TreeScope: An Agricultural Robotics Dataset for LiDAR-Based Mapping of Trees in Forests and Orchards

TL;DR

TreeScope v1.0 is a pioneering robotics dataset with LiDAR data for mapping and analyzing trees in forests and orchards, aiming to improve agricultural data collection efficiency.

Contribution

This paper introduces the first robotics dataset for precision agriculture, including LiDAR data, annotations, and benchmark scripts for tree mapping and diameter estimation.

Findings

Baseline algorithms achieve promising results on tree segmentation and diameter estimation.

The dataset enables standardized evaluation of agricultural robotics algorithms.

Open-source tools facilitate further research in forestry and orchard management.

Abstract

Data collection for forestry, timber, and agriculture currently relies on manual techniques which are labor-intensive and time-consuming. We seek to demonstrate that robotics offers improvements over these techniques and accelerate agricultural research, beginning with semantic segmentation and diameter estimation of trees in forests and orchards. We present TreeScope v1.0, the first robotics dataset for precision agriculture and forestry addressing the counting and mapping of trees in forestry and orchards. TreeScope provides LiDAR data from agricultural environments collected with robotics platforms, such as UAV and mobile robot platforms carried by vehicles and human operators. In the first release of this dataset, we provide ground-truth data with over 1,800 manually annotated semantic labels for tree stems and field-measured tree diameters. We share benchmark scripts for these tasks that researchers may use to evaluate the accuracy of their algorithms. Finally, we run our open-source diameter estimation and off-the-shelf semantic segmentation algorithms and share our baseline results.