Leveraging UAV hyperspectral imaging for crop physiology and biochemistry: A comprehensive review of feature extraction and selection methods

Crop physiological and nutrient biochemical information plays a vital role in uncovering patterns of crop growth and development, as well as understanding their interactions with environmental factors. Unmanned aerial vehicles (UAV) -based hyperspectral imaging (HSI) technology offers an innovative tool for acquiring physiological and nutrient biochemical information through non-destructive and rapid collection of continuous spectral data from crops. However, challenges such as low signal-to-noise ratios (SNR), spectral variability for the same material, and high dimensionality in hyperspectral data make feature selection and extraction critical steps in data processing and analysis. Therefore, this review focuses on feature selection and extraction methods in the application of UAV-based hyperspectral technology for retrieving and monitoring crop physiological and biochemical information, providing theoretical support for its use in agriculture. Firstly, it provides a detailed discussion of feature selection methods, including filter-based, wrapper-based, and embedded approaches, along with various feature extraction techniques, analyzing their applicability and limitations in crop retrieving and monitoring. Secondly, the review highlights the use of vegetation indices (VIs) in feature extraction, covering advancements from basic indices to those optimized for specific applications. Finally, the article summarizes the main challenges of existing methods, particularly the issues of high-dimensional data processing and noise, and outlines potential future directions. This review highlights the significance of feature selection and extraction methods as critical tools for efficiently processing hyperspectral data. Through systematic analysis and synthesis, it provides theoretical support for agricultural researchers and practitioners while underscoring the importance of these techniques in driving innovation and advancements in hyperspectral technology.