PlantInquiryVQA — Thinking Like a Botanist Benchmark and framework for multi turn, intent driven visual question answering in plant pathology. Accepted at ACL 2026 Findings . Overview PlantInquiryVQA formalises diagnostic reasoning in plant pathology as a Chain of Inquiry (CoI) — an ordered sequence of visually grounded questions that adapts to the plant's severity and the expert's epistemic intent ( Diagnosis / Prognosis / Management ). The benchmark evaluates whether modern Multimodal Large Language Models (MLLMs) can reason like a botanist , not just classify a leaf. Key findings from benchmarking 18 state of the art models: All 18 MLLMs describe symptoms competently but fail at reliable clinical reasoning (top Clinical Utility score = 0.188 / 1.0) Structured question guided inquiry improves diagnostic accuracy by ~48% over direct diagnosis Structured CoI reduces hallucination significantly compared to free form dialogue Dataset at a Glance Attribute Value Leaf images 24,950 QA pairs 138,068 Train / Test split 82,800 / 55,268 Crop species 34 Disease categories 116 Image categories disease · healthy · insect\ damage · senescence Severity levels MILD · MODERATE · SEVERE Source dat…
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