Academic Journal
Terahertz spectral imaging based quantitative determination of spatial distribution of plant leaf constituents
| Title: | Terahertz spectral imaging based quantitative determination of spatial distribution of plant leaf constituents |
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| Authors: | Ziyi Zang, Jie Wang, Hong-Liang Cui, Shihan Yan |
| Source: | Plant Methods, Vol 15, Iss 1, Pp 1-11 (2019) |
| Publisher Information: | BMC, 2019. |
| Publication Year: | 2019 |
| Collection: | LCC:Plant culture LCC:Biology (General) |
| Subject Terms: | Terahertz imaging, Quantitative analysis, Plant leaf, Water content, Solid matter content, Gas content, Plant culture, SB1-1110, Biology (General), QH301-705.5 |
| More Details: | Abstract Background Plant leaves have heterogeneous structures composed of spatially variable distribution of liquid, solid, and gaseous matter. Such contents and distribution characteristics correlate with the leaf vigor and phylogenic traits. Recently, terahertz (THz) techniques have been proved to access leaf water content and spatial heterogeneity distribution information, but the solid matter content and gas network information were usually ignored, even though they also affect the THz dielectric function of the leaf. Results A particle swarm optimization algorithm is employed for a one-off quantitative assay of spatial variability distribution of the leaf compositions from THz data, based on an extended Landau–Lifshitz–Looyenga model, and experimentally verified using Bougainvillea spectabilis leaves. A good agreement is demonstrated for water and solid matter contents between the THz-based method and the gravimetric analysis. In particular, the THz-based method shows good sensitivity to fine-grained differences of leaf growth and development stages. Furthermore, such subtle features as damages and wounds in leaf could be discovered through THz detection and comparison regarding spatial heterogeneity of component contents. Conclusions This THz imaging method provides quantitative assay of the leaf constituent contents with the spatial distribution feature, which has the potential for applications in crop disease diagnosis and farmland cultivation management. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1746-4811 |
| Relation: | http://link.springer.com/article/10.1186/s13007-019-0492-y; https://doaj.org/toc/1746-4811 |
| DOI: | 10.1186/s13007-019-0492-y |
| Access URL: | https://doaj.org/article/050fce13097d46999a5af9ea40ec6116 |
| Accession Number: | edsdoj.050fce13097d46999a5af9ea40ec6116 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 17464811 |
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| DOI: | 10.1186/s13007-019-0492-y |
| Published in: | Plant Methods |
| Language: | English |