dc.contributor.author |
Thothela, NT
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dc.contributor.author |
Markus, E
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dc.contributor.author |
Masinde, M
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dc.contributor.author |
Abu-Mahfouz, Adnan MI
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dc.date.accessioned |
2024-01-11T10:29:11Z |
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dc.date.available |
2024-01-11T10:29:11Z |
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dc.date.issued |
2023-07 |
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dc.identifier.citation |
Thothela, N., Markus, E., Masinde, M. & Abu-Mahfouz, A.M. 2023. A framework for an intelligent agro-climate decision support system for small-scale farmers in Swayimane. http://hdl.handle.net/10204/13507 . |
en_ZA |
dc.identifier.isbn |
979-8-3503-2297-2 |
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dc.identifier.isbn |
979-8-3503-2298-9 |
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dc.identifier.uri |
DOI: 10.1109/ICECCME57830.2023.10253285
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|
dc.identifier.uri |
http://hdl.handle.net/10204/13507
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|
dc.description.abstract |
Machine Learning (ML) is fast becoming a technology of choice with enormous potential to transform small-scale agriculture, particularly in helping farmers make informed decisions about crop choices. On the other hand, there is evidence that small-scale farmers face several challenges, such as lack of access to market information, poor soil quality, and inadequate farming techniques. ML technology can be used to provide real-time information on weather patterns, soil quality, and other factors that affect crop growth and yields. By providing this information, ML can help farmers choose the right crops to plant and optimize their yields. In this paper, the authors report on the use of AI to select the appropriate crop to plant, which resulted in crop choices that are 99% accurate. This was achieved by collecting climatic and edaphic data, and using different multi-class classification algorithms to train the dataset. The results of the different algorithms were compared and contrasted using different metrics to determine the best fit for the development a framework for prediction of crop suitability at pre-planting stage. The resulting framework utilizes ML as well as observed Indigenous Knowledge (IK) to synthesize edaphic and climatic factors to support decision management for small-scale farmers. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/document/10253285 |
en_US |
dc.source |
2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Canary Islands, Spain, 19-21 July 2023 |
en_US |
dc.subject |
Artificial Intelligences |
en_US |
dc.subject |
AI |
en_US |
dc.subject |
Indigenous knowledge cropping decisions |
en_US |
dc.subject |
IK |
en_US |
dc.subject |
Machine learnings |
en_US |
dc.subject |
ML |
en_US |
dc.subject |
Small-scale farmers |
en_US |
dc.subject |
Sensor technology |
en_US |
dc.title |
A framework for an intelligent agro-climate decision support system for small-scale farmers in Swayimane |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.description.pages |
7 |
en_US |
dc.description.note |
©2023 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: https://ieeexplore.ieee.org/document/10253285 |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
EDT4IR Management |
en_US |
dc.identifier.apacitation |
Thothela, N., Markus, E., Masinde, M., & Abu-Mahfouz, A. M. (2023). A framework for an intelligent agro-climate decision support system for small-scale farmers in Swayimane. http://hdl.handle.net/10204/13507 |
en_ZA |
dc.identifier.chicagocitation |
Thothela, NT, E Markus, M Masinde, and Adnan MI Abu-Mahfouz. "A framework for an intelligent agro-climate decision support system for small-scale farmers in Swayimane." <i>2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Canary Islands, Spain, 19-21 July 2023</i> (2023): http://hdl.handle.net/10204/13507 |
en_ZA |
dc.identifier.vancouvercitation |
Thothela N, Markus E, Masinde M, Abu-Mahfouz AM, A framework for an intelligent agro-climate decision support system for small-scale farmers in Swayimane; 2023. http://hdl.handle.net/10204/13507 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Thothela, NT
AU - Markus, E
AU - Masinde, M
AU - Abu-Mahfouz, Adnan MI
AB - Machine Learning (ML) is fast becoming a technology of choice with enormous potential to transform small-scale agriculture, particularly in helping farmers make informed decisions about crop choices. On the other hand, there is evidence that small-scale farmers face several challenges, such as lack of access to market information, poor soil quality, and inadequate farming techniques. ML technology can be used to provide real-time information on weather patterns, soil quality, and other factors that affect crop growth and yields. By providing this information, ML can help farmers choose the right crops to plant and optimize their yields. In this paper, the authors report on the use of AI to select the appropriate crop to plant, which resulted in crop choices that are 99% accurate. This was achieved by collecting climatic and edaphic data, and using different multi-class classification algorithms to train the dataset. The results of the different algorithms were compared and contrasted using different metrics to determine the best fit for the development a framework for prediction of crop suitability at pre-planting stage. The resulting framework utilizes ML as well as observed Indigenous Knowledge (IK) to synthesize edaphic and climatic factors to support decision management for small-scale farmers.
DA - 2023-07
DB - ResearchSpace
DP - CSIR
J1 - 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Canary Islands, Spain, 19-21 July 2023
KW - Artificial Intelligences
KW - AI
KW - Indigenous knowledge cropping decisions
KW - IK
KW - Machine learnings
KW - ML
KW - Small-scale farmers
KW - Sensor technology
LK - https://researchspace.csir.co.za
PY - 2023
SM - 979-8-3503-2297-2
SM - 979-8-3503-2298-9
T1 - A framework for an intelligent agro-climate decision support system for small-scale farmers in Swayimane
TI - A framework for an intelligent agro-climate decision support system for small-scale farmers in Swayimane
UR - http://hdl.handle.net/10204/13507
ER -
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en_ZA |
dc.identifier.worklist |
27207 |
en_US |