Datasets:

AVLL
/

PGP_v2

---
license: cc-by-4.0
pretty_name: "PGP v2: A Multispecies Plant Phenotyping Dataset with RGB and Multispectral Images"
tags:
  - plant-phenotyping
  - agriculture
  - agronomy
  - image-segmentation
  - object-detection
  - multispectral-imaging
  - computer-vision
  - remote-sensing
  - cold-stress
task_categories:
  - image-segmentation
  - object-detection
---
# PGP v2 Dataset — Release Package

Companion dataset for:

*A Data-Driven Image Extraction and Analysis Pipeline for Plant Phenotyping in Controlled Environments*
Fahimeh Orvati Nia, Joshua Peeples, et al. — *The Plant Phenome Journal*

This release contains the Plant Growth and Phenotyping version 2 (PGP v2) dataset collected in the
Texas A&M AgriLife Automated Precision Phenotyping Greenhouse (APPG). PGP v2 expands the earlier
PGP v1 release and includes longitudinal multispectral greenhouse imagery of maize, cotton, rice,
and sorghum, together with processed pseudo-RGB images and annotation products used for
computer-vision and plant-phenotyping analyses. It also includes an independent maize cold-stress
RGB dataset (Case Study 2) used to evaluate the generalizability of the analysis pipeline beyond
the APPG robotic imaging system.

> **Release status:** the `core/` bundle (pseudo-RGB images, segmentation masks, and COCO
> annotations for all four crops) is available now. The `raw_multispectral/` bundle and the
> Case Study 2 (CERCA maize cold-stress) bundle are being added in follow-up updates — see the
> Files table below for per-file status.
See **`DATASET_MANIFEST.csv`** in this folder for the release-level inventory, package sizes,
file counts, filename patterns, examples, and package-specific notes.
## Dataset at a glance
The manuscript reports the following PGP v2 image collection:
| Crop | Date range | Imaging sessions | Imaging frequency | Number of plants | Frames per plant | Total images |
|---|---|---|---|---|---|---|
| Maize | Dec 2023–Jul 2024 | 40 | 3–4×/week | 271 | 6–15 | 14,448 |
| Cotton | Apr 2024–Jul 2024 | 66 | Daily | 12 | 5–10 | 27,660 |
| Sorghum | Dec 2024–May 2025 | 17 | Weekly | 48 | 13 | 10,608 |
| Rice | May 2024–Aug 2024 | 8 | Weekly | 8 | 10–26 | 1,376 |
Total reported in the manuscript: **54,092 images**.
## What is in this dataset?
PGP v2 contains repeated plant-imaging sessions collected in a controlled greenhouse. Plants were
imaged on fixed benches using a robotic gantry. During an imaging run, the camera moved through the
greenhouse and captured vertically stacked sequences of overlapping frames from the plant base
toward the canopy apex.
The dataset is intended to support tasks including:
- plant detection and segmentation
- semantic and instance segmentation
- plant-instance tracking across vertically stacked frames
- image stitching / plant mosaic reconstruction
- vegetation-index analysis
- texture analysis
- morphological trait extraction
- longitudinal plant phenotyping
- development and evaluation of computer-vision and deep-learning methods
The full analysis pipeline used in the companion manuscript is available at:
https://github.com/Advanced-Vision-and-Learning-Lab/Plant_Analysis_Tool_Pipeline

