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Xu YL, Yan F, Li XS, Qu D, Zhao X. Influence and progress of tea pigment research: A comprehensive analysis of application of bibliometrics. Heliyon 2024; 10:e34940. [PMID: 39170582 PMCID: PMC11336268 DOI: 10.1016/j.heliyon.2024.e34940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 06/20/2024] [Accepted: 07/18/2024] [Indexed: 08/23/2024] Open
Abstract
Tea pigment, as a natural pigment component in tea, has attracted much attention because of its unique health benefits. In recent years, with the deepening of scientific research, the research on biological activity, extraction technology and application of tea pigment has made remarkable progress. Through systematic bibliometrics analysis, this paper comprehensively combs and evaluates the research status of tea pigment. The propose is to provide valuable reference for future research and application. In this paper, the chemical structure of tea pigment is firstly summarized, and then its diverse biological activities, such as antioxidant, anti-inflammatory and anti-tumor, are deeply discussed, especially its potential application in the treatment of cardiovascular diseases and diabetes. In addition, the application prospect of tea pigment in food coloring, textile dyeing and other industrial fields is also discussed in detail. Through the collection and arrangement of a large number of research literatures, this paper analyzes the development trend, research methods and main achievements of tea pigment research, and pays special attention to the dosage and effect of tea pigment in practical application. These analyses not only contribute to a more comprehensive understanding of the characteristics and functions of tea pigments, but also provide scientific basis for the further development and application of tea pigments.
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Affiliation(s)
- Yue-Ling Xu
- Shaanxi Four Subject One Union University Enterprise Cooperation Research Center for Tea Industry, Hanzhong, China
- College of Biological Science and Engineering, Shaanxi University of Technology, Hanzhong, China
| | - Fei Yan
- Shaanxi Four Subject One Union University Enterprise Cooperation Research Center for Tea Industry, Hanzhong, China
- Shaanxi Bio-Resources Key Laboratory, Hanzhong, China
- Coordination and Innovation Center for Comprehensive Development of Qinba Biological Resources, Hanzhong, China
- College of Biological Science and Engineering, Shaanxi University of Technology, Hanzhong, China
| | - Xin-Sheng Li
- Shaanxi Four Subject One Union University Enterprise Cooperation Research Center for Tea Industry, Hanzhong, China
- Shaanxi Bio-Resources Key Laboratory, Hanzhong, China
- Coordination and Innovation Center for Comprehensive Development of Qinba Biological Resources, Hanzhong, China
- College of Biological Science and Engineering, Shaanxi University of Technology, Hanzhong, China
| | - Dong Qu
- Shaanxi Bio-Resources Key Laboratory, Hanzhong, China
- Coordination and Innovation Center for Comprehensive Development of Qinba Biological Resources, Hanzhong, China
- College of Biological Science and Engineering, Shaanxi University of Technology, Hanzhong, China
| | - Xuan Zhao
- Shaanxi Four Subject One Union University Enterprise Cooperation Research Center for Tea Industry, Hanzhong, China
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Zhou J, Li F, Wang X, Yin H, Zhang W, Du J, Pu H. Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll. PLANTS (BASEL, SWITZERLAND) 2024; 13:1270. [PMID: 38732485 PMCID: PMC11085301 DOI: 10.3390/plants13091270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024]
Abstract
Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization-Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.
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Affiliation(s)
- Ju Zhou
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China; (J.Z.); (F.L.); (H.Y.); (W.Z.)
| | - Feiyi Li
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China; (J.Z.); (F.L.); (H.Y.); (W.Z.)
| | - Xinwu Wang
- College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625000, China;
| | - Heng Yin
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China; (J.Z.); (F.L.); (H.Y.); (W.Z.)
| | - Wenjing Zhang
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China; (J.Z.); (F.L.); (H.Y.); (W.Z.)
| | - Jiaoyang Du
- Forge Business School, Chongqing Yitong University, He’chuan 401520, China;
| | - Haibo Pu
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China; (J.Z.); (F.L.); (H.Y.); (W.Z.)
