Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.22916 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911234193883136 |
|---|---|
| author | Liao, Qiyu Wang, Dadong Haling, Rebecca Liu, Jiajun Li, Xun Plomecka, Martyna Robson, Andrew Pringle, Matthew Pirie, Rhys Walker, Megan Whelan, Joshua |
| author_facet | Liao, Qiyu Wang, Dadong Haling, Rebecca Liu, Jiajun Li, Xun Plomecka, Martyna Robson, Andrew Pringle, Matthew Pirie, Rhys Walker, Megan Whelan, Joshua |
| contents | Accurate estimation of pasture biomass is important for decision-making in livestock production systems. Estimates of pasture biomass can be used to manage stocking rates to maximise pasture utilisation, while minimising the risk of overgrazing and promoting overall system health. We present a comprehensive dataset of 1,162 annotated top-view images of pastures collected across 19 locations in Australia. The images were taken across multiple seasons and include a range of temperate pasture species. Each image captures a 70cm * 30cm quadrat and is paired with on-ground measurements including biomass sorted by component (green, dead, and legume fraction), vegetation height, and Normalized Difference Vegetation Index (NDVI) from Active Optical Sensors (AOS). The multidimensional nature of the data, which combines visual, spectral, and structural information, opens up new possibilities for advancing the use of precision grazing management. The dataset is released and hosted in a Kaggle competition that challenges the international Machine Learning community with the task of pasture biomass estimation. The dataset is available on the official Kaggle webpage: https://www.kaggle.com/competitions/csiro-biomass |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22916 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimating Pasture Biomass from Top-View Images: A Dataset for Precision Agriculture Liao, Qiyu Wang, Dadong Haling, Rebecca Liu, Jiajun Li, Xun Plomecka, Martyna Robson, Andrew Pringle, Matthew Pirie, Rhys Walker, Megan Whelan, Joshua Computer Vision and Pattern Recognition Accurate estimation of pasture biomass is important for decision-making in livestock production systems. Estimates of pasture biomass can be used to manage stocking rates to maximise pasture utilisation, while minimising the risk of overgrazing and promoting overall system health. We present a comprehensive dataset of 1,162 annotated top-view images of pastures collected across 19 locations in Australia. The images were taken across multiple seasons and include a range of temperate pasture species. Each image captures a 70cm * 30cm quadrat and is paired with on-ground measurements including biomass sorted by component (green, dead, and legume fraction), vegetation height, and Normalized Difference Vegetation Index (NDVI) from Active Optical Sensors (AOS). The multidimensional nature of the data, which combines visual, spectral, and structural information, opens up new possibilities for advancing the use of precision grazing management. The dataset is released and hosted in a Kaggle competition that challenges the international Machine Learning community with the task of pasture biomass estimation. The dataset is available on the official Kaggle webpage: https://www.kaggle.com/competitions/csiro-biomass |
| title | Estimating Pasture Biomass from Top-View Images: A Dataset for Precision Agriculture |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.22916 |