PlantVillageVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science

TL;DR

PlantVillageVQA is a comprehensive dataset for visual question answering in plant science, designed to improve vision-language models for agricultural diagnostics and research.

Contribution

The paper introduces a large, expert-verified VQA dataset specifically for plant disease diagnosis, with a structured question-answer format grounded in diverse plant images.

Findings

Dataset contains 193,609 QA pairs over 55,448 images.

Evaluated with three state-of-the-art models for quality.

Aims to enhance diagnostic accuracy in plant disease identification.

Abstract

PlantVillageVQA is a large-scale visual question answering (VQA) dataset derived from the widely used PlantVillage image corpus. It was designed to advance the development and evaluation of vision-language models for agricultural decision-making and analysis. The PlantVillageVQA dataset comprises 193,609 high-quality question-answer (QA) pairs grounded over 55,448 images spanning 14 crop species and 38 disease conditions. Questions are organised into 3 levels of cognitive complexity and 9 distinct categories. Each question category was phrased manually following expert guidance and generated via an automated two-stage pipeline: (1) template-based QA synthesis from image metadata and (2) multi-stage linguistic re-engineering. The dataset was iteratively reviewed by domain experts for scientific accuracy and relevancy. The final dataset was evaluated using three state-of-the-art models for quality assessment. Our objective remains to provide a publicly available, standardised and expert-verified database to enhance diagnostic accuracy for plant disease identifications and advance scientific research in the agricultural domain. Our dataset will be open-sourced at https://huggingface.co/datasets/SyedNazmusSakib/PlantVillageVQA.