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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/60460709:41310/20:85216

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20705 - Remote sensing

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Agronomy Research

ISSN

1406-894X

e-ISSN

—

Svazek periodika

18

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

EE - Estonská republika

Počet stran výsledku

12

Strana od-do

2665-2676

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85097302881