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


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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Analytical spectral devices


Canopy cover


Complementary metal oxide semiconductor

CO2 :

Carbon dioxide


Ground control points


Green Normalized Difference Vegetation Index


Global navigation satellite system


Incident light sensor


Leaf area index


Normalized Difference Red-Edge Index


Normalized Difference Vegetation Index


Normalized Green Red Difference Index




Optimized soil adjusted vegetation index


Photosynthetically active radiation


Partial least squares discriminant analysis


PLS regression





R2 :

Coefficient of determination


Ratio analysis of reflectance spectra chlorophyll a


Ratio analysis of reflectance spectra chlorophyll b


Ratio analysis of reflectance spectra carotenoid


Red-edge inflection point


Red, green and blue


Root mean square error


RMSE for calibration


RMSE for cross validation


RMSEC for validation


Relative RMSE


Real time kinematic


Relative water content


Structure insensitive pigment index


Simple ratio


Tons per hectare


Transformed Chlorophyll Absorption Reflectance Index


Triangular Greenness Index


Triangular Vegetation Index


Unmanned aerial vehicles




Vegetation and Environmental New micro Spacecraft


Variable importance in projection


Vegetation Indices


Vegetative tasseling


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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).

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