Dataset Card for AGM_HS Dataset

Dataset Summary

The AGM_HS (AGricolaModerna Healthy-Stress) Dataset is an extension of the AGM Dataset, specifically curated to address the challenge of detecting and localizing plant stress in top-view images of harvested crops. This dataset comprises 6,127 high-resolution RGB images, each with a resolution of 120x120 pixels, selected from the original AGM Dataset. The images in AGM_HS are divided into two categories: healthy samples (3,798 images) and stressed samples (2,329 images) representing 14 of the 18 classes present in AGM. Alongside the healthy/stressed classification labels, the dataset also provides segmentation masks for the stressed areas.

Supported Tasks

Image classification: Healthy-stressed classification Image segmentation: detection and localization of plant stress in top-view images.

Languages

The dataset primarily consists of image data and does not involve language content. Therefore, the primary language is English, but it is not relevant to the dataset's core content.

Dataset Structure

Data Instances

A typical data instance from the AGM_HS Dataset consists of the following:

{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=120x120 at 0x29CEAD71780>,
'labels': 'stressed',
'crop_type': 'by'
'mask': <PIL.PngImagePlugin.PngImageFile image mode=L size=120x120 at 0x29CEAD71780>
}

Data Fields

The dataset's data instances have the following fields:

• image: A PIL.Image.Image object representing the image.
• labels: A string representation indicating whether the image is "healthy" or "stressed."
• crop_type: An string representation of the crop type in the image
• mask: A PIL.Image.Image object representing the segmentation mask of stressed areas in the image, stored as a PNG image.

Data Splits

• Training Set:
  • Number of Examples: 6,127
  • Healthy Samples: 3,798
  • Stressed Samples: 2,329

Dataset Creation

Curation Rationale

The AGM_HS Dataset was created as an extension of the AGM Dataset to specifically address the challenge of detecting and localizing plant stress in top-view images of harvested crops. This dataset is essential for the development and evaluation of advanced segmentation models tailored for this task.

Source Data

Initial Data Collection and Normalization

The images in AGM_HS were extracted from the original AGM Dataset. During the extraction process, labelers selected images showing clear signs of either good health or high stress. These sub-images were resized to 120x120 pixels to create AGM_HS.

Annotations

Annotation Process

The AGM_HS Dataset underwent a secondary stage of annotation. Labelers manually collected images by clicking on points corresponding to stressed areas on the leaves. These clicked points served as prompts for the semi-automatic generation of segmentation masks using the "Segment Anything" technique \cite{kirillov2023segment}. Each image is annotated with a classification label ("healthy" or "stressed") and a corresponding segmentation mask.

Who Are the Annotators?

The annotators for AGM_HS are domain experts with knowledge of plant health and stress detection.

Personal and Sensitive Information

The dataset does not contain personal or sensitive information about individuals. It exclusively consists of images of plants.

Considerations for Using the Data

Social Impact of Dataset

The AGM_HS Dataset plays a crucial role in advancing research and technologies for plant stress detection and localization in the context of modern agriculture. By providing a diverse set of top-view crop images with associated segmentation masks, this dataset can facilitate the development of innovative solutions for sustainable agriculture, contributing to increased crop health, yield prediction, and overall food security.

Discussion of Biases and Known Limitations

While AGM_HS is a valuable dataset, it inherits some limitations from the original AGM Dataset. It primarily involves images from a single vertical farm setting, potentially limiting the representativeness of broader agricultural scenarios. Additionally, the dataset's composition may reflect regional agricultural practices and business-driven crop preferences specific to vertical farming. Researchers should be aware of these potential biases when utilizing AGM_HS for their work.

Additional Information

Dataset Curators

The AGM_HS Dataset is curated by DeepPlants and AgricolaModerna. For further information, please contact us at:

• nico@deepplants.com
• etienne.david@agricolamoderna.com

Licensing Information

Citation Information

If you use the AGM_HS dataset in your work, please consider citing the following publication:

@InProceedings{Sama_2023_ICCV,
    author    = {Sama, Nico and David, Etienne and Rossetti, Simone and Antona, Alessandro and Franchetti, Benjamin and Pirri, Fiora},
    title     = {A new Large Dataset and a Transfer Learning Methodology for Plant Phenotyping in Vertical Farms},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
    month     = {October},
    year      = {2023},
    pages     = {540-551}
}