LFuji-air dataset: Annotated 3D LiDAR point clouds of Fuji apple trees for fruit detection scanned under different forced air flow conditions

Abstract

This article presents the LFuji-air dataset, which contains LiDAR based point clouds of 11 Fuji apples trees and the corresponding apples location ground truth. A mobile terrestrial laser scanner (MTLS) comprised of a LiDAR sensor and a real-time kinematics global navigation satellite system was used to acquire the data. The MTLS was mounted on an air-assisted sprayer used to generate different air flow conditions. A total of 8 scans per tree were performed, including scans from different LiDAR sensor positions (multi-view approach) and under different air flow conditions. These variability of the scanning conditions allows to use the LFuji-air dataset not only for training and testing new fruit detection algorithms, but also to study the usefulness of the multi-view approach and the application of forced air flow to reduce the number of fruit occlusions. The data provided in this article is related to the research article entitled "Fruit detection, yield prediction and canopy geometric characterization using LiDAR with forced air flow" [1].

Plant phenotyping relevance

リンゴ果実の位置を含む3D LiDARデータセットを構築し、果実検出、マルチビュー、遮蔽低減の評価に利用できる再利用可能なフェノタイピング基盤であるため。

Code and data availability

The paper's own LFuji-air dataset (annotated 3D LiDAR point clouds of Fuji apple trees with apple location ground truth) is publicly available at the authors' GRAP-UdL dataset pages, and the authors' point cloud generation and fruit detection code is publicly available on GitHub.