Enhancement of Sorghum Forecasting Models Using Machine Learning in the rain-fed sector in Sudan

Elaf Alnayer; Kasba Elsheikh; Amna Alawad; Omayma Husain; Isam Basheir; Abubakr Siddig

doi:10.1109/icecce61019.2023.10442132

Enhancement of Sorghum Forecasting Models Using Machine Learning in the rain-fed sector in Sudan

Elaf Alnayer, Kasba Elsheikh, Amna Alawad, Omayma Husain, Isam Basheir, Abubakr Siddig

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Original language	English
Title of host publication	4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023
Pages	1-6
Number of pages	6
ISBN (Electronic)	9798350369694
DOIs	https://doi.org/10.1109/icecce61019.2023.10442132
Publication status	Published - 30 Dec 2023

Publication series

Name	4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023

Keywords

MLP
XGBoost regressor
crop yield prediction
forecasting
machine learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/icecce61019.2023.10442132

Cite this

Alnayer, E., Elsheikh, K., Alawad, A., Husain, O., Basheir, I., & Siddig, A. (2023). Enhancement of Sorghum Forecasting Models Using Machine Learning in the rain-fed sector in Sudan. In 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023 (pp. 1-6). (4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023). https://doi.org/10.1109/icecce61019.2023.10442132

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title = "Enhancement of Sorghum Forecasting Models Using Machine Learning in the rain-fed sector in Sudan",

keywords = "MLP, XGBoost regressor, crop yield prediction, forecasting, machine learning",

author = "Elaf Alnayer and Kasba Elsheikh and Amna Alawad and Omayma Husain and Isam Basheir and Abubakr Siddig",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.",

year = "2023",

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doi = "10.1109/icecce61019.2023.10442132",

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Alnayer, E, Elsheikh, K, Alawad, A, Husain, O, Basheir, I & Siddig, A 2023, Enhancement of Sorghum Forecasting Models Using Machine Learning in the rain-fed sector in Sudan. in 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023. 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023, pp. 1-6. https://doi.org/10.1109/icecce61019.2023.10442132

Enhancement of Sorghum Forecasting Models Using Machine Learning in the rain-fed sector in Sudan. / Alnayer, Elaf; Elsheikh, Kasba; Alawad, Amna et al.
4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023. 2023. p. 1-6 (4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Enhancement of Sorghum Forecasting Models Using Machine Learning in the rain-fed sector in Sudan

AU - Alnayer, Elaf

AU - Elsheikh, Kasba

AU - Alawad, Amna

AU - Husain, Omayma

AU - Basheir, Isam

AU - Siddig, Abubakr

PY - 2023/12/30

Y1 - 2023/12/30

KW - MLP

KW - XGBoost regressor

KW - crop yield prediction

KW - forecasting

KW - machine learning

UR - https://www.scopus.com/pages/publications/85187224130

U2 - 10.1109/icecce61019.2023.10442132

DO - 10.1109/icecce61019.2023.10442132

M3 - Conference contribution

T3 - 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023

SP - 1

EP - 6

BT - 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023

ER -

Alnayer E, Elsheikh K, Alawad A, Husain O, Basheir I, Siddig A. Enhancement of Sorghum Forecasting Models Using Machine Learning in the rain-fed sector in Sudan. In 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023. 2023. p. 1-6. (4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023). doi: 10.1109/icecce61019.2023.10442132