Project website: https://advanced-vision-and-learning-lab.github.io/PGP_Website/
## Multispectral imaging system
APPG multispectral images were collected using the MSISAGRI1A multispectral camera (Spectral
Devices Inc.). The camera uses a 4-megapixel CMOS sensor and captures four spectral bands
simultaneously:
| Band | Center wavelength | Description in the manuscript |
|---|---|---|
| Yellow / yellow-green | 580 nm | Used as the surrogate visible/green channel in the analysis pipeline |
| Red | 660 nm | Visible red band |
| Red-edge | 735 nm | Red-edge spectral band |
| Near-infrared (NIR) | 820 nm | Near-infrared spectral band |
Each multispectral frame is stored as a 16-bit TIFF image at 512 × 512 pixels with the four
spectral channels.
The imaging system used synchronized LED illumination and AntiXTalk technology to reduce
inter-band leakage. Exposure time and gain were held fixed at 1000 µs and 1.0, respectively, and
LED illumination settings were held constant across imaging sessions.
**Important spectral-data limitation:** the raw multispectral images were not converted to
absolute reflectance. The manuscript states that no dark-reference correction, white-reference
correction, sensor non-uniformity correction, or other radiometric calibration was applied before
analysis. Therefore, spectral measurements in this release should be interpreted as image-derived
spectral intensities, not absolute reflectance measurements.
## Pseudo-RGB images
For compatibility with standard computer-vision models, the four-band multispectral images were
converted to 8-bit pseudo-RGB composites. Each spectral band was independently min-max normalized
to the 0–255 range and cast to unsigned 8-bit format. The pseudo-RGB composite uses:
- 580 nm yellow/yellow-green band as the surrogate green channel
- 735 nm red-edge band
- 660 nm red band
The 820 nm NIR band was retained separately for vegetation-index and spectral analysis.
This normalization is a processing step for visualization and model compatibility and does not
constitute radiometric or color calibration across imaging sessions.
## Case Study 2 — CERCA maize cold-stress dataset
This release also includes RGB images from an independent maize cold-stress experiment conducted
at the University of Nebraska–Lincoln CERCA facility, used to evaluate the generalizability of the
analysis pipeline beyond the APPG robotic imaging system. The dataset contains 50 RGB images from
10 maize genotypes, with one image per genotype under five conditions: before treatment, after
10 °C treatment, after 4 °C treatment, recovery following 10 °C treatment, and recovery following
4 °C treatment. Images were acquired using a Canon EOS M10 camera and are provided in
`Maize-CS-2-RGB-1.zip` and `Maize-CS-2-RGB-2.zip`. The companion manuscript uses these images to
evaluate RGB-derived vegetation-index, texture, and morphological features for characterizing
variation associated with cold stress and recovery.
## Image acquisition and sequence structure
Each plant is represented by a vertical sequence of frames captured at successive robot positions
along the Z-axis. Taller plants require more frames to cover the complete plant profile.
For maize, cotton, and sorghum, the manuscript reports an approximate 50 mm vertical spacing
between consecutive camera positions, with scans beginning near a robot Z-position of 925 mm. The
upper scan position was selected according to crop and canopy height.
Consecutive frames intentionally overlap spatially so that they can be used for:
- plant-instance association
- frame-to-frame matching
- image stitching
- full-plant reconstruction
- trait aggregation across views
A fixed numerical overlap percentage was not recorded in the acquisition metadata.
## Recorded acquisition metadata
For each image acquisition, the APPG system records metadata including: species, room number,
date, time, image index, row number, plant number, orientation, plant spatial coordinates, and
robot spatial coordinates. These metadata support plant identification, spatial calibration, and
longitudinal tracking.
Treatment and genotype annotations are not uniformly available for every component of PGP v2.
Where available, those annotations are included in the associated metadata.
## Release files
The release is organized into three logical bundle types.
### Core bundles
Core bundles contain processed imagery and annotation products intended for most computer-vision
users.
| File | Crop | Contents | Manifest item count | Approx. size | Status |
|---|---|---|---|---|---|
| `core/rice_core.zip` | Rice | pseudo-RGB images + semantic masks + instance masks + COCO annotations | 2,791 | 0.28 GB | ✅ available |
| `core/sorghum_core.zip` | Sorghum | pseudo-RGB images + semantic masks + instance masks + COCO annotations | 17,609 | 3.85 GB | ✅ available |
| `core/maize_core.zip` | Maize | pseudo-RGB images + semantic masks + instance masks + COCO annotations | 19,864 | 5.11 GB | ✅ available |
| `core/cotton_core.zip` | Cotton | pseudo-RGB images + semantic masks + instance masks + COCO annotations | 71,648 | 10.08 GB | ✅ available |

*Important: the core-bundle item count is the number of packaged files/items and should not be
interpreted as the number of unique raw plant images, because each bundle can contain images,
masks, and annotation files.*

### Raw multispectral bundles

Raw multispectral bundles contain the original four-band 16-bit TIFF frames.

| File | Crop | Contents | Raw frame count in release manifest | Approx. size | Status |
|---|---|---|---|---|---|
| `raw_multispectral/rice_multispectral.zip` | Rice | 4-band 16-bit TIFF frames | 1,376 | 1.31 GB | ✅ available |
| `raw_multispectral/sorghum_multispectral.zip` | Sorghum | 4-band 16-bit TIFF frames | 10,607 | 10.40 GB | ✅ available |
| `raw_multispectral/maize_multispectral.zip` | Maize | 4-band 16-bit TIFF frames | 14,448 | 16.22 GB | ✅ available |
| `raw_multispectral/cotton_multispectral.zip` | Cotton | 4-band 16-bit TIFF frames | 27,660 | 31.62 GB | ✅ available |

