Research graph
References from Comparative Evaluation of UAV Multispectral and Measured Biophysical Feature Combinations for Winter Wheat Canopy Nitrogen Estimation. Local targets link to admitted publications; unresolved targets remain external evidence.
Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review
10.1016/j.compag.2018.05.012 · 2018 · External reference
10.3389/fpls.2019.01601
10.3389/fpls.2019.01601 · External reference
10.3390/rs15112831
10.3390/rs15112831 · External reference
Developing an efficiency and energy-saving nitrogen management strategy for winter wheat based on the UAV multispectral imagery and machine learning algorithm
10.1007/s11119-023-10028-6 · 2023 · External reference
Remote sensing for agricultural applications: A meta-review
10.1016/j.rse.2019.111402 · 2020 · External reference
Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture
10.1016/j.tplants.2018.11.007 · 2019 · External reference
10.3390/info10110349
10.3390/info10110349 · External reference
Significant remote sensing vegetation indices: A review of developments and applications
10.1155/2017/1353691 · 2017 · External reference
10.3390/plants11131712
10.3390/plants11131712 · External reference
Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and −3
2013 · External reference
Critical nitrogen dilution curve for optimizing nitrogen management of winter wheat production in the North China Plain
10.2134/agronj2011.0258 · 2012 · External reference
10.3389/fpls.2020.549636
10.3389/fpls.2020.549636 · External reference
A global dataset to parametrize critical nitrogen dilution curves for major crop species
10.1038/s41597-022-01395-2 · 2022 · External reference
Evaluation of Sentinel-2 vegetation indices for prediction of LAI, fAPAR and fCover of winter wheat in Bulgaria
10.1080/22797254.2020.1839359 · 2021 · External reference
Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems
10.1016/j.rse.2018.06.037 · 2018 · External reference
10.3390/rs17030406
10.3390/rs17030406 · External reference
Combining fixed-wing UAV multispectral imagery and machine learning to diagnose winter wheat nitrogen status at the farm scale
10.1016/j.eja.2022.126537 · 2022 · External reference
10.3390/agriculture15131373
10.3390/agriculture15131373 · External reference
Combining spectrum, thermal, and texture features using machine learning algorithms for wheat nitrogen nutrient index estimation and model transferability analysis
10.1016/j.compag.2024.109022 · 2024 · External reference
Application of unmanned aerial vehicle optical remote sensing in crop nitrogen diagnosis: A systematic literature review
10.1016/j.compag.2024.109565 · 2024 · External reference
Unresolved reference
External reference
10.3390/data11020035
10.3390/data11020035 · External reference
A review of variable selection methods in partial least squares regression
10.1016/j.chemolab.2012.07.010 · 2012 · External reference
10.1007/978-1-4302-5990-9
10.1007/978-1-4302-5990-9 · External reference
A random forest guided tour
10.1007/s11749-016-0481-7 · 2016 · External reference
10.1145/2939672.2939785
10.1145/2939672.2939785 · External reference
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
10.1016/j.neunet.2017.12.012 · 2018 · External reference
Dropout: A simple way to prevent neural networks from overfitting
2014 · External reference
Unresolved reference
External reference
The RPD statistic: A tutorial note
10.1255/nirn.1419 · 2014 · External reference
10.3390/agriculture14071064
10.3390/agriculture14071064 · External reference
10.3389/fpls.2024.1367828
10.3389/fpls.2024.1367828 · External reference
10.3390/rs17030498
10.3390/rs17030498 · External reference
10.3390/agriculture14101775
10.3390/agriculture14101775 · External reference
10.3390/agriculture15151624
10.3390/agriculture15151624 · External reference
10.3390/agronomy15010159
10.3390/agronomy15010159 · External reference
10.3390/agriculture15030353
10.3390/agriculture15030353 · External reference
10.3389/fpls.2025.1709459
10.3389/fpls.2025.1709459 · External reference
Prediction of winter wheat nitrogen status using UAV imagery, weather data, and machine learning
10.1016/j.eja.2025.127534 · 2025 · External reference
Improving winter wheat plant nitrogen concentration prediction by combining proximal hyperspectral sensing and weather information with machine learning
10.1016/j.compag.2025.110072 · 2025 · External reference
10.3390/agronomy15112610
10.3390/agronomy15112610 · External reference
