ReviewPlant phenomics (Washington, D.C.)2026
Leveraging UAV hyperspectral imaging for crop physiology and biochemistry: A comprehensive review of feature extraction and selection methods.
Review in Plant phenomics (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Estimation of chlorophyll content in cotton leaves using UAV RGB imagery.Frontiers in plant science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.