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3
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Tombuloglu G, Aldahnem A, Tombuloglu H, Slimani Y, Akhtar S, Hakeem KR, Almessiere MA, Baykal A, Ercan I, Manikandan A. Uptake and bioaccumulation of iron oxide nanoparticles (Fe 3O 4) in barley (Hordeum vulgare L.): effect of particle-size. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:22171-22186. [PMID: 38403831 DOI: 10.1007/s11356-024-32378-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 02/04/2024] [Indexed: 02/27/2024]
Abstract
Root-to-shoot translocation of nanoparticles (NPs) is a matter of interest due to their possible unprecedented effects on biota. Properties of NPs, such as structure, surface charge or coating, and size, determine their uptake by cells. This study investigates the size effect of iron oxide (Fe3O4) NPs on plant uptake, translocation, and physiology. For this purpose, Fe3O4 NPs having about 10 and 100 nm in average sizes (namely NP10 and NP100) were hydroponically subjected to barley (Hordeum vulgare L.) in different doses (50, 100, and 200 mg/L) at germination (5 days) and seedling (3 weeks) stages. Results revealed that particle size does not significantly influence the seedlings' growth but improves germination. The iron content in root and leaf tissues gradually increased with increasing NP10 and NP100 concentrations, revealing their root-to-shoot translocation. This result was confirmed by vibrating sample magnetometry analysis, where the magnetic signals increased with increasing NP doses. The translocation of NPs enhanced chlorophyll and carotenoid contents, suggesting their contribution to plant pigmentation. On the other hand, catalase activity and H2O2 production were higher in NP10-treated roots compared to NP100-treated ones. Besides, confocal microscopy revealed that NP10 leads to cell membrane damages. These findings showed that Fe3O4 NPs were efficiently taken up by the roots and transported to the leaves regardless of the size factor. However, small-sized Fe3O4 NPs may be more reactive due to their size properties and may cause cell stress and membrane damage. This study may help us better understand the size effect of NPs in nanoparticle-plant interaction.
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Affiliation(s)
- Guzin Tombuloglu
- Department of Biophysics, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
| | - Anwar Aldahnem
- Department of Genetics Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
| | - Huseyin Tombuloglu
- Department of Genetics Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia.
| | - Yassine Slimani
- Department of Biophysics, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
| | - Sultan Akhtar
- Department of Biophysics, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
| | - Khalid Rehman Hakeem
- Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
- Princess Dr. Najla Bint Saud Al-Saud Center for Excellence Research in Biotechnology, King Abdulaziz University, P.O. Box 80200, Jeddah, 21589, Saudi Arabia
| | - Munirah A Almessiere
- Department of Biophysics, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
- Department of Physics, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
| | - Abdulhadi Baykal
- Food Engineering Department, Faculty of Engineering, Istanbul Aydin University, Istanbul, 34295, Türkiye
| | - Ismail Ercan
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Duzce University, 81010, Duzce, Türkiye
| | - Ayyar Manikandan
- Department of Chemistry, Bharath Institute of Higher Education and Research (BIHER), Bharath University, Chennai, Tamil Nadu, 600073, India
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Mahato T, Parida BR, Bar S. Assessing tea plantations biophysical and biochemical characteristics in Northeast India using satellite data. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:327. [PMID: 38421498 DOI: 10.1007/s10661-024-12502-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 02/24/2024] [Indexed: 03/02/2024]
Abstract
Despite advancements in using multi-temporal satellite data to assess long-term changes in Northeast India's tea plantations, a research gap exists in understanding the intricate interplay between biophysical and biochemical characteristics. Further exploration is crucial for precise, sustainable monitoring and management. In this study, satellite-derived vegetation indices and near-proximal sensor data were deployed to deduce various physico-chemical characteristics and to evaluate the health conditions of tea plantations in northeast India. The districts, such as Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia in Assam were selected, which are the major contributors to the tea industry in India. The Sentinel-2A (2022) data was processed in the Google Earth Engine (GEE) cloud platform and utilized for analyzing tea plantations biochemical and biophysical properties. Leaf chlorophyll (Cab) and nitrogen contents are determined using the Normalized Area Over Reflectance Curve (NAOC) index and flavanol contents, respectively. Biophysical and biochemical parameters of the tea assessed during the spring season (March-April) 2022 revealed that tea plantations located in Tinsukia and Dibrugarh were much healthier than the other districts in Assam which are evident from satellite-derived Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (fPAR), including the Cab and nitrogen contents. The Cab of healthy tea plants varied from 25 to 35 µg/cm2. Pearson correlation among satellite-derived Cab and nitrogen with field measurements showed R2 of 0.61-0.62 (p-value < 0.001). This study offered vital information about land alternations and tea health conditions, which can be crucial for conservation, monitoring, and management practices.
