Integrating earth observation, artificial intelligence, and Crop modelling for maize yield estimation in smallholder farming systems in Kenya
Accurate crop yield estimation remains a persistent challenge in smallholder farming systems, particularly in Sub-Saharan Africa, where fragmented fields, diverse cropping practices, and limited ground-truth data hinder the scalability and precision of models. This study proposes an integrated frame...
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| Formato: | Tesis |
| Lenguaje: | Inglés |
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University Mohammed VI Polytechnic
2025
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| Acceso en línea: | https://hdl.handle.net/10568/176139 |
| _version_ | 1855525490040766464 |
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| author | Owuor, C.A. |
| author_browse | Owuor, C.A. |
| author_facet | Owuor, C.A. |
| author_sort | Owuor, C.A. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Accurate crop yield estimation remains a persistent challenge in smallholder farming systems, particularly in Sub-Saharan Africa, where fragmented fields, diverse cropping practices, and limited ground-truth data hinder the scalability and precision of models. This study proposes an integrated framework combining Earth Observation (EO), Artificial Intelligence (AI), and process-based Crop Modelling (CM) to address these limitations and improve yield prediction in data-scarce environments. The research aims to assess the potential of high-resolution EO data for capturing spatial variability in smallholder landscapes, calibrate crop models using region- specific parameters and agroclimatic data, and develop and benchmark AI models for scalable, cross-regional yield prediction. A multi-source data fusion strategy will integrate satellite imagery, weather records, and field observations to support the development of models. Calibration and validation will be conducted using field-level yield data from diverse agroecological zones in Kenya to ensure robustness and transferability of the results. The study aims to develop a validated, scalable framework for yield estimation that can inform climate- resilient agricultural planning, support early warning systems, and enhance digital advisory services tailored to the needs of smallholders. This research will contribute to the broader agenda of food security by advancing context-sensitive, data-driven tools for monitoring crop productivity in under-resourced regio |
| format | Tesis |
| id | CGSpace176139 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | University Mohammed VI Polytechnic |
| publisherStr | University Mohammed VI Polytechnic |
| record_format | dspace |
| spelling | CGSpace1761392025-08-25T09:22:27Z Integrating earth observation, artificial intelligence, and Crop modelling for maize yield estimation in smallholder farming systems in Kenya Owuor, C.A. crop modelling maize farming systems smallholders artificial intelligence Accurate crop yield estimation remains a persistent challenge in smallholder farming systems, particularly in Sub-Saharan Africa, where fragmented fields, diverse cropping practices, and limited ground-truth data hinder the scalability and precision of models. This study proposes an integrated framework combining Earth Observation (EO), Artificial Intelligence (AI), and process-based Crop Modelling (CM) to address these limitations and improve yield prediction in data-scarce environments. The research aims to assess the potential of high-resolution EO data for capturing spatial variability in smallholder landscapes, calibrate crop models using region- specific parameters and agroclimatic data, and develop and benchmark AI models for scalable, cross-regional yield prediction. A multi-source data fusion strategy will integrate satellite imagery, weather records, and field observations to support the development of models. Calibration and validation will be conducted using field-level yield data from diverse agroecological zones in Kenya to ensure robustness and transferability of the results. The study aims to develop a validated, scalable framework for yield estimation that can inform climate- resilient agricultural planning, support early warning systems, and enhance digital advisory services tailored to the needs of smallholders. This research will contribute to the broader agenda of food security by advancing context-sensitive, data-driven tools for monitoring crop productivity in under-resourced regio 2025-06-27 2025-08-19T07:25:01Z 2025-08-19T07:25:01Z Thesis https://hdl.handle.net/10568/176139 en Open Access application/pdf University Mohammed VI Polytechnic Owuor, C.A. (2025). Integrating earth observation, artificial intelligence, and Crop modelling for maize yield estimation in smallholder farming systems in Kenya. University Mohammed IV polytechnic. 34p. |
| spellingShingle | crop modelling maize farming systems smallholders artificial intelligence Owuor, C.A. Integrating earth observation, artificial intelligence, and Crop modelling for maize yield estimation in smallholder farming systems in Kenya |
| title | Integrating earth observation, artificial intelligence, and Crop modelling for maize yield estimation in smallholder farming systems in Kenya |
| title_full | Integrating earth observation, artificial intelligence, and Crop modelling for maize yield estimation in smallholder farming systems in Kenya |
| title_fullStr | Integrating earth observation, artificial intelligence, and Crop modelling for maize yield estimation in smallholder farming systems in Kenya |
| title_full_unstemmed | Integrating earth observation, artificial intelligence, and Crop modelling for maize yield estimation in smallholder farming systems in Kenya |
| title_short | Integrating earth observation, artificial intelligence, and Crop modelling for maize yield estimation in smallholder farming systems in Kenya |
| title_sort | integrating earth observation artificial intelligence and crop modelling for maize yield estimation in smallholder farming systems in kenya |
| topic | crop modelling maize farming systems smallholders artificial intelligence |
| url | https://hdl.handle.net/10568/176139 |
| work_keys_str_mv | AT owuorca integratingearthobservationartificialintelligenceandcropmodellingformaizeyieldestimationinsmallholderfarmingsystemsinkenya |