"Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.

Autor:	Young TJ; Department of Mechanical Engineering, Iowa State University, Ames, IA, United States., Jubery TZ; Translational AI Center, Iowa State University, Ames, IA, United States., Carley CN; Department of Agronomy, Iowa State University, Ames, IA, United States., Carroll M; Department of Agronomy, Iowa State University, Ames, IA, United States., Sarkar S; Department of Mechanical Engineering, Iowa State University, Ames, IA, United States.; Translational AI Center, Iowa State University, Ames, IA, United States., Singh AK; Department of Agronomy, Iowa State University, Ames, IA, United States., Singh A; Department of Agronomy, Iowa State University, Ames, IA, United States., Ganapathysubramanian B; Department of Mechanical Engineering, Iowa State University, Ames, IA, United States.; Translational AI Center, Iowa State University, Ames, IA, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in plant science [Front Plant Sci] 2023 Mar 29; Vol. 14, pp. 1141153. Date of Electronic Publication: 2023 Mar 29 (Print Publication: 2023).
DOI:	10.3389/fpls.2023.1141153
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Young, Jubery, Carley, Carroll, Sarkar, Singh, Singh and Ganapathysubramanian.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu