Marine and Freshwater Plants: Challenges and Expectations

Maréchal, Éric

doi:10.3389/fpls.2019.01545

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…The Photosynthesis of marine plants is the energy source of all living activities in the ocean and provides a material basis for the composition and operation of marine ecological environment [30–33] . Wide variety of algae are the main components of marine plants, unique marine habitats cause differences in algae form and metabolism, making it an important source of marine natural products [34–37] .…”

Section: Insecticidal Natural Products Originated From Marine Plantsmentioning

confidence: 99%

Marine Natural Products: The Important Resource of Biological Insecticide

Song

Yang

Zhang

et al. 2021

Chemistry & Biodiversity

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Due to the unique environmental conditions and vast territory, marine habitat breeds more abundant biological resources than terrestrial environment. Massive marine biological species provide valuable resources for obtaining a large number of natural products with diverse structure and excellent activity. In recent years, new breakthroughs have been made in the application of marine natural products in drug development. In addition, the use of marine natural products to develop insecticides and other pesticide products has also been widely concerned. Targeting marine plants, animals, and microorganisms, we have collected information on marine natural products with insecticidal activity for nearly decade, including alkaloids, terpenes, flavonoids and phenols fatty acids, peptides, and proteins, et al. In addition, some active crude extracts are also included. This review describes the insecticidal activities of marine natural products and their broad applications for future research in agriculture and health.

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Section: Insecticidal Natural Products Originated From Marine Plantsmentioning

confidence: 99%

Marine Natural Products: The Important Resource of Biological Insecticide

Song

Yang

Zhang

et al. 2021

Chemistry & Biodiversity

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“…Comparatively, the marine cultured macroalgae have been less studied than terrestrial plants [37]. Until now, it has no such research been published on high-throughput phenotyping of cultivated macroalgae using UAV-based spectral imaging.…”

Section: Open Accessmentioning

confidence: 99%

Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging

Che

Wang³

et al. 2021

Plant Methods

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Background Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported. Results In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%. Conclusions This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

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“…Barnaby et al [ 38 ] evaluated rice grain quality using high-throughput HSI for GWAS and revealed plausible candidate genes. Comparatively, algae have been studied less in high-throughput phenotyping research than terrestrial plants [ 39 , 40 ]. Most studies quantify algal biochemical contents using non-imaging optical spectroscopy, which provides information on a limited surface area [ 41 – 44 ].…”

Section: Introductionmentioning

confidence: 99%

Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery

Che

Zhong

et al. 2023

Plant Phenomics

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Phycobilisomes and chlorophyll-a ( Chla ) play important roles in the photosynthetic physiology of red macroalgae and serve as the primary light-harvesting antennae and reaction center for photosystem II. Neopyropia is an economically important red macroalga widely cultivated in East Asian countries. The contents and ratios of 3 main phycobiliproteins and Chla are visible traits to evaluate its commercial quality. The traditional analytical methods used for measuring these components have several limitations. Therefore, a high-throughput, nondestructive, optical method based on hyperspectral imaging technology was developed for phenotyping the pigments phycoerythrin (PE), phycocyanin (PC), allophycocyanin (APC), and Chla in Neopyropia thalli in this study. The average spectra from the region of interest were collected at wavelengths ranging from 400 to 1000 nm using a hyperspectral camera. Following different preprocessing methods, 2 machine learning methods, partial least squares regression (PLSR) and support vector machine regression (SVR), were performed to establish the best prediction models for PE, PC, APC, and Chla contents. The prediction results showed that the PLSR model performed the best for PE ( R Test 2 = 0.96, MAPE = 8.31%, RPD = 5.21) and the SVR model performed the best for PC ( R Test 2 = 0.94, MAPE = 7.18%, RPD = 4.16) and APC ( R Test 2 = 0.84, MAPE = 18.25%, RPD = 2.53). Two models (PLSR and SVR) performed almost the same for Chla (PLSR: R Test 2 = 0.92, MAPE = 12.77%, RPD = 3.61; SVR: R Test 2 = 0.93, MAPE = 13.51%, RPD =3.60). Further validation of the optimal models was performed using field-collected samples, and the result demonstrated satisfactory robustness and accuracy. The distribution of PE, PC, APC, and Chla contents within a thallus was visualized according to the optimal prediction models. The results showed that hyperspectral imaging technology was effective for fast, accurate, and noninvasive phenotyping of the PE, PC, APC, and Chla contents of Neopyropia in situ. This could benefit the efficiency of macroalgae breeding, phenomics research, and other related applications.

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Marine and Freshwater Plants: Challenges and Expectations

Cited by 5 publications

References 36 publications

Marine Natural Products: The Important Resource of Biological Insecticide

Marine Natural Products: The Important Resource of Biological Insecticide

Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging

Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery

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