Application of feature selection for predicting leaf chlorophyll content in oats (Avena sativa l.) from hyperspectral imagery

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027006%3A_____%2F20%3A10149389" target="_blank" >RIV/00027006:_____/20:10149389 - isvavai.cz</a>

Alternative codes found

RIV/60460709:41310/20:85216

Result on the web

<a href="https://agronomy.emu.ee/wp-content/uploads/2020/07/AR2020_Vol18No4_Zelazny.pdf" target="_blank" >https://agronomy.emu.ee/wp-content/uploads/2020/07/AR2020_Vol18No4_Zelazny.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15159/AR.20.174" target="_blank" >10.15159/AR.20.174</a>

Result language

angličtina

Original language name

Application of feature selection for predicting leaf chlorophyll content in oats (Avena sativa l.) from hyperspectral imagery

Original language description

Feature selection can improve predictions generated by partial least squares models. In the context of hyperspectral imaging, it can also enable the development of affordable devices with specialized applications. The feasibility of feature selection for oat leaf chlorophyll estimation from hyperspectral imagery was assessed using a public domain dataset. A wrapper approach resulted in a simplistic model with poor predictive performance. The number of model inputs decreased from 94 to 3 bands when a filter approach based on the minimum redundancy, maximum relevance criterion was attempted. The filtering led to improved prediction quality, with the root mean square error decreasing from 0.17 to 0.16 g m-2 and R2 increasing from 0.57 to 0.62. Accurate predictions were obtained especially for low chlorophyll levels. The obtained model estimated leaf chlorophyll concentration from near infra-red reflectance, canopy darkness, and its blueness. The prediction robustness needs to be investigated, which can be done by employing an ensemble methodology and testing the model on a new dataset with improved ground-truth measurements and additional crop species.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

—

OECD FORD branch

20705 - Remote sensing

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

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

Name of the periodical

Agronomy Research

ISSN

1406-894X

e-ISSN

—

Volume of the periodical

18

Issue of the periodical within the volume

4

Country of publishing house

EE - ESTONIA

Number of pages

12

Pages from-to

2665-2676

UT code for WoS article

—

EID of the result in the Scopus database

2-s2.0-85097302881