Evaluation of Soil Properties, Topographic Metrics, Plant Height, and Unmanned Aerial Vehicle Multispectral Imagery Using Machine Learning Methods to Estimate Canopy Nitrogen Weight in Corn

Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, vegetation indices (VI), crop height, field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m2) of a corn field in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models were evaluated for canopy nitrogen weight prediction from 29 variables. RF consistently had better performance than SVR, and the top-performing validation model was RF using 15 selected height, spectral, and topographic variables with an R2 of 0.73 and Root Mean Square Error (RMSE) of 2.21 g/m2. Of the model’s 15 variables, crop height was the most important predictor, followed by 10 VIs, three MicaSense band reflectance mosaics (blue, red, and green), and topographic profile curvature. The model information can be used to improve field nitrogen prediction, leading to more effective and efficient N fertilizer management.

[1]  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.

[2]  Heather McNairn,et al.  Estimation of Crop Biomass and Leaf Area Index from Multitemporal and Multispectral Imagery Using Machine Learning Approaches , 2020, Canadian Journal of Remote Sensing.

[3]  Earl D. Vories,et al.  Corn response to nitrogen is influenced by soil texture and weather , 2012 .

[4]  Randal K. Taylor,et al.  RED EDGE AS A POTENTIAL INDEX FOR DETECTING DIFFERENCES IN PLANT NITROGEN STATUS IN WINTER WHEAT , 2012 .

[5]  T. R. Ellsworth,et al.  Need for a Soil‐Based Approach in Managing Nitrogen Fertilizers for Profitable Corn Production , 2006 .

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

[7]  A. Good,et al.  Can less yield more? Is reducing nutrient input into the environment compatible with maintaining crop production? , 2004, Trends in plant science.

[8]  W. D. Reynolds,et al.  Impacts of Recent Climate Trends on Agriculture in Southwestern Ontario , 2003 .

[9]  N. E. Hansen,et al.  Conservation Agriculture in North America , 2015 .

[10]  Gyles W. Randall,et al.  Corn Production as Affected by Nitrogen Application Timing and Tillage , 2004 .

[11]  D. Mulla.  Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[12]  F. Daniel-Vedele,et al.  REVIEW: PART OF A SPECIAL ISSUE ON PLANT NUTRITION Nitrogen uptake, assimilation and remobilization in plants: challenges for sustainable and productive agriculture , 2010 .

[13]  Craig S. T. Daughtry,et al.  Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring , 2010, Remote. Sens..

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

[15]  Ram L. Ray,et al.  Applications of Remote Sensing in Precision Agriculture: A Review , 2020, Remote. Sens..

[16]  William R. Raun,et al.  By‐Plant Prediction of Corn Forage Biomass and Nitrogen Uptake at Various Growth Stages Using Remote Sensing and Plant Height , 2007 .

[17]  Michael Bock,et al.  System for Automated Geoscientific Analyses (SAGA) v. 2.1.4 , 2015 .

[18]  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 .

[19]  Jonathan A. Patz,et al.  Reactive Nitrogen and Human Health:Acute and Long-term Implications , 2002, Ambio.

[20]  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 .

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

[22]  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.

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

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

[25]  Yang Song,et al.  Winter Wheat Canopy Height Extraction from UAV-Based Point Cloud Data with a Moving Cuboid Filter , 2019, Remote. Sens..

[26]  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.

[27]  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..

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

[29]  E. Justes,et al.  Determination of a Critical Nitrogen Dilution Curve for Winter Wheat Crops , 1994 .

[30]  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 .

[31]  Barry Smit,et al.  Adaptation in Canadian Agriculture to Climatic Variability and Change , 2000 .

[32]  Weixing Cao,et al.  Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice , 2008, Int. J. Appl. Earth Obs. Geoinformation.

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

[34]  J. Eitel,et al.  Using in‐situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status , 2007 .

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

[36]  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..

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

[38]  D. Beegle,et al.  Evaluating Multiple Indices from a Canopy Reflectance Sensor to Estimate Corn N Requirements , 2008 .

[39]  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.