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...
| Main Author: | |
|---|---|
| Format: | Blog Post |
| Language: | Inglés |
| Published: |
International Food Policy Research Institute
2025
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/178198 |
| Summary: | 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). |
|---|