AgriGround Task Reference
AgriGround contains 14 instruction-tuning tasks spanning captioning, segmentation, localization, counting, conversation, and agricultural question answering. Counts below follow the current manuscript statistics and include both train and test samples.
Task Distribution
| Task | Output supervision | Train | Test | Total | Share |
|---|---|---|---|---|---|
| Semantic segmentation | Text + masks | 1,651,229 | 422,187 | 2,073,416 | 18.15% |
| Phrase grounding | Bounding boxes | 1,638,327 | 418,948 | 2,057,275 | 18.01% |
| Referring expression segmentation | Text + masks | 1,638,327 | 418,948 | 2,057,275 | 18.01% |
| Part segmentation | Text + masks | 820,240 | 209,124 | 1,029,364 | 9.01% |
| Region-level conversation | Region + multi-turn text | 749,141 | 191,757 | 940,898 | 8.24% |
| Region-level captioning | Region + text | 722,833 | 185,023 | 907,856 | 7.95% |
| Classification QA | Text | 639,962 | 163,786 | 803,748 | 7.04% |
| Image-level captioning | Text | 400,872 | 102,587 | 503,459 | 4.41% |
| Grounded caption generation | Interleaved text + masks | 379,023 | 97,007 | 476,030 | 4.17% |
| Multi-turn grounded conversation | Multi-turn text + masks | 379,023 | 97,007 | 476,030 | 4.17% |
| Grounded counting | Count | 21,865 | 5,596 | 27,461 | 0.24% |
| Grounded detection | Count + bounding boxes | 21,637 | 5,535 | 27,172 | 0.24% |
| Reasoning detection | Count + bounding boxes | 21,637 | 5,535 | 27,172 | 0.24% |
| Negative absence QA | Text | 11,204 | 2,788 | 13,992 | 0.12% |
| Total | 9,095,320 | 2,325,828 | 11,421,148 | 100% |
Task Definitions
Captioning
Image-level captioning generates a holistic agricultural description without requiring spatial output.
Region-level captioning receives a specified image region and describes the localized agricultural content.
Grounded caption generation produces a detailed caption with grounded phrases interleaved with [SEG] tokens. Every grounded phrase is paired with a corresponding mask.
Segmentation
Referring expression segmentation segments the object or region described by a natural-language expression.
Semantic segmentation segments one or more instances of a requested semantic category.
Part segmentation targets a specific plant or object component, such as a stem, branch, leaf, flower structure, or lesion.
Detection and Localization
Phrase grounding maps a phrase to a normalized bounding box.
Grounded counting reports the number of requested agricultural objects in an image.
Grounded detection reports the count and normalized bounding boxes for all requested instances.
Reasoning detection uses an agricultural-context prompt to infer and localize the target instances.
Conversation and QA
Region-level conversation supports follow-up questions about a selected image region.
Multi-turn grounded conversation combines image-level discussion with later phrase localization or segmentation.
Classification QA answers questions about species, diseases, pests, weeds, conditions, and other agricultural classes.
Negative absence QA teaches the model to state when a requested entity is not visible instead of producing an unsupported mask.
Annotation Representation
The public schema will be documented with the annotation release. Records are expected to include an image reference, task identifier, user instruction, target response, and task-dependent spatial supervision such as masks, regions, or normalized bounding boxes.