### Case Study 2 bundle

| File | Crop | Contents | Item count | Status |
|---|---|---|---|---|
| `case_study_2/Maize-CS-2-RGB-1.zip` | Maize (CERCA cold-stress) | RGB images, Canon EOS M10 | part of 50 total | ✅ available |
| `case_study_2/Maize-CS-2-RGB-2.zip` | Maize (CERCA cold-stress) | RGB images, Canon EOS M10 | part of 50 total | ✅ available |

**Core bundles total: 19.29 GB** &nbsp;|&nbsp; **Raw multispectral total: 59.55 GB** &nbsp;|&nbsp;
**Grand total (core + raw multispectral): 78.83 GB** &nbsp;|&nbsp; **Case Study 2: to be added**

## Which files do I need?

For most computer-vision and segmentation work, download the `core/` bundle for the crop(s) of
interest. Use the `raw_multispectral/` bundle when you need the original four-band 16-bit TIFF
data for spectral analysis, vegetation-index development, alternative pseudo-color construction,
custom normalization or preprocessing, or methods that operate directly on multispectral channels.
If you need both processed products and the original spectral data, download both the core and raw
multispectral bundles for that crop. Use the `case_study_2/` bundle if you are evaluating
pipeline generalizability on an independent RGB dataset with a cold-stress treatment design.

## Filename conventions

Filename patterns differ slightly across crops.

**Rice core:** `crop-date-plantN-frameN.ext`
Example: `rice-2024-05-29-plant1-frame1.png`

**Sorghum core and raw multispectral:** `crop-date-plantN-frameN.ext`
Examples: `sorghum-2024-12-04-plant10-frame1.png`, `sorghum-2024-12-04-plant10-frame1.tif`

**Maize and cotton:** `crop-date-rowN-cameraN-frameN.ext`
Examples: `maize-2024-06-24-row1-camera2-frame21.png`, `maize-2024-06-24-row1-camera2-frame21.tif`,
`cotton-2024-06-24-row1-camera2-frame21.png`, `cotton-2024-06-24-row1-camera2-frame21.tif`

**Rice raw multispectral:** `crop-date-cameraN-plantN-frameN.ext`
Example: `rice-2024-05-29-camera1-plant1-frame1.tif`

## Data organization and intended reuse

PGP v2 was designed to support longitudinal and multi-crop controlled-environment phenotyping.
Data are organized around crop identity, imaging date, plant identity, and frame position.

Potential reuse includes: benchmarking plant segmentation algorithms, training segmentation and
detection models, testing instance-tracking methods, evaluating image-stitching algorithms,
extracting plant morphology and canopy traits, computing vegetation indices, studying spectral and
texture features, developing self-supervised learning methods, and longitudinal analysis of plant
growth and stress.

The manuscript reports that the full processing framework generated 863 quantitative features per
plant and imaging date, spanning vegetation indices, spectral statistics, texture descriptors, and
morphological traits. These derived features are products of the companion analysis pipeline
rather than a definition of every file included in the release package.

## Hosting plan

- **Hugging Face Hub** (`fahimehorvatinia/PGP_v2`) — primary host, for the citable DOI and for
  practical browsing / `load_dataset(...)` access; handles the full file/byte counts comfortably.
- Link the permanent host from the existing project site:
  https://advanced-vision-and-learning-lab.github.io/PGP_Website/
## Related resources
- Project website: https://advanced-vision-and-learning-lab.github.io/PGP_Website/
- Analysis pipeline: https://github.com/Advanced-Vision-and-Learning-Lab/Plant_Analysis_Tool_Pipeline
## Citation
Please cite the companion article when using this dataset:
Orvati Nia, F., Peeples, J., et al. *A Data-Driven Image Extraction and Analysis Pipeline for
Plant Phenotyping in Controlled Environments.* The Plant Phenome Journal.
Pipeline code: https://github.com/Advanced-Vision-and-Learning-Lab/Plant_Analysis_Tool_Pipeline

	---
	license: cc-by-4.0
	pretty_name: "PGP v2: A Multispecies Plant Phenotyping Dataset with RGB and Multispectral Images"
	tags:
	- plant-phenotyping
	- agriculture
	- agronomy
	- image-segmentation
	- object-detection
	- multispectral-imaging
	- computer-vision
	- remote-sensing
	- cold-stress
	task_categories:
	- image-segmentation
	- object-detection
	---