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Affiliation(s)
- Trinath Mahato
- Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi, 835222, India
- Centre for Environment and Energy Development, Ranchi, 834001, India
| | - Bikash Ranjan Parida
- Department of Geoinformatics, School of Natural Resource Management, Central University of Jharkhand, Ranchi, 835222, India.
| | - Somnath Bar
- Department of Geography and Environmental Science, University of Southampton, Highfield, Southampton, SO17 1BJ, UK
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Zahir SADM, Jamlos MF, Omar AF, Jamlos MA, Mamat R, Muncan J, Tsenkova R. Review - Plant nutritional status analysis employing the visible and near-infrared spectroscopy spectral sensor. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123273. [PMID: 37666099 DOI: 10.1016/j.saa.2023.123273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/12/2023] [Accepted: 08/16/2023] [Indexed: 09/06/2023]
Abstract
Experiments demonstrated that visible and near-infrared (Vis-NIR) spectroscopy is a highly reliable tool for determining the nutritional status of plants. Although numerous studies on various kinds of plants have been conducted, there are only a few summaries of the research findings regarding the absorbance bands in the visible and near-infrared region and how they relate to the nutritional status of plants. This article will discuss the application of Vis-NIR spectroscopy for monitoring the nutrient conditions of plants, with a particular emphasis on three major components required by plants, namely nitrogen (N), phosphorus (P), and potassium (K), or NPK. Each section discussed different topics, for instance, the essential nutrients needed by plants, the application of Vis-NIR spectroscopy in nutrient status analysis, chemometrics tools, and absorbance bands related to the nutrient status, respectively. Deduction made concluded that factors affecting the plant's structure are contributed by several circumstances like the age of leaves, concentration of pigments, and water content. These factors are intertwined, strongly correlated, and can be observed in the visible and near-infrared regions. While the visible region is commonly utilised for nutritional analysis in plants, the literature review performed in this paper shows that the near-infrared region as well contains valuable information about the plant's nutritional status. A few wavelengths related to the direct estimation of nutrients in this review explained that information on nutrients can be linked with chlorophyll and water absorption bands such that N and P are the components of chlorophyll and protein; on the other hand, K exists in the form of cationic carbohydrates which are sensitive to water region.
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Affiliation(s)
- Siti Anis Dalila Muhammad Zahir
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Malaysia
| | - Mohd Faizal Jamlos
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Malaysia; Centre of Excellence for Artificial Intelligence & Data Science, Universiti Malaysia Pahang Al-Sultan Abdullah, 26300 Gambang, Malaysia.
| | - Ahmad Fairuz Omar
- School of Physics, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia.
| | - Mohd Aminudin Jamlos
- Faculty of Electronics Engineering Technology, Universiti Malaysia Perlis, 26600 Arau, Malaysia
| | - Rizalman Mamat
- Centre for Automotive Engineering Centre, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan 26600, Malaysia
| | - Jelena Muncan
- Aquaphotomics Research Department, Faculty of Agriculture, Kobe University, Kobe, Japan
| | - Roumiana Tsenkova
- Aquaphotomics Research Department, Faculty of Agriculture, Kobe University, Kobe, Japan
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Tang T, Luo Q, Yang L, Gao C, Ling C, Wu W. Research Review on Quality Detection of Fresh Tea Leaves Based on Spectral Technology. Foods 2023; 13:25. [PMID: 38201054 PMCID: PMC10778318 DOI: 10.3390/foods13010025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/13/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024] Open
Abstract
As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.
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Affiliation(s)
- Ting Tang
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Qing Luo
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Liu Yang
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Changlun Gao
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
| | - Caijin Ling
- Tea Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
| | - Weibin Wu
- College of Engineering, South China Agricultural University, Guangzhou 510642, China; (T.T.); (Q.L.); (L.Y.); (C.G.)
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Varghese R, Cherukuri AK, Doddrell NH, Doss CGP, Simkin AJ, Ramamoorthy S. Machine learning in photosynthesis: Prospects on sustainable crop development. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 335:111795. [PMID: 37473784 DOI: 10.1016/j.plantsci.2023.111795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/10/2023] [Accepted: 07/13/2023] [Indexed: 07/22/2023]
Abstract
Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.
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Affiliation(s)
- Ressin Varghese
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India
| | - Aswani Kumar Cherukuri
- School of Information Technology and Engineering, VIT University, Vellore 632014, Tamil Nadu, India
| | | | - C George Priya Doss
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India
| | - Andrew J Simkin
- School of Biosciences, University of Kent, Canterbury CT2 7NJ, UK; School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
| | - Siva Ramamoorthy
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India.
