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readme file - full description of the dataset.

README.md

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+---
+license: cc-by-4.0
+task_categories:
+- image-classification
+- text-to-image
+- image-to-text
+language:
+- en
+tags:
+- plant-growth
+- phenotyping
+- agriculture
+- multiview
+- time-series
+- leaf-counting
+- age-estimation
+- precision-agriculture
+pretty_name: GroMo 25 — Multiview Plant Growth Dataset
+size_categories:
+- 10K<n<100K
+---
+
+# GroMo25: Multiview Time-Series Plant Image Dataset for Age Estimation and Leaf Counting
+
+## Dataset Summary
+
+**GroMo25** is a multiview, time-series plant image dataset designed for plant age estimation (in days) and leaf counting tasks in precision agriculture. It contains high-quality images of four crop species — **Wheat, Okra, Radish, and Mustard** — captured over multiple days under controlled conditions. Each plant is photographed from 24 angles across 5 vertical levels per day, providing rich spatial and temporal information for plant growth modeling.
+
+This dataset is intended for researchers and practitioners in **academic research, plant phenotyping, and agriculture**, and serves as the official dataset for the **GroMo Challenge**.
+
+>  **Paper:** [GroMo Challenge: Multiview time-series plant image dataset for age estimation and leaf counting in precision agriculture](https://doi.org/10.48550/arXiv.2503.06608)
+
+---
+
+## Dataset Structure
+
+### Crops Included
+
+| Crop    | Description                             |
+|---------|-----------------------------------------|
+| Wheat   | Cereal crop,    multiple growth stages  |
+| Okra    | Vegetable crop, leaf-heavy growth       |
+| Radish  | Root vegetable, rapid growth cycle      |
+| Mustard | Oilseed crop,   distinct leaf pattern   |
+
+### Image Capture Setup
+
+All images were captured in a **controlled indoor environment**:
+- Each plant is grown in a **pot** placed on a **rotating disk**
+- The background is covered with a **white cloth** to ensure clean segmentation and consistent background
+- Images are taken at **24 angles** per level (0° to 345°, 15° increment between consecutive angles)
+- Each plant is photographed across **5 vertical levels** (L1 to L5) per day
+
+### Images Per Day (Per Plant)
+
+```
+5 levels × 24 angles = 120 images per plant per day
+```
+
+### Directory Structure
+
+```
+dataset/
+├── train/
+│   ├── p1/
+│   │   ├── d1/
+│   │   │   ├── L1/
+│   │   │   │   ├── radish_p1_d1_L1_0.png
+│   │   │   │   ├── radish_p1_d1_L1_15.png
+│   │   │   │   ├── ...
+│   │   │   │   └── radish_p1_d1_L1_345.png
+│   │   │   ├── L2/
+│   │   │   ├── L3/
+│   │   │   ├── L4/
+│   │   │   └── L5/
+│   │   ├── d2/
+│   │   └── ...
+│   ├── p2/
+│   ├── p3/
+│   └── p4/
+└── test/
+    └── (same structure as train)
+```
+
+### File Naming Convention
+
+Each image follows the format:
+
+```
+{crop}_p{X}_d{Y}_L{Z}_{A}.png
+```
+
+| Field | Description |
+|-------|-------------|
+| `crop` | Crop name (e.g., `radish`, `wheat`) |
+| `X` | Plant ID (e.g., `p1`, `p2`) |
+| `Y` | Day number (e.g., `d1`, `d2`) |
+| `Z` | Level (L1 to L5) |
+| `A` | Angle in degrees (0 to 345, step 15) |
+
+**Example:** `radish_p1_d3_L2_90.png` → Radish, Plant 1, Day 3, Level 2, angle 90°
+
+---
+
+## Ground Truth / Annotations
+
+Each crop has a corresponding `{crop}_train.csv` file containing per-image labels.
+
+### Format
+
+| Column | Description |
+|--------|-------------|
+| `filename` | Relative path to the image |
+| `leaf_count` | Number of leaves on the plant |
+| `Age` | Age of the plant in days |
+
+### Example
+```csv
+filename,leaf_count,Age
+mustard/p1/d1/L1/mustard_p1_d1_L1_0.png,2,1
+```
+
+A separate CSV is provided for each crop covering all plants, days, levels, and angles.
+
+---
+
+## Tasks
+
+This dataset supports the following tasks:
+
+- **Plant Age Estimation** — Predict the number of days since planting (regression)
+- **Leaf Counting** — Predict the number of leaves visible on the plant (regression)
+
+### Evaluation Metrics
+
+| Metric | Description |
+|--------|-------------|
+| RMSE | Root Mean Squared Error |
+| MAE  | Mean Absolute Error |
+
+---
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@inproceedings{10.1145/3746027.3762097,
+author = {Bansal, Shreya and Bhatt, Ruchi and Chander, Amanpreet and Kaur, Rupinder and Singh, Malya and Kankanhalli, Mohan and El Saddik, Abdulmotaleb and Saini, Mukesh},
+title = {GroMo25: ACM Multimedia 2025 Grand Challenge for Plant Growth Modeling with Multiview Images},
+year = {2025},
+isbn = {9798400720352},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3746027.3762097},
+doi = {10.1145/3746027.3762097},
+abstract = {Understanding plant growth dynamics is a critical component of modern agricultural research, with applications in yield prediction, phenotyping, and sustainable crop management. Despite recent advances in computer vision and deep learning, progress in plant growth modeling has been constrained by the lack of publicly available, high-resolution, multiview, and temporally rich datasets. To address this gap, we introduce Growth Modelling GroMo25, the first international challenge on plant growth modeling using multiview imagery. In this challenge, we propose a dataset that comprises high-resolution images of four crops: wheat, mustard, radish, and okra, captured at consistent time intervals from multiple camera viewpoints under controlled environmental conditions. The challenge focuses on two key tasks: (1) plant age prediction and (2) leaf count estimation, both requiring models to use spatial and temporal plant features. GroMo25 attracted participation from multiple teams worldwide, encouraging benchmarking and innovation in vision-based plant phenotyping. The GitHub repository is publicly available at https://github.com/mriglab/GroMo-Plant-Growth-Modeling-with-Multiview-Images.},
+booktitle = {Proceedings of the 33rd ACM International Conference on Multimedia},
+pages = {14204–14209},
+numpages = {6},
+keywords = {growth age prediction, leaf count estimation, multiview},
+location = {Dublin, Ireland},
+series = {MM '25}
+}
+```
+
+---
+
+## Authors & Affiliations
+
+| Name | Institution |
+|------|------------|
+| Shreya Bansal | IIT Ropar |
+| Ruchi Bhatt | IIT Ropar |
+| Amanpreet Chander | IIT Ropar |
+| Rupinder Kaur | IIT Ropar |
+| Malya Singh | IIT Ropar |
+| Dr. Mohan Kankanhalli | National University of Singapore |
+| Abdulmotaleb El Saddik | University of Ottawa |
+| Mukesh Kumar Saini | IIT Ropar |
+
+ For queries, contact: **mrig@iitrpr.ac.in**
+
+---
+
+## License
+
+This dataset is released under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.
+
+You are free to use, share, and adapt this dataset for any purpose, including commercial use, as long as appropriate credit is given to the authors.