Using Linear Regression, Random Forests, and Support Vector Machine with Unmanned Aerial Vehicle Multispectral Images to Predict Canopy Nitrogen Weight in Corn

[1]  Tiantian Wang,et al.  Improving Unmanned Aerial Vehicle Remote Sensing-Based Rice Nitrogen Nutrition Index Prediction with Machine Learning , 2020, Remote. Sens..

[2]  A. Gitelson,et al.  Detection of Red Edge Position and Chlorophyll Content by Reflectance Measurements Near 700 nm , 1996 .

[3]  L. Deng,et al.  UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[4]  Mohammad El-Hajj,et al.  Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping , 2019, Remote. Sens..

[5]  Francesco Pirotti,et al.  Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques , 2019, Remote. Sens..

[6]  Susan L. Ustin,et al.  Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices , 2014 .

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

[8]  J. Dungan,et al.  Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. , 1990, Tree physiology.

[9]  Jaume Lloveras,et al.  Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard UAV Service , 2016, Remote. Sens..

[10]  N. Broge,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2001 .

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

[12]  R. Fernandes,et al.  Landsat-5 TM and Landsat-7 ETM+ based accuracy assessment of leaf area index products for Canada derived from SPOT-4 VEGETATION data , 2003 .

[13]  John R. Miller,et al.  Vegetation stress detection through chlorophyll a + b estimation and fluorescence effects on hyperspectral imagery. , 2002, Journal of environmental quality.

[14]  Arko Lucieer,et al.  Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery , 2012, Remote. Sens..

[15]  Hao Yang,et al.  Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives , 2017, Front. Plant Sci..

[16]  Wenshan Guo,et al.  Estimation of leaf nitrogen concentration in wheat using the MK-SVR algorithm and satellite remote sensing data , 2017, Comput. Electron. Agric..

[17]  Arko Lucieer,et al.  Direct Georeferencing of Ultrahigh-Resolution UAV Imagery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Mahmoud Omid,et al.  Multispectral remote sensing for site-specific nitrogen fertilizer management , 2013 .

[19]  C. François,et al.  Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements , 2004 .

[20]  Yanjie Wang,et al.  Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models , 2017, Remote. Sens..

[21]  J. Watmough,et al.  Non-destructive estimation of wheat leaf chlorophyll content from hyperspectral measurements through analytical model inversion , 2010 .

[22]  Sofia Bajocco,et al.  A bibliometric analysis on the use of unmanned aerial vehicles in agricultural and forestry studies , 2019, International Journal of Remote Sensing.

[23]  X. Yao,et al.  Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance , 2011 .

[24]  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.

[25]  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..

[26]  Chunhua Zhang,et al.  The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.

[27]  A. Gitelson,et al.  Remote estimation of crop fractional vegetation cover: the use of noise equivalent as an indicator of performance of vegetation indices , 2013 .

[28]  Simon Bennertz,et al.  Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[29]  Wenjiang Huang,et al.  Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

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

[31]  Wei Li,et al.  A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle , 2018, Remote. Sens..

[32]  Masahiko Nagai,et al.  Estimating Canopy Nitrogen Concentration in Sugarcane Using Field Imaging Spectroscopy , 2012, Remote. Sens..

[33]  Brigitte Leblon,et al.  Intra-Field Canopy Nitrogen Retrieval from Unmanned Aerial Vehicle Imagery for Wheat and Corn Fields , 2020, Canadian Journal of Remote Sensing.

[34]  Anatoly A. Gitelson,et al.  Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels , 2013, Int. J. Appl. Earth Obs. Geoinformation.

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

[36]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[37]  Yong Liu,et al.  Comparative analysis of vegetation indices, non-parametric and physical retrieval methods for monitoring nitrogen in wheat using UAV-based multispectral imagery , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[38]  J. Chen Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal Applications , 1996 .

[39]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

[40]  A. Calera,et al.  Remote sensing-based crop biomass with water or light-driven crop growth models in wheat commercial fields , 2018 .

[41]  D. Haboudane,et al.  New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat , 2010 .

[42]  Fei Li,et al.  Estimating N status of winter wheat using a handheld spectrometer in the North China Plain , 2008 .

[43]  Danfeng Huang,et al.  Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for Brassica Campestris ssp. Chinensis L. , 2019, Sensors.

[44]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[45]  Fumin Wang,et al.  New Vegetation Index and Its Application in Estimating Leaf Area Index of Rice , 2007 .

[46]  A. Gitelson,et al.  Remote sensing of chlorophyll concentration in higher plant leaves , 1998 .

[47]  F. Maupas,et al.  Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping , 2017 .

[48]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[49]  Arko Lucieer,et al.  Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing , 2012, Remote. Sens..

[50]  Jan G. P. W. Clevers,et al.  Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[51]  Moon S. Kim,et al.  Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance , 2000 .

[52]  J. Roujean,et al.  Estimating PAR absorbed by vegetation from bidirectional reflectance measurements , 1995 .

[53]  Hao Yang,et al.  Hyperspectral-Based Estimation of Leaf Nitrogen Content in Corn Using Optimal Selection of Multiple Spectral Variables , 2019, Sensors.

[54]  Jan G. P. W. Clevers,et al.  Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3 , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[55]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[56]  Mohsen Shahhosseini,et al.  Maize yield and nitrate loss prediction with machine learning algorithms , 2019, Environmental Research Letters.

[57]  Anka Lisec,et al.  Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments , 2018, ISPRS Int. J. Geo Inf..

[58]  J. Schjoerring,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[59]  G. V. G. Baranoski,et al.  A practical approach for estimating the red edge position of plant leaf reflectance , 2005 .

[60]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[61]  P. Stephen Baenziger,et al.  Evaluating canopy spectral reflectance vegetation indices to estimate nitrogen use traits in hard winter wheat , 2018 .

[62]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .