Snap and diagnose: An advanced multimodal retrieval system for identifying plant diseases in the wild

Paper

Wei, Tianqi, Chen, Zhi and Yu, Xin. 2024. "Snap and diagnose: An advanced multimodal retrieval system for identifying plant diseases in the wild." 6th ACM International Conference on Multimedia in Asia (MMAsia '24). Auckland, New Zealand 03 - 06 Dec 2024 United States. Association for Computing Machinery (ACM). https://doi.org/10.1145/3696409.3700293
Paper/Presentation Title

Snap and diagnose: An advanced multimodal retrieval system for identifying plant diseases in the wild

Presentation TypePaper
AuthorsWei, Tianqi, Chen, Zhi and Yu, Xin
Journal or Proceedings TitleProceedings of the 6th ACM International Conference on Multimedia in Asia (MMAsia '24)
Journal Citationpp. 1-3
Article Number131
Number of Pages3
Year2024
PublisherAssociation for Computing Machinery (ACM)
Place of PublicationUnited States
ISBN9798400712739
Digital Object Identifier (DOI)https://doi.org/10.1145/3696409.3700293
Web Address (URL) of Paperhttps://dl.acm.org/doi/10.1145/3696409.3700293
Web Address (URL) of Conference Proceedingshttps://dl.acm.org/doi/proceedings/10.1145/3696409
Conference/Event6th ACM International Conference on Multimedia in Asia (MMAsia '24)
Event Details
6th ACM International Conference on Multimedia in Asia (MMAsia '24)
Delivery
In person
Event Date
03 to end of 06 Dec 2024
Event Location
Auckland, New Zealand
Abstract

Plant disease recognition is a critical task that ensures crop health and mitigates the damage caused by diseases. A handy tool that enables farmers to receive a diagnosis based on query pictures or text descriptions of suspicious plants is in high demand for initiating treatment before potential diseases spread further. In this paper, we develop a multimodal plant disease image retrieval system to support disease search based on either image or text prompts. Specifically, we utilize the largest in-the-wild plant disease dataset PlantWild, which includes over 18,000 images across 89 categories, to provide a comprehensive view of potential diseases relating to the query. Furthermore, cross-modal retrieval is achieved in the developed system, facilitated by a novel CLIP-based vision-language model that encodes both disease descriptions and disease images into the same latent space. Built on top of the retriever, our retrieval system allows users to upload either plant disease images or disease descriptions to retrieve the corresponding images with similar characteristics from the disease dataset to suggest candidate diseases for end users’ consideration.

KeywordsPlant disease recognition; Multimodal image retrieval; Vision language models
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
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Byline AffiliationsUniversity of Queensland
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https://research.usq.edu.au/item/zyx4x/snap-and-diagnose-an-advanced-multimodal-retrieval-system-for-identifying-plant-diseases-in-the-wild

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