Expand dataset card with full documentation and Case Study 2 description

README.md

@@ -10,6 +10,7 @@ tags:
   - multispectral-imaging
   - computer-vision
   - remote-sensing
+  - cold-stress
 task_categories:
   - image-segmentation
   - object-detection
@@ -17,39 +18,229 @@ task_categories:
 
 # PGP v2 Dataset — Release Package
 
-Companion dataset for: *A Data-Driven Image Extraction and Analysis Pipeline for Plant
-Phenotyping in Controlled Environments* (Orvati Nia, Peeples, et al.).
+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 (original
-> 4-band 16-bit TIFF frames) is being added in a follow-up update — see the Files table below
-> for per-file status.
+> 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.
 
-See **`DATASET_MANIFEST.csv`** in this folder for the full breakdown (file-by-file contents,
-item counts, filename convention, dataset summary matching the paper's Table 1, known data
-gaps, and citation info). This README is a quick-reference summary of the same release.
+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.
 
-## Files (all final, built successfully, zero errors)
+Consecutive frames intentionally overlap spatially so that they can be used for:
 
-| File | Contents | Size | Status |
-|---|---|---|---|
-| `core/rice_core.zip` | pseudo-RGB + masks + COCO annotations | 0.28 GB | ✅ available |
-| `core/sorghum_core.zip` | pseudo-RGB + masks + COCO annotations | 3.83 GB | ✅ available |
-| `core/maize_core.zip` | pseudo-RGB + masks + COCO annotations | 5.10 GB | ✅ available |
-| `core/cotton_core.zip` | pseudo-RGB + masks + COCO annotations | 10.08 GB | ✅ available |
-| `raw_multispectral/rice_multispectral.zip` | raw 4-band 16-bit TIFF frames | 1.31 GB | ⏳ coming soon |
-| `raw_multispectral/sorghum_multispectral.zip` | raw 4-band 16-bit TIFF frames | 10.40 GB | ⏳ coming soon |
-| `raw_multispectral/maize_multispectral.zip` | raw 4-band 16-bit TIFF frames | 16.22 GB | ⏳ coming soon |
-| `raw_multispectral/cotton_multispectral.zip` | raw 4-band 16-bit TIFF frames | 31.62 GB | ⏳ coming soon |
+- plant-instance association
+- frame-to-frame matching
+- image stitching
+- full-plant reconstruction
+- trait aggregation across views
 
-**Core bundles total: 19.29 GB**  |  **Raw multispectral total: 59.55 GB**  |  **Grand total: 78.83 GB**
+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 | ⏳ coming soon |
+| `raw_multispectral/sorghum_multispectral.zip` | Sorghum | 4-band 16-bit TIFF frames | 10,607 | 10.40 GB | ⏳ coming soon |
+| `raw_multispectral/maize_multispectral.zip` | Maize | 4-band 16-bit TIFF frames | 14,448 | 16.22 GB | ⏳ coming soon |
+| `raw_multispectral/cotton_multispectral.zip` | Cotton | 4-band 16-bit TIFF frames | 27,660 | 31.62 GB | ⏳ coming soon |
+
+### 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 | ⏳ coming soon |
+| `case_study_2/Maize-CS-2-RGB-2.zip` | Maize (CERCA cold-stress) | RGB images, Canon EOS M10 | part of 50 total | ⏳ coming soon |
+
+**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?
 
-Most users only need the `core/` bundle for their crop(s) of interest — pseudo-RGB images plus
-semantic/instance segmentation masks and COCO instance annotations. `raw_multispectral/` is
-optional, for users who need the original 4-band 16-bit TIFF frames instead of the processed
-pseudo-RGB composites.
+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
 
@@ -58,9 +249,16 @@ pseudo-RGB composites.
 - 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.*
+Plant Phenotyping in Controlled Environments.* The Plant Phenome Journal.
 
 Pipeline code: https://github.com/Advanced-Vision-and-Learning-Lab/Plant_Analysis_Tool_Pipeline