Modelling of Forecasting Crop Yields Based on Earth Remote Sensing Data and Remote Sensing Methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU145691" target="_blank" >RIV/00216305:26210/22:PU145691 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.cetjournal.it/index.php/cet/article/view/CET2294003" target="_blank" >https://www.cetjournal.it/index.php/cet/article/view/CET2294003</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3303/CET2294003" target="_blank" >10.3303/CET2294003</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modelling of Forecasting Crop Yields Based on Earth Remote Sensing Data and Remote Sensing Methods

Popis výsledku v původním jazyce

In this work, the authors proposed a method of determining the yield of spring wheat based on the analysis of the dynamics of spectral parameters of its growth and development, determined by multispectral satellite images. It was found that by processing the satellite images of the fields in selected spectral regions, it is possible to estimate with a high degree of reliability the productivity of plants, biomass value, photosynthesis intensity and other parameters. By means of mathematical processing of the statistical data array of phosphorus, potassium and nitrogen content in the soil according to the Remote Sensing (RS) data in comparison with the actual data obtained after agrochemical analysis of soil samples, the total value of the average error (the average absolute error ranging from 24 to 36 % for the analysed period) was calculated. Using remote sensing data and Convolutional Neural Networks (CNN), the forecast of spring wheat yield in the conditions of soil and climatic zone of Eastern Kazakhstan was carried out. The results obtained with the predictive model are close to the actual yield results of the previous year (the error smaller than 9 %), indicating the relationship between yield and agrochemical analysis of the soil.

Název v anglickém jazyce

Modelling of Forecasting Crop Yields Based on Earth Remote Sensing Data and Remote Sensing Methods

Popis výsledku anglicky

In this work, the authors proposed a method of determining the yield of spring wheat based on the analysis of the dynamics of spectral parameters of its growth and development, determined by multispectral satellite images. It was found that by processing the satellite images of the fields in selected spectral regions, it is possible to estimate with a high degree of reliability the productivity of plants, biomass value, photosynthesis intensity and other parameters. By means of mathematical processing of the statistical data array of phosphorus, potassium and nitrogen content in the soil according to the Remote Sensing (RS) data in comparison with the actual data obtained after agrochemical analysis of soil samples, the total value of the average error (the average absolute error ranging from 24 to 36 % for the analysed period) was calculated. Using remote sensing data and Convolutional Neural Networks (CNN), the forecast of spring wheat yield in the conditions of soil and climatic zone of Eastern Kazakhstan was carried out. The results obtained with the predictive model are close to the actual yield results of the previous year (the error smaller than 9 %), indicating the relationship between yield and agrochemical analysis of the soil.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Chemical Engineering Transactions

ISSN

2283-9216

e-ISSN

—

Svazek periodika

neuveden

Číslo periodika v rámci svazku

94

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

6

Strana od-do

19-24

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85139266096