AgroTutor: Localised generative AI infrastructure for agri advisories
Generative AI (GenAI) technologies offer transformative potential in agriculture, enabling precise and timely advisories tailored to regional needs. The AgroTutor platform developed by CIMMYT pioneers a scalable approach to leveraging localised GenAI to deliver actionable agronomic insights to small...
| Autores principales: | , , , , , , , , |
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| Formato: | Brochure |
| Lenguaje: | Inglés |
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International Food Policy Research Institute
2024
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| Acceso en línea: | https://hdl.handle.net/10568/169921 |
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| author | Nagaraji, Satish Gardeazábal Monsalve, Andrea Narasimhan K.V. Swathi Vurrakula Saji, Sherin Maria Sandya N.R. Achar, Fredrick Bautista Ramirez, Rosa Elena Mahto, Sanjeev |
| author_browse | Achar, Fredrick Bautista Ramirez, Rosa Elena Gardeazábal Monsalve, Andrea Mahto, Sanjeev Nagaraji, Satish Narasimhan K.V. Saji, Sherin Maria Sandya N.R. Swathi Vurrakula |
| author_facet | Nagaraji, Satish Gardeazábal Monsalve, Andrea Narasimhan K.V. Swathi Vurrakula Saji, Sherin Maria Sandya N.R. Achar, Fredrick Bautista Ramirez, Rosa Elena Mahto, Sanjeev |
| author_sort | Nagaraji, Satish |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Generative AI (GenAI) technologies offer transformative potential in agriculture, enabling precise and timely advisories tailored to regional needs. The AgroTutor platform developed by CIMMYT pioneers a scalable approach to leveraging localised GenAI to deliver actionable agronomic insights to smallholder farmers across the global south. By employing Retrieval-Augmented Generation (RAG), low-cost language models, and multi-modal data integration, AgroTutor addresses critical challenges such as context localisation, resource optimisation, and quick deployment of RAG. This white paper outlines the process, methodology, pilot outcomes, and the way forward of AgroTutor for agriculture advisories. AgroTutor is an open-source application designed to enable local organisations to provide farmers with cutting-edge, AI-powered advisory services, particularly in the Global South. The system leverages a generative AI framework to mitigate critical knowledge gaps within the agricultural sector. Designed with localised contexts in mind, the system integrates RAG frameworks with multi-modal datasets to enhance advisory relevance and accuracy. Pilots conducted in India, Mexico, and Kenya demonstrate its potential for scalability and ease of deployment, addressing challenges such as crop management, pest control, and climate adaptation. |
| format | Brochure |
| id | CGSpace169921 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1699212025-05-04T09:22:22Z AgroTutor: Localised generative AI infrastructure for agri advisories Nagaraji, Satish Gardeazábal Monsalve, Andrea Narasimhan K.V. Swathi Vurrakula Saji, Sherin Maria Sandya N.R. Achar, Fredrick Bautista Ramirez, Rosa Elena Mahto, Sanjeev artificial intelligence digital agriculture large language models computer applications Generative AI (GenAI) technologies offer transformative potential in agriculture, enabling precise and timely advisories tailored to regional needs. The AgroTutor platform developed by CIMMYT pioneers a scalable approach to leveraging localised GenAI to deliver actionable agronomic insights to smallholder farmers across the global south. By employing Retrieval-Augmented Generation (RAG), low-cost language models, and multi-modal data integration, AgroTutor addresses critical challenges such as context localisation, resource optimisation, and quick deployment of RAG. This white paper outlines the process, methodology, pilot outcomes, and the way forward of AgroTutor for agriculture advisories. AgroTutor is an open-source application designed to enable local organisations to provide farmers with cutting-edge, AI-powered advisory services, particularly in the Global South. The system leverages a generative AI framework to mitigate critical knowledge gaps within the agricultural sector. Designed with localised contexts in mind, the system integrates RAG frameworks with multi-modal datasets to enhance advisory relevance and accuracy. Pilots conducted in India, Mexico, and Kenya demonstrate its potential for scalability and ease of deployment, addressing challenges such as crop management, pest control, and climate adaptation. 2024 2025-01-25T23:13:35Z 2025-01-25T23:13:35Z Brochure https://hdl.handle.net/10568/169921 en Open Access application/pdf International Food Policy Research Institute Nagaraji, S., Gardeazabal Monsalve, A., Narasimhan K. V., Swathi Vurrakula, Saji, S., Sandya N. R., Achar, F., Bautista Ramirez, R. E., & Mahto, S. (2024). AgroTutor: Localised generative AI infrastructure for agri advisories. IFPRI. https://hdl.handle.net/10883/35423 |
| spellingShingle | artificial intelligence digital agriculture large language models computer applications Nagaraji, Satish Gardeazábal Monsalve, Andrea Narasimhan K.V. Swathi Vurrakula Saji, Sherin Maria Sandya N.R. Achar, Fredrick Bautista Ramirez, Rosa Elena Mahto, Sanjeev AgroTutor: Localised generative AI infrastructure for agri advisories |
| title | AgroTutor: Localised generative AI infrastructure for agri advisories |
| title_full | AgroTutor: Localised generative AI infrastructure for agri advisories |
| title_fullStr | AgroTutor: Localised generative AI infrastructure for agri advisories |
| title_full_unstemmed | AgroTutor: Localised generative AI infrastructure for agri advisories |
| title_short | AgroTutor: Localised generative AI infrastructure for agri advisories |
| title_sort | agrotutor localised generative ai infrastructure for agri advisories |
| topic | artificial intelligence digital agriculture large language models computer applications |
| url | https://hdl.handle.net/10568/169921 |
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