Literature DB >> 25752861

Using hyperspectral imaging to determine germination of native Australian plant seeds.

Christian Nansen1, Genpin Zhao2, Nicole Dakin3, Chunhui Zhao2, Shane R Turner3.   

Abstract

We investigated the ability to accurately and non-destructively determine the germination of three native Australian tree species, Acacia cowleana Tate (Fabaceae), Banksia prionotes L.F. (Proteaceae), and Corymbia calophylla (Lindl.) K.D. Hill & L.A.S. Johnson (Myrtaceae) based on hyperspectral imaging data. While similar studies have been conducted on agricultural and horticultural seeds, we are unaware of any published studies involving reflectance-based assessments of the germination of tree seeds. Hyperspectral imaging data (110 narrow spectral bands from 423.6nm to 878.9nm) were acquired of individual seeds after 0, 1, 2, 5, 10, 20, 30, and 50days of standardized rapid ageing. At each time point, seeds were subjected to hyperspectral imaging to obtain reflectance profiles from individual seeds. A standard germination test was performed, and we predicted that loss of germination was associated with a significant change in seed coat reflectance profiles. Forward linear discriminant analysis (LDA) was used to select the 10 spectral bands with the highest contribution to classifications of the three species. In all species, germination decreased from over 90% to below 20% in about 10-30days of experimental ageing. P50 values (equal to 50% germination) for each species were 19.3 (A. cowleana), 7.0 (B. prionotes) and 22.9 (C. calophylla) days. Based on independent validation of classifications of hyperspectral imaging data, we found that germination of Acacia and Corymbia seeds could be classified with over 85% accuracy, while it was about 80% for Banksia seeds. The selected spectral bands in each LDA-based classification were located near known pigment peaks involved in photosynthesis and/or near spectral bands used in published indices to predict chlorophyll or nitrogen content in leaves. The results suggested that seed germination may be successfully classified (predicted) based on reflectance in narrow spectral bands associated with the primary metabolism function and performance of plants.
Copyright © 2015 Elsevier B.V. All rights reserved.

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Year:  2015        PMID: 25752861     DOI: 10.1016/j.jphotobiol.2015.02.015

Source DB:  PubMed          Journal:  J Photochem Photobiol B        ISSN: 1011-1344            Impact factor:   6.252


  8 in total

1.  Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects.

Authors:  Christian Nansen; Mohammad S Imtiaz; Mohsen B Mesgaran; Hyoseok Lee
Journal:  Plant Methods       Date:  2022-06-03       Impact factor: 5.827

2.  Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale.

Authors:  Stefan Paulus; Anne-Katrin Mahlein
Journal:  Gigascience       Date:  2020-08-01       Impact factor: 6.524

3.  Detection of temporal changes in insect body reflectance in response to killing agents.

Authors:  Christian Nansen; Leandro Prado Ribeiro; Ian Dadour; John Dale Roberts
Journal:  PLoS One       Date:  2015-04-29       Impact factor: 3.240

4.  Using proximal remote sensing in non-invasive phenotyping of invertebrates.

Authors:  Xiaowei Li; Hongxing Xu; Ling Feng; Xiao Fu; Yalin Zhang; Christian Nansen
Journal:  PLoS One       Date:  2017-05-04       Impact factor: 3.240

5.  Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth).

Authors:  Maor Matzrafi; Ittai Herrmann; Christian Nansen; Tom Kliper; Yotam Zait; Timea Ignat; Dana Siso; Baruch Rubin; Arnon Karnieli; Hanan Eizenberg
Journal:  Front Plant Sci       Date:  2017-04-03       Impact factor: 5.753

6.  A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds.

Authors:  Tingting Zhang; Wensong Wei; Bin Zhao; Ranran Wang; Mingliu Li; Liming Yang; Jianhua Wang; Qun Sun
Journal:  Sensors (Basel)       Date:  2018-03-08       Impact factor: 3.576

Review 7.  Hyperspectral imaging for seed quality and safety inspection: a review.

Authors:  Lei Feng; Susu Zhu; Fei Liu; Yong He; Yidan Bao; Chu Zhang
Journal:  Plant Methods       Date:  2019-08-08       Impact factor: 4.993

8.  Penetration and scattering-Two optical phenomena to consider when applying proximal remote sensing technologies to object classifications.

Authors:  Christian Nansen
Journal:  PLoS One       Date:  2018-10-09       Impact factor: 3.240

  8 in total

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