MrigLabIITRopar/GroMo25

Tasks

• image-classification
• text-to-image
• image-to-text

Citation text from the card

@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 fro

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