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

TL;DR

This paper introduces a comprehensive dataset of top-view pasture images with associated biomass measurements, aiming to advance machine learning methods for precise biomass estimation in agriculture.

Contribution

It provides a large, annotated dataset combining visual, spectral, and structural data for pasture biomass estimation, facilitating research in precision agriculture.

Findings

Dataset includes 1,162 images from 19 locations in Australia.

Images are paired with detailed biomass and vegetation measurements.

The dataset is used in a Kaggle competition to promote machine learning solutions.

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