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

Ittai Herrmann*, Eyal Bdolach, Yogev Montekyo, Shimon Rachmilevitch, Philip A. Townsend, Arnon Karnieli

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

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.

Original languageAmerican English
Pages (from-to)51-76
Number of pages26
JournalPrecision Agriculture
Volume21
Issue number1
DOIs
StatePublished - 1 Feb 2020

Bibliographical note

Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

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

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