Multi-temporal UAV Imaging-Based Mapping of Chlorophyll Content in Potato Crop

[1]  Sung-Min Kim,et al.  Machine learning algorithms to predict flow boiling pressure drop in mini/micro-channels based on universal consolidated data , 2021 .

[2]  C. Webster,et al.  Estimating construction waste generation in the Greater Bay Area, China using machine learning. , 2021, Waste management.

[3]  Lei Zhang,et al.  Monitoring of peanut leaves chlorophyll content based on drone-based multispectral image feature extraction , 2021, Comput. Electron. Agric..

[4]  Thomas Udelhoven,et al.  Comparison of Crop Trait Retrieval Strategies Using UAV-Based VNIR Hyperspectral Imaging , 2021, Remote. Sens..

[5]  Zuzana Lhotáková,et al.  Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV) , 2021, Remote. Sens..

[6]  Ali Moghimi,et al.  A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery , 2020, Remote. Sens..

[7]  I. Mondragón,et al.  A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops , 2020, PloS one.

[8]  Jinfei Wang,et al.  Using Linear Regression, Random Forests, and Support Vector Machine with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy Nitrogen Weight in Corn , 2020, Remote. Sens..

[9]  Yu-Rong Zeng,et al.  Forecasting Tourist Arrivals via Random Forest and Long Short-term Memory , 2020, Cognitive Computation.

[10]  Changchun Li,et al.  Estimation of potato chlorophyll content using composite hyperspectral index parameters collected by an unmanned aerial vehicle , 2020 .

[11]  Jérôme Théau,et al.  Crop scouting using UAV imagery: a case study for potatoes , 2020 .

[12]  Jonathan Li,et al.  Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery , 2019, Remote. Sens..

[13]  Vittorio Casella,et al.  Geometric and Radiometric Consistency of Parrot Sequoia Multispectral Imagery for Precision Agriculture Applications , 2019, Applied Sciences.

[14]  Lini Mathew,et al.  Chlorophyll estimation using multi-spectral unmanned aerial system based on machine learning techniques , 2019, Remote Sensing Applications: Society and Environment.

[15]  Qian Du,et al.  Random forest–based estimation of heavy metal concentration in agricultural soils with hyperspectral sensor data , 2019, Environmental Monitoring and Assessment.

[16]  Luis Guanter,et al.  Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations , 2019, Remote Sensing of Environment.

[17]  A. B. M. Shawkat Ali,et al.  A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat , 2019, Remote. Sens..

[18]  Hang Lei,et al.  Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization , 2019 .

[19]  Yubin Lan,et al.  Real time estimation of chlorophyll content based on vegetation indices derived from multispectral UAV in the kinnow orchard , 2018 .

[20]  Eija Honkavaara,et al.  A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone , 2018 .

[21]  Wenjiang Huang,et al.  New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery , 2018, Sensors.

[22]  A. Qin,et al.  Exploring new spectral bands and vegetation indices for estimating nitrogen nutrition index of summer maize , 2018 .

[23]  Jan G. P. W. Clevers,et al.  Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop , 2017, Remote. Sens..

[24]  Xin Li,et al.  A new three-band spectral index for mitigating the saturation in the estimation of leaf area index in wheat , 2017 .

[25]  Xinkai Zhu,et al.  Estimation of biomass in wheat using random forest regression algorithm and remote sensing data , 2016 .

[26]  J. Clevers,et al.  Estimating potato leaf chlorophyll content using ratio vegetation indices , 2016 .

[27]  Méndez-Tejeda Rafael,et al.  Environmental and Economic Impact of Forest Fires in Puerto Rico 2013-2014 , 2015 .

[28]  Juliane Bendig,et al.  Low-weight and UAV-based Hyperspectral Full-frame Cameras for Monitoring Crops: Spectral Comparison with Portable Spectroradiometer Measurements , 2015 .

[29]  Georg Bareth,et al.  Estimating leaf chlorophyll of barley at different growth stages using spectral indices to reduce soil background and canopy structure effects , 2014 .

[30]  Juliane Bendig,et al.  UAV-based Imaging for Multi-Temporal, very high Resolution Crop Surface Models to monitor Crop Growth Variability , 2013 .

[31]  Georg Bareth,et al.  Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the Northeast China Plain , 2013 .

[32]  Conghe Song,et al.  Optical remote sensing of forest leaf area index and biomass , 2013 .

[33]  Yoshio Inoue,et al.  Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements , 2012 .

[34]  Onisimo Mutanga,et al.  High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[35]  Luis Alonso,et al.  Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3 , 2012 .

[36]  Weixing Cao,et al.  Estimating Leaf Chlorophyll Content Using Red Edge Parameters , 2010 .

[37]  Georg Bareth,et al.  Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages , 2010, Precision Agriculture.

[38]  Chaoyang Wu,et al.  Estimating chlorophyll content from hyperspectral vegetation indices : Modeling and validation , 2008 .

[39]  Bruno Mary,et al.  Elaboration of a nitrogen nutrition indicator for winter wheat based on leaf area index and chlorophyll content for making nitrogen recommendations , 2007 .

[40]  H. Pleijel,et al.  Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings , 2007, Photosynthesis Research.

[41]  J. G. White,et al.  Aerial Color Infrared Photography for Determining Early In‐Season Nitrogen Requirements in Corn , 2005 .

[42]  L. Sangoi,et al.  Leaf relative chlorophyll content as an indicator parameter to predict nitrogen fertilization in maize , 2004 .

[43]  Hartmut K. Lichtenthaler,et al.  The Chlorophyll Fluorescence Ratio F735/F700 as an Accurate Measure of the Chlorophyll Content in Plants , 1999 .

[44]  A. Gitelson,et al.  Quantitative estimation of chlorophyll-a using reflectance spectra : experiments with autumn chestnut and maple leaves , 1994 .

[45]  Compton J. Tucker,et al.  Monitoring corn and soybean crop development with hand-held radiometer spectral data , 1979 .

[46]  C. Jordan Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[47]  Lei Shi,et al.  Comparing Pixel-Based Random Forest and the Object-Based Support Vector Machine Approaches to Map the Quasi-Circular Vegetation Patches Using Individual Seasonal Fused GF-1 Imagery , 2020, IEEE Access.

[48]  Hong Sun,et al.  Estimation of Chlorophyll Content in Potato Leaves Based on Spectral Red Edge Position , 2018 .

[49]  Vishnu S. Pendyala,et al.  Machine Learning Algorithms , 2018, Optimization Techniques and Applications with Examples.

[50]  D. Martínez,et al.  Distortion of the SPAD 502 chlorophyll meter readings by changes in irradiance and leaf water status , 2004 .

[51]  J. Markwell,et al.  Calibration of the Minolta SPAD-502 leaf chlorophyll meter , 2004, Photosynthesis Research.

[52]  Yuri A. Gritz,et al.  Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. , 2003, Journal of plant physiology.

[53]  P. M. Hansena,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[54]  C. Langdon,et al.  FOR THE ESTIMATION OF CHLOROPHYLL A , 1993 .

[55]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[56]  S. Sundberg [On chlorophyll]. , 1950 .