“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies
Date
2023-03-28
Authors
Young, Therin J.
Jubery, Talukder Z.
Carley, Clayton N.
Carroll, Matthew
Singh, Arti
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Frontiers Media
Abstract
Advances in imaging hardware allow high throughput capture of the detailed three-dimensional (3D) structure of plant canopies. The point cloud data is typically post-processed to extract coarse-scale geometric features (like volume, surface area, height, etc.) for downstream analysis. We extend feature extraction from 3D point cloud data to various additional features, which we denote as ‘canopy fingerprints’. This is motivated by the successful application of the fingerprint concept for molecular fingerprints in chemistry applications and acoustic fingerprints in sound engineering applications. We developed an end-to-end pipeline to generate canopy fingerprints of a three-dimensional point cloud of soybean [Glycine max (L.) Merr.] canopies grown in hill plots captured by a terrestrial laser scanner (TLS). The pipeline includes noise removal, registration, and plot extraction, followed by the canopy fingerprint generation. The canopy fingerprints are generated by splitting the data into multiple sub-canopy scale components and extracting sub-canopy scale geometric features. The generated canopy fingerprints are interpretable and can assist in identifying patterns in a database of canopies, querying similar canopies, or identifying canopies with a certain shape. The framework can be extended to other modalities (for instance, hyperspectral point clouds) and tuned to find the most informative fingerprint representation for downstream tasks. These canopy fingerprints can aid in the utilization of canopy traits at previously unutilized scales, and therefore have applications in plant breeding and resilient crop production.
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Comments
This article is published as Young TJ, Jubery TZ, Carley CN, Carroll M, Sarkar S, Singh AK, Singh A and Ganapathysubramanian B (2023) “Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies. Front. Plant Sci. 14:1141153. doi: 10.3389/fpls.2023.1141153.
Rights Statement
© 2023 Young, Jubery, Carley, Carroll, Sarkar, Singh, Singh and Ganapathysubramanian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.