Vision-language models see more about agriculture than they report, and test-time scaling remains bounded by parametric knowledge

Vision-language models (VLMs) show promise for agricultural diagnosis but fail unpredictably in zero-shot settings. We test whether this stems from poor visual features or an inability to verbalize what the model already perceives, using a benchmark spanning classification and detection drawn from 1