	# PGP v2 Dataset — Release Package

	Companion dataset for:

	A Data-Driven Image Extraction and Analysis Pipeline for Plant Phenotyping in Controlled Environments
	Fahimeh Orvati Nia, Joshua Peeples, et al. — The Plant Phenome Journal

	This release contains the Plant Growth and Phenotyping version 2 (PGP v2) dataset collected in the
	Texas A&M AgriLife Automated Precision Phenotyping Greenhouse (APPG). PGP v2 expands the earlier
	PGP v1 release and includes longitudinal multispectral greenhouse imagery of maize, cotton, rice,
	and sorghum, together with processed pseudo-RGB images and annotation products used for
	computer-vision and plant-phenotyping analyses. It also includes an independent maize cold-stress
	RGB dataset (Case Study 2) used to evaluate the generalizability of the analysis pipeline beyond
	the APPG robotic imaging system.

	> Release status: the `core/` bundle (pseudo-RGB images, segmentation masks, and COCO
	> annotations for all four crops) is available now. The `raw_multispectral/` bundle and the
	> Case Study 2 (CERCA maize cold-stress) bundle are being added in follow-up updates — see the
	> Files table below for per-file status.

	See `DATASET_MANIFEST.csv` in this folder for the release-level inventory, package sizes,
	file counts, filename patterns, examples, and package-specific notes.

	## Dataset at a glance

	The manuscript reports the following PGP v2 image collection:

	\| Crop \| Date range \| Imaging sessions \| Imaging frequency \| Number of plants \| Frames per plant \| Total images \|
	\|---\|---\|---\|---\|---\|---\|---\|
	\| Maize \| Dec 2023–Jul 2024 \| 40 \| 3–4×/week \| 271 \| 6–15 \| 14,448 \|
	\| Cotton \| Apr 2024–Jul 2024 \| 66 \| Daily \| 12 \| 5–10 \| 27,660 \|
	\| Sorghum \| Dec 2024–May 2025 \| 17 \| Weekly \| 48 \| 13 \| 10,608 \|
	\| Rice \| May 2024–Aug 2024 \| 8 \| Weekly \| 8 \| 10–26 \| 1,376 \|

	Total reported in the manuscript: 54,092 images.

	## What is in this dataset?

	PGP v2 contains repeated plant-imaging sessions collected in a controlled greenhouse. Plants were
	imaged on fixed benches using a robotic gantry. During an imaging run, the camera moved through the
	greenhouse and captured vertically stacked sequences of overlapping frames from the plant base
	toward the canopy apex.

	The dataset is intended to support tasks including:

	- plant detection and segmentation
	- semantic and instance segmentation
	- plant-instance tracking across vertically stacked frames
	- image stitching / plant mosaic reconstruction
	- vegetation-index analysis
	- texture analysis
	- morphological trait extraction
	- longitudinal plant phenotyping
	- development and evaluation of computer-vision and deep-learning methods

	The full analysis pipeline used in the companion manuscript is available at:
	https://github.com/Advanced-Vision-and-Learning-Lab/Plant_Analysis_Tool_Pipeline

	Project website: https://advanced-vision-and-learning-lab.github.io/PGP_Website/

	## Multispectral imaging system

	APPG multispectral images were collected using the MSISAGRI1A multispectral camera (Spectral
	Devices Inc.). The camera uses a 4-megapixel CMOS sensor and captures four spectral bands
	simultaneously:

	\| Band \| Center wavelength \| Description in the manuscript \|
	\|---\|---\|---\|
	\| Yellow / yellow-green \| 580 nm \| Used as the surrogate visible/green channel in the analysis pipeline \|
	\| Red \| 660 nm \| Visible red band \|
	\| Red-edge \| 735 nm \| Red-edge spectral band \|
	\| Near-infrared (NIR) \| 820 nm \| Near-infrared spectral band \|

	Each multispectral frame is stored as a 16-bit TIFF image at 512 × 512 pixels with the four
	spectral channels.

	The imaging system used synchronized LED illumination and AntiXTalk technology to reduce
	inter-band leakage. Exposure time and gain were held fixed at 1000 µs and 1.0, respectively, and
	LED illumination settings were held constant across imaging sessions.

	Important spectral-data limitation: the raw multispectral images were not converted to
	absolute reflectance. The manuscript states that no dark-reference correction, white-reference
	correction, sensor non-uniformity correction, or other radiometric calibration was applied before
	analysis. Therefore, spectral measurements in this release should be interpreted as image-derived
	spectral intensities, not absolute reflectance measurements.

