Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F23%3APU150366" target="_blank" >RIV/00216305:26210/23:PU150366 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2077-0472/13/6/1195" target="_blank" >https://www.mdpi.com/2077-0472/13/6/1195</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/agriculture13061195" target="_blank" >10.3390/agriculture13061195</a>

Result language

angličtina

Original language name

Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan

Original language description

The article provides an overview of the accuracy of various yield forecasting algorithms and offers a detailed explanation of the models and machine learning algorithms that are required for crop yield forecasting. A unified crop yield forecasting methodology is developed, which can be adjusted by adding new indicators and extensions. The proposed methodology is based on remote sensing data taken from free sources. Experiments were carried out on crops of cereals, legumes, oilseeds and forage crops in eastern Kazakhstan. Data on agricultural lands of the experimental farms were obtained using processed images from Sentinel-2 and Landsat-8 satellites (EO Browser) for the period of 2017-2022. In total, a dataset of 1600 indicators was collected with NDVI and MSAVI indices recorded at a frequency of once a week. Based on the results of this work, it is found that yields can be predicted from NDVI vegetation index data and meteorological data on average temperature, surface soil moisture and wind speed. A machine learning programming language can calculate the relationship between these indicators and build a neural network that predicts yield. The neural network produces predictions based on the constructed data weights, which are corrected using activation function algorithms. As a result of the research, the functions with the highest prediction accuracy during vegetative development for all crops presented in this paper are multi-layer perceptron, with a prediction accuracy of 66% to 99% (85% on average), and polynomial regression, with a prediction accuracy of 63% to 98% (82% on average). Thus, it is shown that the use of machine learning and neural networks for crop yield prediction has advantages over other mathematical modelling techniques. The use of machine learning (neural network) technologies makes it possible to predict crop yields on the basis of relevant data. The individual approach of machine learning to each crop allows for the determination of the op

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

40106 - Agronomy, plant breeding and plant protection; (Agricultural biotechnology to be 4.4)

Project

<a href="/en/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Sustainable Process Integration Laboratory (SPIL)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

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

Name of the periodical

Agriculture

ISSN

2077-0472

e-ISSN

—

Volume of the periodical

13

Issue of the periodical within the volume

6

Country of publishing house

CH - SWITZERLAND

Number of pages

27

Pages from-to

1-27

UT code for WoS article

001013808200001

EID of the result in the Scopus database

2-s2.0-85164128805