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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

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

CEP classification

—

OECD FORD branch

20704 - Energy and fuels

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Name of the periodical

Chemical Engineering Transactions

ISSN

2283-9216

e-ISSN

—

Volume of the periodical

neuveden

Issue of the periodical within the volume

94

Country of publishing house

IT - ITALY

Number of pages

6

Pages from-to

19-24

UT code for WoS article

—

EID of the result in the Scopus database

2-s2.0-85139266096