The role of explainability in AI for agriculture: Making digital systems easier to understand for farmers
Agriculture, like many industries, is continuously evolving through technological innovations. One example is precision agriculture - a practice that employs data collection and analysis to optimize the use of inputs such as water, fertilizers, and pesticides based on local environmental conditions...
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| Format: | Blog Post |
| Language: | Inglés |
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International Food Policy Research Institute
2025
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| Online Access: | https://hdl.handle.net/10568/178198 |
| _version_ | 1855531468784140288 |
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| author | Girmay, Mengisti Berihu |
| author_browse | Girmay, Mengisti Berihu |
| author_facet | Girmay, Mengisti Berihu |
| author_sort | Girmay, Mengisti Berihu |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Agriculture, like many industries, is continuously evolving through technological innovations. One example is precision agriculture - a practice that employs data collection and analysis to optimize the use of inputs such as water, fertilizers, and pesticides based on local environmental conditions at the sub-field level. Artificial Intelligence (AI) and the availability of low-cost sensors have renewed interest in precision agriculture and have broadened areas of application to the livestock sector. Falling costs have further increased the accessibility of these tools to smallholder farmers in low- and middle-income countries (LMICs). |
| format | Blog Post |
| id | CGSpace178198 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1781982025-11-25T20:09:02Z The role of explainability in AI for agriculture: Making digital systems easier to understand for farmers Girmay, Mengisti Berihu agriculture digital technology artificial intelligence farmers food systems Agriculture, like many industries, is continuously evolving through technological innovations. One example is precision agriculture - a practice that employs data collection and analysis to optimize the use of inputs such as water, fertilizers, and pesticides based on local environmental conditions at the sub-field level. Artificial Intelligence (AI) and the availability of low-cost sensors have renewed interest in precision agriculture and have broadened areas of application to the livestock sector. Falling costs have further increased the accessibility of these tools to smallholder farmers in low- and middle-income countries (LMICs). 2025-05-30 2025-11-25T19:51:16Z 2025-11-25T19:51:16Z Blog Post https://hdl.handle.net/10568/178198 en https://doi.org/10.48550/arXiv.2506.11665 https://dl.gi.de/server/api/core/bitstreams/86e6e38b-604a-4377-a975-fc06c2b8b1f5/content Open Access International Food Policy Research Institute Girmay, Mengisti Berihu. 2025. The role of explainability in AI for agriculture: Making digital systems easier to understand for farmers. IFPRI Blog Post. https://www.ifpri.org/blog/the-role-of-explainability-in-ai-for-agriculture-making-digital-systems-easier-to-understand-for-farmers/ |
| spellingShingle | agriculture digital technology artificial intelligence farmers food systems Girmay, Mengisti Berihu The role of explainability in AI for agriculture: Making digital systems easier to understand for farmers |
| title | The role of explainability in AI for agriculture: Making digital systems easier to understand for farmers |
| title_full | The role of explainability in AI for agriculture: Making digital systems easier to understand for farmers |
| title_fullStr | The role of explainability in AI for agriculture: Making digital systems easier to understand for farmers |
| title_full_unstemmed | The role of explainability in AI for agriculture: Making digital systems easier to understand for farmers |
| title_short | The role of explainability in AI for agriculture: Making digital systems easier to understand for farmers |
| title_sort | role of explainability in ai for agriculture making digital systems easier to understand for farmers |
| topic | agriculture digital technology artificial intelligence farmers food systems |
| url | https://hdl.handle.net/10568/178198 |
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