	## Pseudo-RGB images

	For compatibility with standard computer-vision models, the four-band multispectral images were
	converted to 8-bit pseudo-RGB composites. Each spectral band was independently min-max normalized
	to the 0–255 range and cast to unsigned 8-bit format. The pseudo-RGB composite uses:

	- 580 nm yellow/yellow-green band as the surrogate green channel
	- 735 nm red-edge band
	- 660 nm red band

	The 820 nm NIR band was retained separately for vegetation-index and spectral analysis.

	This normalization is a processing step for visualization and model compatibility and does not
	constitute radiometric or color calibration across imaging sessions.

	## Case Study 2 — CERCA maize cold-stress dataset

	This release also includes RGB images from an independent maize cold-stress experiment conducted
	at the University of Nebraska–Lincoln CERCA facility, used to evaluate the generalizability of the
	analysis pipeline beyond the APPG robotic imaging system. The dataset contains 50 RGB images from
	10 maize genotypes, with one image per genotype under five conditions: before treatment, after
	10 °C treatment, after 4 °C treatment, recovery following 10 °C treatment, and recovery following
	4 °C treatment. Images were acquired using a Canon EOS M10 camera and are provided in
	`Maize-CS-2-RGB-1.zip` and `Maize-CS-2-RGB-2.zip`. The companion manuscript uses these images to
	evaluate RGB-derived vegetation-index, texture, and morphological features for characterizing
	variation associated with cold stress and recovery.

	## Image acquisition and sequence structure

	Each plant is represented by a vertical sequence of frames captured at successive robot positions
	along the Z-axis. Taller plants require more frames to cover the complete plant profile.

	For maize, cotton, and sorghum, the manuscript reports an approximate 50 mm vertical spacing
	between consecutive camera positions, with scans beginning near a robot Z-position of 925 mm. The
	upper scan position was selected according to crop and canopy height.

	Consecutive frames intentionally overlap spatially so that they can be used for:

	- plant-instance association
	- frame-to-frame matching
	- image stitching
	- full-plant reconstruction
	- trait aggregation across views

	A fixed numerical overlap percentage was not recorded in the acquisition metadata.

	## Recorded acquisition metadata

	For each image acquisition, the APPG system records metadata including: species, room number,
	date, time, image index, row number, plant number, orientation, plant spatial coordinates, and
	robot spatial coordinates. These metadata support plant identification, spatial calibration, and
	longitudinal tracking.

	Treatment and genotype annotations are not uniformly available for every component of PGP v2.
	Where available, those annotations are included in the associated metadata.

	## Release files

	The release is organized into three logical bundle types.

	### Core bundles

	Core bundles contain processed imagery and annotation products intended for most computer-vision
	users.

	\| File \| Crop \| Contents \| Manifest item count \| Approx. size \| Status \|
	\|---\|---\|---\|---\|---\|---\|
	\| `core/rice_core.zip` \| Rice \| pseudo-RGB images + semantic masks + instance masks + COCO annotations \| 2,791 \| 0.28 GB \| ✅ available \|
	\| `core/sorghum_core.zip` \| Sorghum \| pseudo-RGB images + semantic masks + instance masks + COCO annotations \| 17,609 \| 3.85 GB \| ✅ available \|
	\| `core/maize_core.zip` \| Maize \| pseudo-RGB images + semantic masks + instance masks + COCO annotations \| 19,864 \| 5.11 GB \| ✅ available \|
	\| `core/cotton_core.zip` \| Cotton \| pseudo-RGB images + semantic masks + instance masks + COCO annotations \| 71,648 \| 10.08 GB \| ✅ available \|

	*Important: the core-bundle item count is the number of packaged files/items and should not be
	interpreted as the number of unique raw plant images, because each bundle can contain images,
	masks, and annotation files.*

	### Raw multispectral bundles

	Raw multispectral bundles contain the original four-band 16-bit TIFF frames.

	\| File \| Crop \| Contents \| Raw frame count in release manifest \| Approx. size \| Status \|
	\|---\|---\|---\|---\|---\|---\|
	\| `raw_multispectral/rice_multispectral.zip` \| Rice \| 4-band 16-bit TIFF frames \| 1,376 \| 1.31 GB \| ✅ available \|
	\| `raw_multispectral/sorghum_multispectral.zip` \| Sorghum \| 4-band 16-bit TIFF frames \| 10,607 \| 10.40 GB \| ✅ available \|
	\| `raw_multispectral/maize_multispectral.zip` \| Maize \| 4-band 16-bit TIFF frames \| 14,448 \| 16.22 GB \| ✅ available \|
	\| `raw_multispectral/cotton_multispectral.zip` \| Cotton \| 4-band 16-bit TIFF frames \| 27,660 \| 31.62 GB \| ✅ available \|

	### Case Study 2 bundle

	\| File \| Crop \| Contents \| Item count \| Status \|
	\|---\|---\|---\|---\|---\|
	\| `case_study_2/Maize-CS-2-RGB-1.zip` \| Maize (CERCA cold-stress) \| RGB images, Canon EOS M10 \| part of 50 total \| ✅ available \|
	\| `case_study_2/Maize-CS-2-RGB-2.zip` \| Maize (CERCA cold-stress) \| RGB images, Canon EOS M10 \| part of 50 total \| ✅ available \|

	Core bundles total: 19.29 GB  \|  Raw multispectral total: 59.55 GB  \|
	Grand total (core + raw multispectral): 78.83 GB  \|  Case Study 2: to be added

	## Which files do I need?

	For most computer-vision and segmentation work, download the `core/` bundle for the crop(s) of
	interest. Use the `raw_multispectral/` bundle when you need the original four-band 16-bit TIFF
	data for spectral analysis, vegetation-index development, alternative pseudo-color construction,
	custom normalization or preprocessing, or methods that operate directly on multispectral channels.
	If you need both processed products and the original spectral data, download both the core and raw
	multispectral bundles for that crop. Use the `case_study_2/` bundle if you are evaluating
	pipeline generalizability on an independent RGB dataset with a cold-stress treatment design.

	## Filename conventions

	Filename patterns differ slightly across crops.

	Rice core: `crop-date-plantN-frameN.ext`
	Example: `rice-2024-05-29-plant1-frame1.png`

	Sorghum core and raw multispectral: `crop-date-plantN-frameN.ext`
	Examples: `sorghum-2024-12-04-plant10-frame1.png`, `sorghum-2024-12-04-plant10-frame1.tif`

	Maize and cotton: `crop-date-rowN-cameraN-frameN.ext`
	Examples: `maize-2024-06-24-row1-camera2-frame21.png`, `maize-2024-06-24-row1-camera2-frame21.tif`,
	`cotton-2024-06-24-row1-camera2-frame21.png`, `cotton-2024-06-24-row1-camera2-frame21.tif`

	Rice raw multispectral: `crop-date-cameraN-plantN-frameN.ext`
	Example: `rice-2024-05-29-camera1-plant1-frame1.tif`

	## Data organization and intended reuse

	PGP v2 was designed to support longitudinal and multi-crop controlled-environment phenotyping.
	Data are organized around crop identity, imaging date, plant identity, and frame position.

	Potential reuse includes: benchmarking plant segmentation algorithms, training segmentation and
	detection models, testing instance-tracking methods, evaluating image-stitching algorithms,
	extracting plant morphology and canopy traits, computing vegetation indices, studying spectral and
	texture features, developing self-supervised learning methods, and longitudinal analysis of plant
	growth and stress.

	The manuscript reports that the full processing framework generated 863 quantitative features per
	plant and imaging date, spanning vegetation indices, spectral statistics, texture descriptors, and
	morphological traits. These derived features are products of the companion analysis pipeline
	rather than a definition of every file included in the release package.

	## Hosting plan

	- Hugging Face Hub (`fahimehorvatinia/PGP_v2`) — primary host, for the citable DOI and for
	practical browsing / `load_dataset(...)` access; handles the full file/byte counts comfortably.
	- Link the permanent host from the existing project site:
	https://advanced-vision-and-learning-lab.github.io/PGP_Website/

	## Related resources

	- Project website: https://advanced-vision-and-learning-lab.github.io/PGP_Website/
	- Analysis pipeline: https://github.com/Advanced-Vision-and-Learning-Lab/Plant_Analysis_Tool_Pipeline

	## Citation

	Please cite the companion article when using this dataset:

	Orvati Nia, F., Peeples, J., et al. *A Data-Driven Image Extraction and Analysis Pipeline for
	Plant Phenotyping in Controlled Environments.* The Plant Phenome Journal.

	Pipeline code: https://github.com/Advanced-Vision-and-Learning-Lab/Plant_Analysis_Tool_Pipeline