Crop yield prediction using machine learning, multi-source remote sensing technologies and data fusion: a case study of Mezőhegyes Hungary

Amankulova Khilola
Crop yield prediction using machine learning, multi-source remote sensing technologies and data fusion: a case study of Mezőhegyes Hungary.
Doctoral thesis (PhD), University of Szeged.
(2024)

[thumbnail of PDF (thesis).pdf] PDF (thesis)
Download (5MB)
[thumbnail of PDF (booklet).pdf] PDF (booklet)
Download (228kB)
Item Type: Thesis (Doctoral thesis (PhD))
Creators: Amankulova Khilola
Hungarian title: Terméshozam-előrejelzés gépi tanulással, több forrásból származó távérzékelési technológiákkal és adatfúzióval: esettanulmány Mezőhegyes Magyarországról
Supervisor(s):
Supervisor
Position, academic title, institution
MTMT author ID
Mucsi László
Associate professor, Dr. habil, Geoinformatikai, Természet- és Környezetföldrajzi Tanszék SZTE / TTIK / FFI
10001354
Subjects: 01. Natural sciences > 01.05. Earth and related environmental sciences > 01.05.01. Geosciences, multidisciplinary
01. Natural sciences > 01.05. Earth and related environmental sciences > 01.05.01. Geosciences, multidisciplinary > 01.05.01.01. Geo-information and spatial data analysis
01. Natural sciences > 01.05. Earth and related environmental sciences > 01.05.01. Geosciences, multidisciplinary > 01.05.01.06. Earth observations from space/remote sensing
01. Natural sciences > 01.05. Earth and related environmental sciences > 01.05.05. Physical geography > 01.05.05.02. Geographical information systems, cartography
Divisions: Doctoral School of Geosciences
Discipline: Natural Sciences > Earth Sciences
Language: English
Date: 2024. May 22.
Uncontrolled Keywords: Remote sensing; Random Forest Regression; Sentinel-2; sunflower; yield prediction, soybean, Machine Learning
Item ID: 12125
Date Deposited: 2024. Mar. 22. 15:00
Last Modified: 2024. May. 24. 12:02
Depository no.: B 7423
URI: https://doktori.bibl.u-szeged.hu/id/eprint/12125
Defence/Citable status: Not Defended. (Do not cite until it has not assigned DOI number!)

Actions (login required)

View Item View Item