Benchmarking Vision-Language Models for Microscopic Plant Image Understanding

Microscopic imaging provides essential visual evidence forstudying plant biology and pathology at the cellular and subcellular lev-els. However, existing benchmarks for vision-language models primarilyfocus on macroscopic plant imagery, while the microscopic domain re-mains underexplored. To address this gap, we present PlantMicro, a com-prehensive benchmark for evaluating vision–language models (VLMs) inmicroscopic plant imagery. PlantMicro integrates more than 5,000 imagescollected from diverse hosts, biological domains, and imaging modali-ties. Building on this diversity, we design a set of complementary tasksthat capture different aspects of microscopic image understanding. Tosupport these tasks, we construct over 9,000 VQA pairs that system-atically evaluate the capabilities of VLMs. Experiments on PlantMicroshow that current VLMs struggle with fine-grained recognition and bio-logically grounded reasoning. For example, GPT-5 achieves 34.93% accu-racy on the pathogen classification task, which is only modestly above a24.95% random guessing baseline. The results highlight a significant gapin the ability of current VLMs to comprehend microscopic plant images.PlantMicro provides a standardized foundation for advancing VLMs to-ward reliable and comprehensive microscopy-level plant understanding.PlantMicro is available at https://github.com/tqwei05/PlantMicro.