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Che S, Du G, Zhong X, Mo Z, Wang Z, Mao Y. Quantification of Photosynthetic Pigments in Neopyropia yezoensis Using Hyperspectral Imagery. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0012. [PMID: 37040513 PMCID: PMC10076050 DOI: 10.34133/plantphenomics.0012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 11/17/2022] [Indexed: 06/19/2023]
Abstract
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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Affiliation(s)
- Shuai Che
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Guoying Du
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Xuefeng Zhong
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Zhaolan Mo
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Zhendong Wang
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, China
| | - Yunxiang Mao
- Key Laboratory of Utilization and Conservation of Tropical Marine Bioresource (Ministry of Education), College of Fisheries and Life Science, Hainan Tropical Ocean University, Sanya, 572002, China
- Yazhou Bay Innovation Institute, Hainan Tropical Ocean University, Sanya, 572025, China
- Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266073, China
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Song C, Yang J, Zhang M, Ding G, Jia C, Qin J, Guo L. Marine Natural Products: The Important Resource of Biological Insecticide. Chem Biodivers 2021; 18:e2001020. [PMID: 33855815 DOI: 10.1002/cbdv.202001020] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 03/23/2021] [Indexed: 12/21/2022]
Abstract
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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Affiliation(s)
- Chenggang Song
- College of Plant Science, Jilin University, Changchun, 130062, P. R. China
| | - Jian Yang
- National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100193, P. R. China
| | - Mingzhe Zhang
- College of Plant Science, Jilin University, Changchun, 130062, P. R. China
| | - Gang Ding
- Institute of Medicinal Plant Department, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100193, P. R. China
| | - Chengguo Jia
- College of Plant Science, Jilin University, Changchun, 130062, P. R. China
| | - Jianchun Qin
- College of Plant Science, Jilin University, Changchun, 130062, P. R. China
| | - Lanping Guo
- National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, 100193, P. R. China
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Che S, Du G, Wang N, He K, Mo Z, Sun B, Chen Y, Cao Y, Wang J, Mao Y. Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging. PLANT METHODS 2021; 17:12. [PMID: 33541365 PMCID: PMC7863433 DOI: 10.1186/s13007-021-00711-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 01/18/2021] [Indexed: 06/12/2023]
Abstract
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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Affiliation(s)
- Shuai Che
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Guoying Du
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Ning Wang
- Xi’ an Ecotech Spectral Imaging and Eco-drone Remote Sensing Research Center Co., Ltd., Xi’ an, 710000 People’s Republic of China
| | - Kun He
- Xi’ an Ecotech Spectral Imaging and Eco-drone Remote Sensing Research Center Co., Ltd., Xi’ an, 710000 People’s Republic of China
| | - Zhaolan Mo
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Bin Sun
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Yu Chen
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Yifei Cao
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Junhao Wang
- Key Laboratory of Marine Genetics and Breeding (Ministry of Education), College of Marine Life Sciences, Ocean University of China, Qingdao, 266003 People’s Republic of China
| | - Yunxiang Mao
- Key Laboratory of Utilization and Conservation of Tropical Marine Bioresource (Ministry of Education), College of Fisheries and Life Science, Hainan Tropical Ocean University, Sanya, 572022 People’s Republic of China
- Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, 266000 People’s Republic of China
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