Author

Lars Lundqvist

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Preprint Sep 2026

Vision-language models know more about agriculture than they show and rubric-grounded verifications close the gap

Vision-language models (VLMs) show promise for agricultural classification, but zero-shot performance on disease, pest, damage, quality, and species identification remains poor, and it is unclear whether this reflects weak visual features or a failure to connect them to domain knowledge. We build a benchmark of 116 dat...

Earl Ranario, Jared Smith, Lars Lundqvist et al. · 0 citations

#artificial intelligence Mar 2026

Using Vision Language Foundation Models to Generate Plant Simulation Configurations via In-Context Learning

A benchmark for evaluating whether vision-language models (VLMs) can generate plant simulation configurations from imagery using in-context learning and establishing a benchmark for studying how multimodal reasoning, prompt design, and the sim-to-real domain gap affect plant phenotyping tasks is established.

Heesup Yun, I. Uyehara, Earl Ranario et al. · arXiv.org · 0 citations