Author

Earl Ranario

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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

Open access Aug 2026

Genetic mapping and genomic prediction for agronomic, grain compositional, and sensing-enabled traits in a cowpea MAGIC population along an environmental gradient

Cowpea (Vigna unguiculata [L.] Walp.) is a resilient grain legume and an important global source of dietary protein, yet the genetic and environmental basis of phenological and canopy development, as well as grain composition, remains incompletely characterized across production environments. In this study, we evaluate...

Jonathan M. Berlingeri, Sassoum Lo, Margaret Riggs et al. · bioRxiv · 0 citations