MegaPlant

A consolidated leaf-image dataset designed to support plant disease classification models that generalize across diverse environmental conditions, from controlled laboratory settings to highly variable in-field scenarios. MegaPlant integrates multiple publicly available datasets and standardizes them into a unified taxonomy of healthy and diseased leaf categories, enabling robust training across modalities.

Download

This is the recommended way to download the dataset to avoid downloading the foundational datasets, daimos.zip, plantvillage.zip and plantdoc.zip.

from huggingface_hub import HfApi

api = HfApi()

path = api.hf_hub_download(
        repo_id="chrisandrei/MegaPlant",
        repo_type="dataset",
        filename="leaves.zip",
    )

Datasets integrated

Dataset	Authors	Description	Retrieved from
PlantVillage	https://doi.org/10.48550/arXiv.1511.08060	Laboratory conditions, small images	https://www.kaggle.com/datasets/nirmalsankalana/plantdoc-dataset
PlantDoc	https://doi.org/10.1145/3371158.3371196	Field and laboratory conditions, stock-photos, small images	https://www.kaggle.com/datasets/nirmalsankalana/plantdoc-dataset
DiaMOS	https://doi.org/10.3390/agronomy11112107	Field conditions, large high quality images	https://zenodo.org/records/5557313

Citation

If you use this dataset, please cite it as below.

@misc{Irag_MegaPlant_An_integrated,
author = {Irag, Chris Andrei and Pramio, Ashley and Mendoza, Monique Antoinette and Dela Vega, Rod Vincent},
title = {{MegaPlant: An integrated image classification dataset of laboratory and field images}},
url = {https://iragca.github.io/DS413-final-project/}
}