Vertical Canopy Profile and the Impact of Branches on Soybean Seed Composition

Rosso, Luiz H. Moro; Reis, André Fróes de Borja; Ciampitti, Ignacio A.

doi:10.3389/fpls.2021.725767

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…Traditionally, only the labor and time-extensive method of plant component partitioning would come close to this capability, but still lacked the ability for fingerprinting ( Hintz and Albrecht, 1994 ; Raza et al., 2021 ). Sub-canopies paired with their fingerprints have the potential to further explore the unique relationships between certain fingerprint types or clusters with known canopy traits such as branching, leaf size, or leaf angle and their relationships with yield and yield component traits ( Feng et al., 2018 ; Bianchi et al., 2020 ; Moro Rosso et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies

et al. 2023

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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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Section: Resultsmentioning

confidence: 99%

“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies

et al. 2023

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Soybean seed yield, protein, and oil concentration for a modern and old genotype under varying row spacings

Pereyra,

Hefley,

Vara Prasad

et al. 2024

Heliyon

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Planting Density and Sowing Date Strongly Influence Canopy Characteristics and Seed Yield of Soybean in Southern Xinjiang

Xu,

Mao,

Zhang

et al. 2024

Agriculture

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Southern Xinjiang is an important soybean production region in China. However, the short growing season and the cultivation of winter crops (such as wheat) in the region limit the expansion of soybean planting areas. An increased planting density can compensate for the loss in yield due to delayed sowing. To identify the quantitative relationship between increased density and delayed days, a two-year field experiment was conducted at the Tarim University Agronomy Experiment Station. Two sowing dates (April 7 (S1) and May 7 (S2)) and three planting densities of 206,800 plants·ha−1 (D1), 308,600 plants·ha−1 (D2), and 510,200 plants·ha−1 (D3) were used to compare various plant growth parameters and canopy characteristics. Late sowing and a high planting density significantly increased the plant height (S2 was 37.3% higher than S1, and D3 was 17.6% and 8.8% higher than D1 and D2), main stem internode, petiole length, and the mean tilt angle of the leaves (S2 was 22.5% higher than S1, and D3 was 11.7% higher than D2) but reduced the stem diameter (D3 was 28.6% and 12.5% lower than D1 and D2), branch number (S2 was 26.7% lower than S1, and D2 was 75% lower than D1), canopy light transmittance (S2 was 49.2% lower than S1, and D3 was 36.7% and 20.8% lower than D1 and D2), photosynthetic rate, and dry matter. The highest yield was achieved at S1D1, but the lowest yield was found for S2D1. Overall, the results suggest that earlier sowing and a lower planting density contribute to achieving an optimum canopy structure and higher yield. Our conclusions provide a reference for soybean production in southern Xinjiang.

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Vertical Canopy Profile and the Impact of Branches on Soybean Seed Composition

Cited by 3 publications

References 48 publications

“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies

“Canopy fingerprints” for characterizing three-dimensional point cloud data of soybean canopies

Soybean seed yield, protein, and oil concentration for a modern and old genotype under varying row spacings

Planting Density and Sowing Date Strongly Influence Canopy Characteristics and Seed Yield of Soybean in Southern Xinjiang

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