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

arXiv: https://arxiv.org/abs/2510.22916 · HTML: https://mlsanity.com/paper/2510.22916/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 3 of 3)

- [DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning](https://mlsanity.com/paper/1810.05726/) (2018)
- [Semi-supervised dry herbage mass estimation using automatic data and synthetic images](https://mlsanity.com/paper/2110.13719/) (2021)
- [Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation](https://mlsanity.com/paper/2204.08271/) (2022)

## Built on by (most cited first, 1 of 1)

- [Fusion Complexity Is Not Fusion Quality: Backbone, Cross View Fusion and Metadata as Separate Factors in Pasture Biomass Regression](https://mlsanity.com/paper/2603.07819/) (2026)

## Abstract (arXiv)

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