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

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.

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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

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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.

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Herrmann, I., Bdolach, E., Montekyo, Y. et al. Assessment of maize yield and phenology by drone-mounted superspectral camera. Precision Agric 21, 51–76 (2020). https://doi.org/10.1007/s11119-019-09659-5

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Keywords

  • Maize
  • Yield assessment
  • Phenotyping
  • Partial least squares
  • UAV
  • VENμS