Enhancement of Sorghum Forecasting Models Using Machine Learning in the rain-fed sector in Sudan-Reference-Cited by-同舟云学术

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

Published:2023-12-30 Issue: Volume:9 Page:1-6
ISSN:
Container-title:2023 International Conference on Electrical, Communication and Computer Engineering (ICECCE)
language:
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Author:

Alnayer Elaf¹,Elsheikh Kasba¹,Alawad Amna¹,Husain Omayma²,Basheir Isam³,Siddig Abubakr⁴

Affiliation:

1. University of Khartoum,Dept of Information Technology,Khartoum,Sudan

2. University of Khartoum,Dept of Information Technology,Sudan

3. Wad Medani Ahlia University,Faculty of Computer Science,Wad Medani,Sudan

4. Technological University Dublin,School of Computer Science,Dublin,Ireland

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10441786/10441790/10442132.pdf?arnumber=10442132

Reference20 articles.

1. Analysis of Multitemporal NDVI Data for Crop Yield Forecasting in the Sahel

2. Machine learning for large-scale crop yield forecasting

3. Machine Learning in Agriculture: A Review

4. Crops yield prediction based on machine learning models: Case of West African countries

5. Weather based forecasting model for crops yield using neural network approach;Laxmi;Stat. Appl,2011

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