AgroGround: Multi-Granularity Grounded Recognition in Agriculture
Agricultural visual models are typically evaluated for either recognition or localization, but reliable diagnosis requires identifying what is present and localizing the evidence. Agricultural visual question answering (VQA) datasets carry rich semantic labels but rarely link them to image regions, and adding such annotations by hand is costly at scale. We introduce AgroGround, a large-scale dataset for grounded agricultural recognition: identifying plant diseases and other agricultural targets and localizing their image regions. An automated pipeline converts the labels of eight agricultural VQA datasets into annotations for disease lesions and whole objects, producing 794,850 instructio...
| Authors | Abdulla Alshehhi, Zongyan Han, Rao Muhammad Anwer |
|---|---|
| Primary category | cs.CV |
| Published | 3 October 2026 |
| Last updated at source | 9 October 2026 |
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