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Kandpal KC, Kumar A. Migrating from Invasive to Noninvasive Techniques for Enhanced Leaf Chlorophyll Content Estimations Efficiency. Crit Rev Anal Chem 2023; 54:2583-2598. [PMID: 36995248 DOI: 10.1080/10408347.2023.2188425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Leaf chlorophyll is vital for plants because it helps them get energy through the process of photosynthesis. The present review thus examines various leaf chlorophyll content estimation techniques in laboratories and outdoor field conditions. The review consists of two sections: (1) destructive and (2) nondestructive methods for chlorophyll estimation. Through this review, we could find that Arnon's spectrophotometry method is the most popular and simplest method for the estimation of leaf chlorophyll under laboratory conditions. While android-based applications and portable equipment for the quantification of chlorophyll content are useful for onsite utilities. The algorithm used in these applications and equipment is trained for specific plants rather than being generalized across all plants. In the case of hyperspectral remote sensing, more than 42 hyperspectral indices were observed for chlorophyll estimations, and among these red-edge-based indices were found to be more appropriate. This review recommends that hyperspectral indices such as the three-band hyperspectral vegetation index, Chlgreen, Triangular Greenness Index, Wavelength Difference Index, and Normalized Difference Chlorophyll are generic and can be used for chlorophyll estimations of various plants. It was also observed that Artificial Intelligence (AI) and Machine Learning (ML)-based algorithms such as Random Forest, Support Vector Machine, and Artificial Neural Network regressions are the most suited and widely applied algorithms for chlorophyll estimation using the above hyperspectral data. It was also concluded that comparative studies are required in order to understand the advantages and disadvantages of reflectance-based vegetation indices and chlorophyll fluorescence imaging methods for chlorophyll estimations to comprehend their efficiency.
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Affiliation(s)
- Kishor Chandra Kandpal
- CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India
| | - Amit Kumar
- CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
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Yang Y, Nan R, Mi T, Song Y, Shi F, Liu X, Wang Y, Sun F, Xi Y, Zhang C. Rapid and Nondestructive Evaluation of Wheat Chlorophyll under Drought Stress Using Hyperspectral Imaging. Int J Mol Sci 2023; 24:ijms24065825. [PMID: 36982900 PMCID: PMC10056805 DOI: 10.3390/ijms24065825] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/11/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
Chlorophyll drives plant photosynthesis. Under stress conditions, leaf chlorophyll content changes dramatically, which could provide insight into plant photosynthesis and drought resistance. Compared to traditional methods of evaluating chlorophyll content, hyperspectral imaging is more efficient and accurate and benefits from being a nondestructive technique. However, the relationships between chlorophyll content and hyperspectral characteristics of wheat leaves with wide genetic diversity and different treatments have rarely been reported. In this study, using 335 wheat varieties, we analyzed the hyperspectral characteristics of flag leaves and the relationships thereof with SPAD values at the grain-filling stage under control and drought stress. The hyperspectral information of wheat flag leaves significantly differed between control and drought stress conditions in the 550-700 nm region. Hyperspectral reflectance at 549 nm (r = -0.64) and the first derivative at 735 nm (r = 0.68) exhibited the strongest correlations with SPAD values. Hyperspectral reflectance at 536, 596, and 674 nm, and the first derivatives bands at 756 and 778 nm, were useful for estimating SPAD values. The combination of spectrum and image characteristics (L*, a*, and b*) can improve the estimation accuracy of SPAD values (optimal performance of RFR, relative error, 7.35%; root mean square error, 4.439; R2, 0.61). The models established in this study are efficient for evaluating chlorophyll content and provide insight into photosynthesis and drought resistance. This study can provide a reference for high-throughput phenotypic analysis and genetic breeding of wheat and other crops.
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Affiliation(s)
- Yucun Yang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Rui Nan
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Tongxi Mi
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Yingxin Song
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Fanghui Shi
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Xinran Liu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Yunqi Wang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
| | - Fengli Sun
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
- Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture, Xianyang 712100, China
| | - Yajun Xi
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
- Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture, Xianyang 712100, China
| | - Chao Zhang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China
- Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture, Xianyang 712100, China
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10
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Han P, Zhai Y, Liu W, Lin H, An Q, Zhang Q, Ding S, Zhang D, Pan Z, Nie X. Dissection of Hyperspectral Reflectance to Estimate Photosynthetic Characteristics in Upland Cotton ( Gossypium hirsutum L.) under Different Nitrogen Fertilizer Application Based on Machine Learning Algorithms. PLANTS (BASEL, SWITZERLAND) 2023; 12:455. [PMID: 36771540 PMCID: PMC9919998 DOI: 10.3390/plants12030455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 12/16/2022] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
Hyperspectral technology has enabled rapid and efficient nitrogen monitoring in crops. However, most approaches involve direct monitoring of nitrogen content or physiological and biochemical indicators directly related to nitrogen, which cannot reflect the overall plant nutritional status. Two important photosynthetic traits, the fraction of absorbed photosynthetically active radiation (FAPAR) and the net photosynthetic rate (Pn), were previously shown to respond positively to nitrogen changes. Here, Pn and FAPAR were used for correlation analysis with hyperspectral data to establish a relationship between nitrogen status and hyperspectral characteristics through photosynthetic traits. Using principal component and band autocorrelation analyses of the original spectral reflectance, two band positions (350-450 and 600-750 nm) sensitive to nitrogen changes were obtained. The performances of four machine learning algorithm models based on six forms of hyperspectral transformations showed that the light gradient boosting machine (LightGBM) model based on the hyperspectral first derivative could better invert the Pn of function-leaves in cotton, and the random forest (RF) model based on hyperspectral first derivative could better invert the FAPAR of the cotton canopy. These results provide advanced metrics for non-destructive tracking of cotton nitrogen status, which can be used to diagnose nitrogen nutrition and cotton growth status in large farms.
