arXiv cs.AI· Abdulla Alshehhi, Zongyan Han, Rao Muhammad Anwer·· 6 天前AI 评分22
AgroGround：农业领域的多粒度接地识别数据集与基准
AgroGround: Multi-Granularity Grounded Recognition in Agriculture
AI 导读
AgroGround 是一个面向农业接地识别的大规模数据集，通过自动化流水线将八个农业 VQA 数据集的标签转化为病害病斑与整体目标的定位标注，生成 794,850 条指令样本。
正文 · 原文
Abstract: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 instruction examples. Healthy images provide negative supervision for disease queries, teaching the model to return empty predictions. We fine-tune a shared vision-language model on known-target grounding instructions combined with instructions requiring both recognition and localization. We evaluate predicted identities, regions, joint correctness, and healthy-image abstention on 1,480 human-verified images disjoint from all training data. Grounding-only fine-tuning reduces recognition accuracy from 51.8\% to 29.1\%, while adding recognition-and-localization instructions raises it to 72.6\%. With images and annotations held fixed, combining the two formats raises joint accuracy from 19.2\% to 43.3\% at comparable grounding. Healthy negatives raise abstention on healthy images to 95.0\%, and reinforcement learning improves lesion-level grounding. The resulting 2B model exceeds its annotation teacher in grounding F1 on our benchmark and on the external PlantSeg test set. AgroGround establishes a benchmark for grounded agricultural recognition, measuring joint correctness of identity and localization along with abstention on healthy images. The code is available at this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.04425 [cs.CV] |
| (or arXiv:2610.04425v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.04425 arXiv-issued DOI via DataCite |
Submission history
From: Zongyan Han [view email]
[v1]
Sat, 3 Oct 2026 10:27:47 UTC (2,078 KB)
[v2]
Fri, 9 Oct 2026 11:13:59 UTC (2,079 KB)
来源：arXiv cs.AI · arxiv.org