10.1007/978-1-4302-5990-9
10.1007/978-1-4302-5990-9 · ExternalCitation · doi-reference
Developing an efficiency and energy-saving nitrogen management strategy for winter wheat based on the UAV multispectral imagery and machine learning algorithm
10.1007/s11119-023-10028-6 · ExternalCitation · doi-reference
A random forest guided tour
10.1007/s11749-016-0481-7 · ExternalCitation · doi-reference
A review of variable selection methods in partial least squares regression
10.1016/j.chemolab.2012.07.010 · ExternalCitation · doi-reference
Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review
10.1016/j.compag.2018.05.012 · ExternalCitation · doi-reference
Combining spectrum, thermal, and texture features using machine learning algorithms for wheat nitrogen nutrient index estimation and model transferability analysis
10.1016/j.compag.2024.109022 · ExternalCitation · doi-reference
Application of unmanned aerial vehicle optical remote sensing in crop nitrogen diagnosis: A systematic literature review
10.1016/j.compag.2024.109565 · ExternalCitation · doi-reference
Improving winter wheat plant nitrogen concentration prediction by combining proximal hyperspectral sensing and weather information with machine learning
10.1016/j.compag.2025.110072 · ExternalCitation · doi-reference
Combining fixed-wing UAV multispectral imagery and machine learning to diagnose winter wheat nitrogen status at the farm scale
10.1016/j.eja.2022.126537 · ExternalCitation · doi-reference
Prediction of winter wheat nitrogen status using UAV imagery, weather data, and machine learning
10.1016/j.eja.2025.127534 · ExternalCitation · doi-reference
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
10.1016/j.neunet.2017.12.012 · ExternalCitation · doi-reference
Retrieval of the canopy chlorophyll content from Sentinel-2 spectral bands to estimate nitrogen uptake in intensive winter wheat cropping systems
10.1016/j.rse.2018.06.037 · ExternalCitation · doi-reference
Remote sensing for agricultural applications: A meta-review
10.1016/j.rse.2019.111402 · ExternalCitation · doi-reference
Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture
10.1016/j.tplants.2018.11.007 · ExternalCitation · doi-reference
A global dataset to parametrize critical nitrogen dilution curves for major crop species
10.1038/s41597-022-01395-2 · ExternalCitation · doi-reference
Evaluation of Sentinel-2 vegetation indices for prediction of LAI, fAPAR and fCover of winter wheat in Bulgaria
10.1080/22797254.2020.1839359 · ExternalCitation · doi-reference
10.1145/2939672.2939785
10.1145/2939672.2939785 · ExternalCitation · doi-reference
Significant remote sensing vegetation indices: A review of developments and applications
10.1155/2017/1353691 · ExternalCitation · doi-reference
The RPD statistic: A tutorial note
10.1255/nirn.1419 · ExternalCitation · doi-reference
Critical nitrogen dilution curve for optimizing nitrogen management of winter wheat production in the North China Plain
10.2134/agronj2011.0258 · ExternalCitation · doi-reference
10.3389/fpls.2019.01601
10.3389/fpls.2019.01601 · ExternalCitation · doi-reference
10.3389/fpls.2020.549636
10.3389/fpls.2020.549636 · ExternalCitation · doi-reference
10.3389/fpls.2024.1367828
10.3389/fpls.2024.1367828 · ExternalCitation · doi-reference
10.3389/fpls.2025.1709459
10.3389/fpls.2025.1709459 · ExternalCitation · doi-reference
10.3390/agriculture14071064
10.3390/agriculture14071064 · ExternalCitation · doi-reference
10.3390/agriculture14101775
10.3390/agriculture14101775 · ExternalCitation · doi-reference
10.3390/agriculture15030353
10.3390/agriculture15030353 · ExternalCitation · doi-reference
10.3390/agriculture15131373
10.3390/agriculture15131373 · ExternalCitation · doi-reference
10.3390/agriculture15151624
10.3390/agriculture15151624 · ExternalCitation · doi-reference
10.3390/agronomy15010159
10.3390/agronomy15010159 · ExternalCitation · doi-reference
10.3390/agronomy15112610
10.3390/agronomy15112610 · ExternalCitation · doi-reference
10.3390/data11020035
10.3390/data11020035 · ExternalCitation · doi-reference
10.3390/info10110349
10.3390/info10110349 · ExternalCitation · doi-reference
10.3390/plants11131712
10.3390/plants11131712 · ExternalCitation · doi-reference
10.3390/rs15112831
10.3390/rs15112831 · ExternalCitation · doi-reference
10.3390/rs17030406
10.3390/rs17030406 · ExternalCitation · doi-reference
10.3390/rs17030498
10.3390/rs17030498 · ExternalCitation · doi-reference
