Estimating Pasture Biomass from Top-View Images: A Dataset for Precision Agriculture
This dataset addresses the need for accurate pasture biomass estimation in livestock production systems, though it is incremental as it primarily provides new data rather than a novel method.
The authors tackled the problem of estimating pasture biomass for livestock management by creating a dataset of 1,162 annotated top-view images from 19 locations in Australia, paired with on-ground measurements like biomass components and NDVI, and released it in a Kaggle competition to advance precision grazing.
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