1162张牧场俯视图像+多维数据，助力精准放牧管理
Estimating Pasture Biomass from Top-View Images: A Dataset for Precision Agriculture
- 采集19个澳大利亚地点的俯视图像，含植被成分与高度等多维信息
- 每张图像对应70cm×30cm样方，附带绿/枯/豆科分项生物量数据
- 面向机器学习社区，推动智能牧场管理算法发展
准确估算牧场生物量对畜牧生产决策至关重要。可通过生物量估计调整载畜率，最大化牧场利用效率，同时降低过度放牧风险并促进系统健康。本文发布一个包含1,162张标注的牧场俯视图像的综合性数据集，覆盖澳大利亚19个地点，采集于多个季节，涵盖多种温带牧草物种。每张图像对应一个70cm×30cm的样方区域，并配有地面实测数据，包括按组分（绿色、枯死、豆科）划分的生物量、植被高度以及来自主动光学传感器（AOS）的归一化差异植被指数（NDVI）。该数据集融合视觉、光谱与结构信息，为推进精准放牧管理提供了新可能。数据集已发布于Kaggle竞赛平台，向全球机器学习社区开放挑战牧场生物量估算任务。
原文摘要 · Abstract (English)
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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