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arXiv · 2510.22916

Estimating Pasture Biomass from Top-View Images: A Dataset for Precision Agriculture

Qiyu Liao·Dadong Wang·Rebecca Haling·Jiajun Liu·Xun Li·Martyna Plomecka·Andrew Robson·Matthew Pringle·Rhys Pirie·Megan Walker·Joshua Whelan
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Abstract

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

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Qiyu Liao, Dadong Wang, Rebecca Haling, Jiajun Liu, Xun Li, Martyna Plomecka, Andrew Robson, Matthew Pringle, Rhys Pirie, Megan Walker, Joshua Whelan. 2025-10-27. Estimating Pasture Biomass from Top-View Images: A Dataset for Precision Agriculture. https://arxiv.org/abs/2510.22916

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