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Affiliation(s)
- Peng Han
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Yaping Zhai
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Wenhong Liu
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Hairong Lin
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Qiushuang An
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Qi Zhang
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Shugen Ding
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Dawei Zhang
- Research Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
| | - Zhenyuan Pan
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
| | - Xinhui Nie
- Key Laboratory of Oasis Ecology Agricultural of Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
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11
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Nazri NAA, Azeman NH, Bakar MHA, Mobarak NN, Aziz THTA, Zain ARM, Arsad N, Luo Y, Bakar AAA. Chlorophyll Detection by Localized Surface Plasmon Resonance Using Functionalized Carbon Quantum Dots Triangle Ag Nanoparticles. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:2999. [PMID: 36080034 PMCID: PMC9457568 DOI: 10.3390/nano12172999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/18/2022] [Accepted: 08/24/2022] [Indexed: 06/15/2023]
Abstract
An optical sensor-based localized surface plasmon resonance (LSPR) sensor was demonstrated for sensitive and selective chlorophyll detection through the integration of amino-functionalized carbon quantum dots (NCQD) and triangle silver nanoparticles (AgNPs). The additions of amino groups to the CQD enhance the detection of chlorophyll through electrostatic interactions. AgNPs-NCQD composite was fabricated on the surface of the silanized glass slide using the self-assembly technique. The experimental results showed that the AgNPs-NCQD film-based LSPR sensor detects better than AgNPs and AgNPs-CQD films with a good correlation coefficient (R2 = 0.9835). AgNPs-NCQD showed a high sensitivity response of 2.23 nm ppm-1. The detection and quantification limits of AgNPs-NCQD are 1.03 ppm and 3.40 ppm, respectively, in the range of 0.05 to 6 ppm. Throughout this study, no significant interference was observed among the other ionic species (NO2-, PO4-, NH4+, and Fe3+). This study demonstrates the applicability of the proposed sensor (AgNPs-NCQD) as a sensing material for chlorophyll detection in oceans.
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Affiliation(s)
- Nur Afifah Ahmad Nazri
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Nur Hidayah Azeman
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Mohd Hafiz Abu Bakar
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Nadhratun Naiim Mobarak
- Department of Chemical Sciences, Faculty of Sciences and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Tg Hasnan Tg Abd Aziz
- Institute of Microengineering and Nanoelectronics, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Ahmad Rifqi Md Zain
- Institute of Microengineering and Nanoelectronics, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Norhana Arsad
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Yunhan Luo
- Guangdong Provincial Key Laboratory of Optical Fibre Sensing and Communications, College of Science and Engineering, Jinan University, Guangzhou 510632, China
| | - Ahmad Ashrif A. Bakar
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
- Institut Islam Hadhari, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
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12
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Xiao Q, Tang W, Zhang C, Zhou L, Feng L, Shen J, Yan T, Gao P, He Y, Wu N. Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves. PLANT PHENOMICS 2022; 2022:9813841. [PMID: 36158530 PMCID: PMC9489230 DOI: 10.34133/2022/9813841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/27/2022] [Indexed: 11/16/2022]
Abstract
Rapid determination of chlorophyll content is significant for evaluating cotton's nutritional and physiological status. Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection. However, the model developed on one batch or variety cannot produce the same effect for another due to variations, such as samples and measurement conditions. Considering that it is costly to establish models for each batch or variety, the feasibility of using spectral preprocessing combined with deep transfer learning for model transfer was explored. Seven different spectral preprocessing methods were discussed, and a self-designed convolutional neural network (CNN) was developed to build models and conduct transfer tasks by fine-tuning. The approach combined first-derivative (FD) and standard normal variate transformation (SNV) was chosen as the best pretreatment. For the dataset of the target domain, fine-tuned CNN based on spectra processed by FD + SNV outperformed conventional partial least squares (PLS) and squares-support vector machine regression (SVR). Although the performance of fine-tuned CNN with a smaller dataset was slightly lower, it was still better than conventional models and achieved satisfactory results. Ensemble preprocessing combined with deep transfer learning could be an effective approach to estimate the chlorophyll content between different cotton varieties, offering a new possibility for evaluating the nutritional status of cotton in the field.
