Publication result detail

Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

JANOUŠEK, J.; MARCOŇ, P.; DOHNAL, P.; JAMBOR, V.; SYNKOVÁ, H.; RAICHL, P.

Original Title

Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

English Title

Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging

Type

WoS Article

Original Abstract

Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (NDRE), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic.

English abstract

Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (NDRE), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic.

Keywords

corn; multispectral imaging; vegetation indices; nutritional analysis; correlation; photogrammetry; optimal harvest time; UAV

Key words in English

corn; multispectral imaging; vegetation indices; nutritional analysis; correlation; photogrammetry; optimal harvest time; UAV

Authors

JANOUŠEK, J.; MARCOŇ, P.; DOHNAL, P.; JAMBOR, V.; SYNKOVÁ, H.; RAICHL, P.

RIV year

2024

Released

16.06.2023

Publisher

MDPI

Location

BASEL

ISBN

2072-4292

Periodical

Remote Sensing

Volume

15

Number

12

State

Swiss Confederation

Pages from

1

Pages to

19

Pages count

19

URL

https://www.mdpi.com/2072-4292/15/12/3152

Full text in the Digital Library

http://hdl.handle.net/11012/213600

BibTex

@article{BUT183996,
  author="Jiří {Janoušek} and Petr {Marcoň} and Přemysl {Dohnal} and Václav {Jambor} and Hana {Synková} and Petr {Raichl}",
  title="Predicting the Optimum Corn Harvest Time via the Quantity of Dry Matter Determined with Vegetation Indices Obtained from Multispectral Field Imaging",
  journal="Remote Sensing",
  year="2023",
  volume="15",
  number="12",
  pages="1--19",
  doi="10.3390/rs15123152",
  url="https://www.mdpi.com/2072-4292/15/12/3152"
}

Documents

remotesensing-15-03152