Work

Beyond Disease Labels: Symptom-Based Vision-Language Learning for Unseen Plant Diseases

Year: 2026

Type: preprint

Abstract: Preprint, code and symptom annotations for learning to read visual plant-disease symptoms with vision-language models and evaluating on diseases withheld from training (10-fold unit-grouped cross-validation over 120 disease units, plus an external farmer-photo test). Code and project page: https://github.com/AmirrrezaZ/plant-diagnose-anything , https://amirrrezaz.github.io/plant-diagnose-anything/ . Code is MIT; annotations in data/ are CC BY 4.0. Photographs are not included (PlantWild, CC BY-NC-ND).

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.24032334

Author: Zibafar, Amirreza

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: green

OpenAlex ID: W7225450305

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