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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148495" target="_blank" >RIV/00216305:26220/23:PU148495 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2072-4292/15/12/3152" target="_blank" >https://www.mdpi.com/2072-4292/15/12/3152</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/rs15123152" target="_blank" >10.3390/rs15123152</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20204 - Robotics and automatic control

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Remote Sensing

ISSN

2072-4292

e-ISSN

—

Volume of the periodical

15

Issue of the periodical within the volume

12

Country of publishing house

CH - SWITZERLAND

Number of pages

19

Pages from-to

1-19

UT code for WoS article

001018332300001

EID of the result in the Scopus database

2-s2.0-85164202651