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

---

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

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:

@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) 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.