Publication detail

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

SADENOVA, M. BEISEKENOV, N. VARBANOV, P. PAN, T.

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

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 optimal learning algorithms to obtain accurate predictions.

Keywords

cereals; forage crops; grain legumes; machine learning; oilseeds; remote sensing; sustainable farming practices; yield forecasting

Authors

SADENOVA, M.; BEISEKENOV, N.; VARBANOV, P.; PAN, T.

Released

1. 6. 2023

Publisher

MDPI

Location

MDPIST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND

ISBN

2077-0472

Periodical

Agriculture

Year of study

13

Number

6

State

Swiss Confederation

Pages from

1

Pages to

27

Pages count

27

URL

https://www.mdpi.com/2077-0472/13/6/1195

Full text in the Digital Library

http://hdl.handle.net/11012/244980

BibTex

@article{BUT187361,
  author="Marzhan {Sadenova} and Nail {Beisekenov} and Petar Sabev {Varbanov} and Ting {Pan}",
  title="Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan",
  journal="Agriculture",
  year="2023",
  volume="13",
  number="6",
  pages="27",
  doi="10.3390/agriculture13061195",
  issn="2077-0472",
  url="https://www.mdpi.com/2077-0472/13/6/1195"
}