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Affiliation(s)
- Qinlin Xiao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Wentan Tang
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou 313000, China
| | - Lei Zhou
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
| | - Lei Feng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Jianxun Shen
- Hangzhou Raw Seed Growing Farm, Hangzhou 311115, China
| | - Tianying Yan
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Pan Gao
- College of Information Science and Technology, Shihezi University, Shihezi 832000, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
| | - Na Wu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
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13
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Evaluation of a One-Dimensional Convolution Neural Network for Chlorophyll Content Estimation Using a Compact Spectrometer. REMOTE SENSING 2022. [DOI: 10.3390/rs14091997] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Leaf chlorophyll content is used as a major indicator of plant stress and growth, and hyperspectral remote sensing is frequently used to monitor the chlorophyll content. Hyperspectral reflectance has been used to evaluate vegetation properties such as pigment content, plant structure and physiological features using portable spectroradiometers. However, the prices of these devices have not yet decreased to consumer-affordable levels, which prevents widespread use. In this study, a system based on a cost-effective fingertip-sized spectrometer (Colorcompass-LF, a total price for the proposed solution was approximately 1600 USD) was evaluated for its ability to estimate the chlorophyll contents of radish and wasabi leaves and was compared with the Analytical Spectral Devices FieldSpec4. The chlorophyll contents per leaf area (cm2) of radish were generally higher than those of wasabi and ranged from 42.20 to 94.39 μg/cm2 and 11.39 to 40.40 μg/cm2 for radish and wasabi, respectively. The chlorophyll content was estimated using regression models based on a one-dimensional convolutional neural network (1D-CNN) that was generated after the original reflectance from the spectrometer measurements was de-noised. The results from an independent validation dataset confirmed the good performance of the Colorcompass-LF after spectral correction using a second-degree polynomial, and very similar estimation accuracies were obtained for the measurements from the FieldSpec4. The coefficients of determination of the regression models based on 1D-CNN were almost same (with R2 = 0.94) and the ratios of performance to deviation based on reflectance after spectral correction using a second-degree polynomial for the Colorcompass-LF and the FieldSpec4 were 4.31 and 4.33, respectively.
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14
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Sterling A, Di Rienzo JA. Prediction of South American Leaf Blight and Disease-Induced Photosynthetic Changes in Rubber Tree, Using Machine Learning Techniques on Leaf Hyperspectral Reflectance. PLANTS (BASEL, SWITZERLAND) 2022; 11:329. [PMID: 35161310 PMCID: PMC8840432 DOI: 10.3390/plants11030329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 01/06/2022] [Accepted: 01/06/2022] [Indexed: 06/14/2023]
Abstract
The efficiency of visible and near-infrared (VIS/NIR) sensors and predictive modeling for detecting and classifying South American Leaf Blight (SALB) (Pseudocercospora ulei) in rubber trees (Hevea brasiliensis) has been poorly explored. Furthermore, the performance of VIS/NIR analysis combined with machine learning (ML) algorithms for predicting photosynthetic alterations caused by SALB is unknown. Therefore, this study aimed to detect and classify the SALB levels, as well as to predict, for the first time, disease-induced photosynthetic changes in rubber trees. Leaf hyperspectral reflectance combined with five ML techniques (random forest (RF), boosted regression tree (BRT), bagged classification and regression trees (BCART), artificial neural network (ANN), and support vector machine (SVM)) were used. The RF, ANN, and BCART models achieved the best performance for classifying the SALB levels on the training dataset (accuracies of 98.0 to 99.8%), with 10-fold cross-validation repeated five times, and test dataset (accuracies of 97.1 to 100%). The ANN and RF models were better at predicting leaf gas exchange-related traits such as net CO2 assimilation rate (A) and extrinsic water use efficiency (WUEe) in the training (R2 ranged from 0.97 to 0.99) and testing (R2 ranged from 0.96 to 0.99) phases. In comparison, lower performances (R2 ranged from 0.24 to 0.52) were evidenced for the photochemical traits. This research provides a basis for future designs of a remote monitoring system based on early detection and accurate diagnosis of biotic stress caused by SALB, which is fundamental for more effective rubber crop protection.
