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Dynamic Influence Elimination and Chlorophyll Content Diagnosis of Maize Using UAV Spectral Imagery

https://doi.org/10.3390/rs12162650
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s11119-018-9600-7openalex-snapshot
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inferredcites →https://doi.org/10.1016/0034-4257(94)00114-3openalex-snapshot
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Optimization of soil-adjusted vegetation indices
inferredcites →https://doi.org/10.1016/j.agrformet.2008.03.005openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.compag.2013.04.010openalex-snapshot
Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model
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inferredcites →https://doi.org/10.1016/j.compag.2015.03.019openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.compag.2016.04.024openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.fcr.2013.12.018openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.rse.2007.11.001openalex-snapshot
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inferredcites →https://doi.org/10.1029/2005gl022688openalex-snapshot
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inferredcites →https://doi.org/10.1078/0176-1617-00887openalex-snapshot
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inferredcites →https://doi.org/10.1080/0143116042000274015openalex-snapshot
The MERIS terrestrial chlorophyll index
inferredcites →https://doi.org/10.1080/07038992.1996.10855178openalex-snapshot
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inferredcites →https://doi.org/10.1186/s13007-020-00567-8openalex-snapshot
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.1007/s40003-025-00923-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1016/b978-0-443-13220-9.00073-1openalex-snapshot
Vegetation Trait Mapping With Optical Remote Sensing: Recent Advances in Methods and Applications
inferred← citeshttps://doi.org/10.1016/j.atech.2022.100067openalex-snapshot
Evaluating biostimulants via high-throughput field phenotyping: Biophysical traits retrieval through PROSAIL inversion
inferred← citeshttps://doi.org/10.1016/j.atech.2025.100894openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.atech.2025.101107openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compag.2021.106260openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compag.2021.106413openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compag.2021.106603openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compag.2022.106775openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compag.2022.106802openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compag.2022.106999openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compag.2022.107358openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compag.2022.107559openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compag.2023.108405openalex-snapshot
Solar-induced chlorophyll fluorescence extraction based on heterogeneous light distribution for improving in-situ chlorophyll content estimation
inferred← citeshttps://doi.org/10.1016/j.compag.2024.109361openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compag.2024.109621openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.compag.2025.110040openalex-snapshot
Diagnosis of leaf chlorophyll content based on close-range multispectral fluorescence image correction
inferred← citeshttps://doi.org/10.1016/j.compag.2025.110147openalex-snapshot
Estimating leaf and canopy nitrogen contents in major field crops across the growing season from hyperspectral images using nonparametric regression
inferred← citeshttps://doi.org/10.1016/j.compag.2025.110523openalex-snapshot
Precision monitoring of rice nitrogen fertilizer levels based on machine learning and UAV multispectral imagery
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2023.102190openalex-snapshot
Co-inversion of island leaf area index combination morphological and spectral parameters based on UAV multi-source remote sensing data
inferred← citeshttps://doi.org/10.1016/j.ecoinf.2024.102622openalex-snapshot
Rice leaf chlorophyll content estimation with different crop coverages based on Sentinel-2
inferred← citeshttps://doi.org/10.1016/j.fcr.2022.108739openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.fcr.2023.108929openalex-snapshot
Improving chlorophyll content detection to suit maize dynamic growth effects by deep features of hyperspectral data
inferred← citeshttps://doi.org/10.1016/j.indcrop.2024.119147openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.indcrop.2025.121345openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.pce.2023.103537openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.scienta.2024.112940openalex-snapshot
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inferred← citeshttps://doi.org/10.1039/d6ay00404kopenalex-snapshot
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inferred← citeshttps://doi.org/10.1079/cabireviews.2025.0012openalex-snapshot
Spectral imaging in crop monitoring and disease diagnosis: A comprehensive review
inferred← citeshttps://doi.org/10.1093/plphys/kiab322openalex-snapshot
Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms
inferred← citeshttps://doi.org/10.1109/access.2022.3230355openalex-snapshot
Study on Deep Learning Model for Online Estimation of Chlorophyll Content Based on Near Ground Multispectral Feature Bands
inferred← citeshttps://doi.org/10.13080/z-a.2021.108.024openalex-snapshot
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inferred← citeshttps://doi.org/10.1515/opag-2022-0357openalex-snapshot
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inferred← citeshttps://doi.org/10.1590/1413-7054202650006126openalex-snapshot
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inferred← citeshttps://doi.org/10.20868/upm.thesis.72375openalex-snapshot
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inferred← citeshttps://doi.org/10.20944/preprints202404.0311.v1openalex-snapshot
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inferred← citeshttps://doi.org/10.21203/rs.3.rs-3560039/v1openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fpls.2021.609876openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fpls.2021.730181openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fpls.2024.1419316openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fpls.2024.1445490openalex-snapshot
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inferred← citeshttps://doi.org/10.3389/fpls.2026.1851992openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/agriculture13051004openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/agriculture14050711openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/agriculture14112004openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/agriengineering6030177openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/agriengineering7070201openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/agronomy12102318openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/agronomy13081994openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/agronomy16151444openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/drones8100578openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/drones8120783openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/rs13112139openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/rs14010136openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/rs14164083openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/rs14225712openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/rs14246290openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/rs15020354openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/rs15133454openalex-snapshot
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inferred← citeshttps://doi.org/10.3390/su16188011openalex-snapshot
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