UAV‐based imaging platform for monitoring maize growth throughout development

Plant height (PH) data collected at high temporal resolutions can give insight into important growth parameters useful for identifying elite material in plant breeding programs and developing management guidelines in production settings. However, in order to increase the temporal resolution of PH data collection, more robust, rapid and low-cost methods are needed to evaluate field plots than those currently available. Due to their low cost and high functionality, unmanned aerial vehicles (UAVs) can be an efficient means for collecting height at various stages throughout development. We have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery and have used this platform to characterize a yield trial consisting of 24 maize hybrids planted in replicate under two dates and three planting densities in St Paul, MN in the summer of 2018. The field was imaged weekly after planting using a DJI Phantom 4 Advanced drone to extract PH and hand measurements were collected following aerial imaging of the field. In this work, we test the error in UAV PH measurements and compare it to the error obtained within manually acquired PH measurements. We also propose a method for improving the correspondence of manual and UAV measured height and evaluate the utility of using UAV obtained PH data for assessing growth of maize genotypes and for estimating end-season height.

[1]  S. Paulus,et al.  Measuring crops in 3D: using geometry for plant phenotyping , 2019, Plant Methods.

[2]  Hao Guo,et al.  Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images , 2019, Remote. Sens..

[3]  S. Popescu,et al.  Prediction of Maize Grain Yield before Maturity Using Improved Temporal Height Estimates of Unmanned Aerial Systems , 2019, The Plant Phenome Journal.

[4]  Jianfeng Zhou,et al.  Cotton Yield Estimation based on Plant Height From UAV-based Imagery Data , 2018 .

[5]  Sukhwinder K. Bali,et al.  A Review of Methods to Improve Nitrogen Use Efficiency in Agriculture , 2017 .

[6]  F. Baret,et al.  High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates , 2017, Front. Plant Sci..

[7]  Jinha Jung,et al.  Crop height monitoring with digital imagery from Unmanned Aerial System (UAS) , 2017, Comput. Electron. Agric..

[8]  Yared Assefa,et al.  Spatio-temporal evaluation of plant height in corn via unmanned aerial systems , 2017 .

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

[10]  Nathan D. Miller,et al.  Genotype-by-environment interactions affecting heterosis in maize , 2017, bioRxiv.

[11]  Alencar Xavier,et al.  Genetic Architecture of Phenomic-Enabled Canopy Coverage in Glycine max , 2017, Genetics.

[12]  Wei Guo,et al.  High-Throughput Phenotyping of Sorghum Plant Height Using an Unmanned Aerial Vehicle and Its Application to Genomic Prediction Modeling , 2017, Front. Plant Sci..

[13]  Martin J. Wooster,et al.  High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing , 2016, Remote. Sens..

[14]  Z. Niu,et al.  Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system , 2016 .

[15]  J. Alex Thomasson,et al.  Corn and sorghum phenotyping using a fixed-wing UAV-based remote sensing system , 2016, SPIE Commercial + Scientific Sensing and Imaging.

[16]  Pablo J. Zarco-Tejada,et al.  High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials , 2015, Remote. Sens..

[17]  G. Grenzdörffer Crop height determination with UAS point clouds , 2014 .

[18]  Sebastian G. Elbaum,et al.  On crop height estimation with UAVs , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Simon Bennertz,et al.  Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging , 2014, Remote. Sens..

[20]  Johanna Link,et al.  Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System , 2014, Remote. Sens..

[21]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[22]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[23]  F. López-Granados,et al.  Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat , 2013, Precision Agriculture.

[24]  S. Robson,et al.  Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application , 2012 .

[25]  Robert M. Hayes,et al.  Comparison of Models in Assessing Relationship of Corn Yield with Plant Height Measured during Early- to Mid-Season , 2011 .

[26]  D. Tyler,et al.  In-Season Prediction of Corn Yield Using Plant Height under Major Production Systems , 2011 .

[27]  Sanford Weisberg,et al.  An R Companion to Applied Regression , 2010 .

[28]  Harold M. van Es,et al.  Spatial Yield Response of Two Corn Hybrids at Two Nitrogen Levels , 2003 .

[29]  Larry C. Purcell,et al.  Soybean Canopy Coverage and Light Interception Measurements Using Digital Imagery , 2000 .

[30]  F. P. Gardner,et al.  Responses of Maize to Plant Population Density. I. Canopy Development, Light Relationships, and Vegetative Growth , 1988 .