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Assessment of maize yield and phenology by drone-mounted superspectral camera

  • Ittai HerrmannEmail author
  • Eyal Bdolach
  • Yogev Montekyo
  • Shimon Rachmilevitch
  • Philip A. Townsend
  • Arnon Karnieli
Article
  • 127 Downloads

Abstract

The capability of unmanned aerial vehicle (UAV) spectral imagery to assess maize yield under full and deficit irrigation is demonstrated by a Tetracam MiniMCA12 11 bands camera. The MiniMCA12 was used to image an experimental field of 19 maize hybrids. Yield prediction models were explored for different maize development stages, with the best model found using maize plant development stage reproductive 2 (R2) for both maize grain yield and ear weight (respective R2 values of 0.73 and 0.49, and root mean square error of validation (RMSEV) values of 2.07 and 3.41 metric tons per hectare using partial least squares regression (PLS-R) validation models). Models using vegetation indices for inputs rather than superspectral data showed similar R2 but higher RMSEV values, and produced best results for the R4 development stage. In addition to being able to predict yield, spectral models were able to distinguish between different development stages and irrigation treatments. These abilities potentially allow for yield prediction of maize plants whose development stage and water status are unknown.

Keywords

Maize Yield assessment Phenotyping Partial least squares UAV VENμS 

Abbreviations

ASD

Analytical spectral devices

CC

Canopy cover

CMOS

Complementary metal oxide semiconductor

CO2

Carbon dioxide

GCPs

Ground control points

GNDVI

Green Normalized Difference Vegetation Index

GNSS

Global navigation satellite system

ILS

Incident light sensor

LAI

Leaf area index

NDREI

Normalized Difference Red-Edge Index

NDVI

Normalized Difference Vegetation Index

NGRDI

Normalized Green Red Difference Index

NIR

Near-infrared

OSAVI

Optimized soil adjusted vegetation index

PAR

Photosynthetically active radiation

PLS-DA

Partial least squares discriminant analysis

PLS-R

PLS regression

PW2

PixelWrench2

R

Reproductive

R2

Coefficient of determination

RARSa

Ratio analysis of reflectance spectra chlorophyll a

RARSb

Ratio analysis of reflectance spectra chlorophyll b

RARSc

Ratio analysis of reflectance spectra carotenoid

REIP

Red-edge inflection point

RGB

Red, green and blue

RMSE

Root mean square error

RMSEC

RMSE for calibration

RMSECV

RMSE for cross validation

RMSEV

RMSEC for validation

rRMSE

Relative RMSE

RTK

Real time kinematic

RWC

Relative water content

SIPI

Structure insensitive pigment index

SR

Simple ratio

t/ha

Tons per hectare

TCARI

Transformed Chlorophyll Absorption Reflectance Index

TGI

Triangular Greenness Index

TVI

Triangular Vegetation Index

UAV

Unmanned aerial vehicles

V

Vegetative

VENμS

Vegetation and Environmental New micro Spacecraft

VIP

Variable importance in projection

VIs

Vegetation Indices

VT

Vegetative tasseling

Notes

Acknowledgments

This research was supported by the Israeli Ministry of Agriculture and Rural Development (Eugene Kandel Knowledge Centers) as part of the Root of the Matter - The root zone knowledge center for leveraging modern agriculture (Contract No. 16-34-0005). The postdoctoral Pratt foundation partially supported Ittai Herrmann. The Townsend lab received support from USDA Hatch funding (Project WIS01874). The authors would like to thank: Alexander Goldberg for all his help in the field and much beyond; Offir Matsrafi for his long term and long distance GIS support; Michael Travis from the University of Wisconsin-Extension, Pepin County for sharing his knowhow regarding corn cultivation in the Midwest; Aditya Singh for his insights; Ben Spaier for his comments, questions and proofreading; Evogene Ltd.: agronomist Mor Manor and his team; phenotyping team, led by Raanan Ganor; sampling team, led by Sara Koretzki; data and imaging team, led by Yogev Montekyo; and R&D Researchers that helped and supported planning and management, especially Inbal Dangoor, Ronit Rimon Knopf and Alon Glick.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11119_2019_9659_MOESM1_ESM.xlsx (39 kb)
Supplementary material 1 (XLSX 39 kb)

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Copyright information

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Authors and Affiliations

  1. 1.The Robert H. Smith Institute of Plant Sciences and Genetics in AgricultureHebrew University of JerusalemRehovotIsrael
  2. 2.The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevBeershebaIsrael
  3. 3.Evogene Ltd.RehovotIsrael
  4. 4.French Associates Institute for Agriculture and Biotechnology of Drylands, The Jacob Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevBeershebaIsrael
  5. 5.Department of Forest & Wildlife EcologyUniversity of Wisconsin-MadisonMadisonUSA

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