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

Created by
  • Haebom

Author

Syed Nazmus Sakib, Nafiul Haque, Mohammad Zabed Hossain, Shifat E. Arman

Outline

PlantVillageVQA is a large-scale visual question-answering (VQA) dataset based on the widely used PlantVillage image dataset. It is designed to advance the development and evaluation of visual-language models for agricultural decision-making and analysis. It consists of 193,609 high-quality question-answer (QA) pairs based on 55,448 images covering 14 crop species and 38 disease states. The questions are organized into three levels of cognitive complexity and nine distinct categories. Each question category was manually crafted with expert guidance and generated through an automated two-stage pipeline: Stage 1: template-based QA synthesis using image metadata; Stage 2: multi-stage linguistic reconstruction. The dataset was repeatedly reviewed by domain experts for scientific accuracy and relevance. The final dataset was evaluated using three state-of-the-art models for quality assessment. The goal of this study is to provide a publicly available, standardized, and expert-validated database to improve the diagnostic accuracy of plant disease identification and advance scientific research in agriculture. The dataset will be made public in https://huggingface.co/datasets/SyedNazmusSakib/PlantVillageVQA .

Takeaways, Limitations

Takeaways:
Providing a large-scale, high-quality VQA dataset for developing and evaluating visual-language models in the agricultural field.
Contributes to improving the accuracy of plant disease diagnosis.
Contribute to the advancement of scientific research in the agricultural field.
Providing standardized datasets that have been verified by experts.
Limitations:
Further validation studies are needed on the size and quality of the dataset.
Further analysis of the limitations of the question generation pipeline is needed.
Potential for data imbalances for specific crops or diseases.
Further research is needed on its applicability in real agricultural environments.
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