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Affiliation(s)
- Armando Sterling
- Phytopathology Laboratory, Instituto Amazónico de Investigaciones Científicas SINCHI-Facultad de Ciencias Básicas, Universidad de la Amazonía, Florencia 180001, Colombia
- InfoStat Transfer Center, Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, Córdoba 5016, Argentina;
| | - Julio A. Di Rienzo
- InfoStat Transfer Center, Facultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, Córdoba 5016, Argentina;
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15
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A novel non-destructive detection of deteriorative dried longan fruits using machine learning algorithms based on low field nuclear magnetic resonance. JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021. [DOI: 10.1007/s11694-021-01190-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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16
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Jung JG, Song KE, Hong SH, Shim SI. Hyperspectral Characteristics of an Individual Leaf of Wheat Grown under Nitrogen Gradient. PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10112291. [PMID: 34834653 PMCID: PMC8626060 DOI: 10.3390/plants10112291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 10/19/2021] [Accepted: 10/23/2021] [Indexed: 06/13/2023]
Abstract
Since the application of hyperspectral technology to agriculture, many scientists have been conducting studies to apply the technology in crop diagnosis. However, due to the properties of optical devices, the reflectances obtained according to the image acquisition conditions are different. Nevertheless, there is no optimized method for minimizing such technical errors in applying hyperspectral imaging. Therefore, this study was conducted to find the appropriate image acquisition conditions that reflect the growth status of wheat grown under different nitrogen fertilization regimes. The experiment plots were comprised of six plots with various N application levels of 145.6 kg N ha-1 (N1), 109.2 kg N ha-1 (N2), 91.0 kg N ha-1 (N3), 72.8 kg N ha-1 (N4), 54.6 kg N ha-1 (N5), and 36.4 kg N ha-1 (N6). Hyperspectral image acquisitions were performed at different shooting angles of 105° and 125° from the surface, and spike, flag leaf, and the second uppermost leaf were divided into five parts from apex to base when analyzing the images. The growth analysis conducted at heading showed that the N6 was 85.6% in the plant height, 44.1% in LAI, and 64.9% in SPAD as compared to N1. The nitrogen content in the leaf decreased by 55.2% compared to N1 and the quantity was 44.9% in N6 compared to N1. Based on the vegetation indices obtained from hyperspectral reflectances at the heading stage, the spike was not suitable for analysis. In the case of the flag leaf and the 2nd uppermost leaf, the vegetation indices from spectral data taken at 105 degrees were more appropriate for acquiring imaging data by clearly dividing the effects of fertilization level. The results of the regional variation in a leaf showed that the region of interest (ROI), which is close to the apex of the flag leaf and the base of the second uppermost leaf, has a high coefficient of determination between the fertilization levels and the vegetation indices, which effectively reflected the status of wheat.
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Affiliation(s)
- Jae Gyeong Jung
- Department of Agronomy, Gyeongsang National University, Jinju 52828, Korea; (J.G.J.); (K.E.S.)
| | - Ki Eun Song
- Department of Agronomy, Gyeongsang National University, Jinju 52828, Korea; (J.G.J.); (K.E.S.)
| | - Sun Hee Hong
- Department of Plant Life Science, Hankyong National University, Anseong 17579, Korea;
| | - Sang In Shim
- Department of Agronomy, Gyeongsang National University, Jinju 52828, Korea; (J.G.J.); (K.E.S.)
- Institute of Life Sciences, Gyeongsang National University, Jinju 52828, Korea
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17
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Yamashita H, Sonobe R, Hirono Y, Morita A, Ikka T. Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms. Sci Rep 2020; 10:17360. [PMID: 33060629 PMCID: PMC7566634 DOI: 10.1038/s41598-020-73745-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 09/21/2020] [Indexed: 11/09/2022] Open
Abstract
Nondestructive techniques for estimating nitrogen (N) status are essential tools for optimizing N fertilization input and reducing the environmental impact of agricultural N management, especially in green tea cultivation, which is notably problematic. Previously, hyperspectral indices for chlorophyll (Chl) estimation, namely a green peak and red edge in the visible region, have been identified and used for N estimation because leaf N content closely related to Chl content in green leaves. Herein, datasets of N and Chl contents, and visible and near-infrared hyperspectral reflectance, derived from green leaves under various N nutrient conditions and albino yellow leaves were obtained. A regression model was then constructed using several machine learning algorithms and preprocessing techniques. Machine learning algorithms achieved high-performance models for N and Chl content, ensuring an accuracy threshold of 1.4 or 2.0 based on the ratio of performance to deviation values. Data-based sensitivity analysis through integration of the green and yellow leaves datasets identified clear differences in reflectance to estimate N and Chl contents, especially at 1325-1575 nm, suggesting an N content-specific region. These findings will enable the nondestructive estimation of leaf N content in tea plants and contribute advanced indices for nondestructive tracking of N status in crops.
