential parameter characterizing the reproductive growth status of winter wheat. Non-destructive and accurate monitorin3g of winter wheat SPAD plays a crucial role in guiding precise management of crop nutrition. In recent years, the spectral saturation problem occurring in the later stage of crop growth has become a major factor restricting the accuracy of SPAD estimation. Therefore, the purpose of this study is to use features selection strategy to optimize sensitive remote sensing information, combined with features fusion strategy to integrate multiple characteristic features, in order to improve the accuracy of estimating wheat SPAD. This study conducted field experiments of winter wheat with different varieties and nitrogen treatments, utilized UAV multispectral sensors to obtain canopy images of winter wheat during the heading, flowering, and late filling stages, extracted spectral features and texture features from multispectral images, and employed features selection strategy (Boruta and Recursive Feature Elimination) to prioritize sensitive remote sensing features. The features fusion strategy and the Support Vector Machine Regression algorithm are applied to construct the SPAD estimation model for winter wheat. The results showed that the spectral features of NIR band combined with other bands can fully capture the spectral differences of winter whe...