Title: AgriGPT-VL: Agricultural Vision–Language Understanding Suite
Primary area: datasets and benchmarks
Keywords: Dataset; Benchmarking; Vision–Language Models; Agriculture; Multimodal Learning

Abstract: Despite rapid advances in multimodal large language models, agricultural applications remain constrained by the scarcity of domain-tailored models, curated vision–language corpora, and rigorous evaluation. To address these challenges, we present the AgriGPT-VL Suite, a unified multimodal framework for agriculture. Our contributions are threefold. First, we introduce Agri-3M-VL, the largest vision–language corpus for agriculture to our knowledge, curated by a scalable multi-agent data generator; it comprises 1M image–caption pairs, 2M image-grounded VQA pairs, 50K expert-level VQA instances, and 15k GRPO reinforcement learning dataset. Second, we develop AgriGPT-VL, an agriculture-specialized vision–language model trained via a progressive curriculum of textual grounding, multimodal shallow/deep alignment, and GRPO refinement. This method achieves strong multimodal reasoning while preserving text-only capability. Third, we establish AgriBench-VL-4K, a compact yet challenging evaluation suite with open-ended and image-grounded questions, paired with a multi-metric evaluation and an LLM-as-a-judge framework. Experiments show that AgriGPT-VL outperforms leading general-purpose VLMs on AgriBench-VL-4K, achieving higher pairwise win rates in the LLM-as-a-judge evaluation. Meanwhile, it remains competitive on the text-only AgriBench-13K with no noticeable degradation of language ability. Ablation studies further confirm consistent gains from our alignment and GRPO refinement stages. All resources will be released to support reproducible research and deployment in low-resource agricultural settings at: https://anonymous.4open.science/r/AgriGPT-VL-DA65/

TL;DR: AgriGPT-VL is an agriculture-specific LLM ecosystem with 1M+2M+50k+15k Agri-VL dataset, Agri-VL model,  Agri-VL benchmark-4K, and a generalizable Data Generator.