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Affiliation(s)
- Hiroto Yamashita
- Faculty of Agriculture, Shizuoka University, Shizuoka, Japan
- United Graduate School of Agricultural Science, Gifu University, Gifu, Japan
| | - Rei Sonobe
- Faculty of Agriculture, Shizuoka University, Shizuoka, Japan.
| | - Yuhei Hirono
- Division of Tea Research, Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization (NARO), Shimada, Japan
| | - Akio Morita
- Faculty of Agriculture, Shizuoka University, Shizuoka, Japan
| | - Takashi Ikka
- Faculty of Agriculture, Shizuoka University, Shizuoka, Japan.
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18
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Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance. REMOTE SENSING 2020. [DOI: 10.3390/rs12193265] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Japanese horseradish (wasabi) grows in very specific conditions, and recent environmental climate changes have damaged wasabi production. In addition, the optimal culture methods are not well known, and it is becoming increasingly difficult for incipient farmers to cultivate it. Chlorophyll a, b and carotenoid contents, as well as their allocation, could be an adequate indicator in evaluating its production and environmental stress; thus, developing an in situ method to monitor photosynthetic pigments based on reflectance could be useful for agricultural management. Besides original reflectance (OR), five pre-processing techniques, namely, first derivative reflectance (FDR), continuum-removed (CR), de-trending (DT), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV), were compared to assess the accuracy of the estimation. Furthermore, five machine learning algorithms—random forest (RF), support vector machine (SVM), kernel-based extreme learning machine (KELM), Cubist, and Stochastic Gradient Boosting (SGB)—were considered. To classify the samples under different pH or sulphur ion concentration conditions, the end of the red edge bands was effective for OR, FDR, DT, MSC, and SNV, while a green-peak band was effective for CR. Overall, KELM and Cubist showed high performance and incorporating pre-processing techniques was effective for obtaining estimated values with high accuracy. The best combinations were found to be DT–KELM for chl a (RPD = 1.511–5.17, RMSE = 1.23–3.62 μg cm−2) and chl a:b (RPD = 0.73–3.17, RMSE = 0.13–0.60); CR–KELM for chl b (RPD = 1.92–5.06, RMSE = 0.41–1.03 μg cm−2) and chl a:car (RPD = 1.31–3.23, RMSE = 0.26–0.50); SNV–Cubist for car (RPD = 1.63–3.32, RMSE = 0.31–1.89 μg cm−2); and DT–Cubist for chl:car (RPD = 1.53–3.96, RMSE = 0.27–0.74).
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19
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Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization. REMOTE SENSING 2020. [DOI: 10.3390/rs12172826] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The analysis of chlorophyll concentration based on spectroscopy has great importance for monitoring the growth state and guiding the precision nitrogen management of potato crops in the field. A suitable data processing and modeling method could improve the stability and accuracy of chlorophyll analysis. To develop such a method, we collected the modelling data by conducting field experiments at the tillering, tuber-formation, tuber-bulking, and tuber-maturity stages in 2018. A chlorophyll analysis model was established using the partial least-square (PLS) algorithm based on original reflectance, standard normal variate reflectance, and wavelet features (WFs) under different decomposition scales (21–210, Scales 1–10), which were optimized by the competitive adaptive reweighted sampling (CARS) algorithm. The performances of various models were compared. The WFs under Scale 3 had the strongest correlation with chlorophyll concentration with a correlation coefficient of −0.82. In the model calibration process, the optimal model was the Scale3-CARS-PLS, which was established based on the sensitive WFs under Scale 3 selected by CARS, with the largest coefficient of determination of calibration set (Rc2) of 0.93 and the smallest Rc2−Rcv2 value of 0.14. In the model validation process, the Scale3-CARS-PLS model had the largest coefficient of determination of validation set (Rv2) of 0.85 and the smallest root–mean–square error of cross-validation (RMSEV) value of 2.77 mg/L, demonstrating good prediction capability of chlorophyll concentration. Finally, the analysis performance of the Scale3-CARS-PLS model was measured using the testing data collected in 2020; the R2 and RMSE values were 0.69 and 3.36 mg/L, showing excellent applicability. Therefore, the Scale3-CARS-PLS model could be used to analyze chlorophyll concentration. This study indicated the best decomposition scale of continuous wavelet transform and provided an important support method for chlorophyll analysis in the potato crops.
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