Deep semantic segmentation of 3D plant point clouds

Heiwolt, Karoline, Duckett, Tom and Cielniak, Grzegorz (2021) Deep semantic segmentation of 3D plant point clouds. In: Towards Autonomous Robotic Systems Conference, 8th-10th September 2021, Lincoln, UK.

Full content URL: https://doi.org/10.1007/978-3-030-89177-0_4

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Deep semantic segmentation of 3D plant point clouds
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Abstract

Plant phenotyping is an essential step in the plant breeding cycle, necessary to ensure food safety for a growing world population. Standard procedures for evaluating three-dimensional plant morphology and extracting relevant phenotypic characteristics are slow, costly, and in need of automation. Previous work towards automatic semantic segmentation of plants relies on explicit prior knowledge about the species and sensor set-up, as well as manually tuned parameters. In this work, we propose to use a supervised machine learning algorithm to predict per-point semantic annotations directly from point cloud data of whole plants and minimise the necessary user input. We train a PointNet++ variant on a fully annotated procedurally generated data set of partial point clouds of tomato plants, and show that the network is capable of distinguishing between the semantic classes of leaves, stems, and soil based on structural data only. We present both quantitative and qualitative evaluation results, and establish a proof of concept, indicating that deep learning is a promising approach towards replacing the current complex, laborious, species-specific, state-of-the-art plant segmentation procedures.

Keywords:plant phenotyping, semantic segmentation, 3D perception
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:46669
Deposited On:16 Dec 2021 10:20

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