PlantExpertVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science
arXiv:2508.17117v3 Announce Type: replace-cross Abstract: Existing plant-disease datasets target classification and detection, leaving vision-language models unable to support interactive, reasoning-based diagnosis. To address this, we present PlantExpertVQA, a large-scale visual question answering (VQA) dataset designed to advance vision-language models for agricultural decision-making. It is compiled from 45 open-source datasets, including the widely used PlantVillage corpus, and comprises 765,186 high-quality question-answer (QA) pairs grounded over 150,841 images spanning 38 crop species a
The proliferation of advanced vision-language models and the increasing demand for data-driven agricultural solutions are making such specialized datasets crucial.
This dataset provides a vital benchmark for developing AI that can improve agricultural decision-making, potentially mitigating food security risks and optimizing crop management globally.
Vision-language models can now be specifically trained and evaluated on their ability to understand and reason about plant health, moving beyond general-purpose image classification.
- · Agricultural AI developers
- · Farmers
- · Agronomy research institutions
- · Generative AI companies
- · Traditional agricultural diagnostic methods
- · Farmers without access to advanced AI tools
Improved diagnosis of plant diseases and optimized resource allocation in agriculture become possible through AI.
Enhanced crop yields globally, potentially addressing food scarcity issues and reducing agricultural waste.
The development of highly specialized, autonomous AI agents capable of end-to-end farm management and agricultural scientific discovery.
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Read at arXiv cs.LG