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Nguyen NN, Nguyen NT, Nguyen PT, Phan QN, Le TL, Do HDK. Current and emerging nanotechnology for sustainable development of agriculture: Implementation design strategy and application. Heliyon 2024; 10:e31503. [PMID: 38818209 PMCID: PMC11137568 DOI: 10.1016/j.heliyon.2024.e31503] [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: 11/30/2023] [Revised: 05/08/2024] [Accepted: 05/16/2024] [Indexed: 06/01/2024] Open
Abstract
Recently, agriculture systems have faced numerous challenges involving sustainable nutrient use efficiency and feeding, environmental pollution especially heavy metals (HMs), infection of harmful microorganisms, and maintenance of crop production quality during postharvesting and packaging. Nanotechnology and nanomaterials have emerged as powerful tools in agriculture applications that provide alternatives or support traditional methods. This review aims to address and highlight the current overarching issue and various implementation strategies of nanotechnology for sustainable agriculture development. In particular, the current progress of different nano-fertilizers (NFs) systems was analyzed to show their advances in enhancing the uptake and translocations in plants and improving nutrient bioavailability in soil. Also, the design strategy and application of nanotechnology for rapid detection of HMs and pathogenic diseases in plant crops were emphasized. The engineered nanomaterials have great potential for biosensors with high sensitivity and selectivity, high signal throughput, and reproducibility through various detection approaches such as Raman, colorimetric, biological, chemical, and electrical sensors. We obtain that the development of microfluidic and lab-on-a-chip (LoC) technologies offers the opportunity to create on-site portable and smart biodevices and chips for real-time monitoring of plant diseases. The last part of this work is a brief introduction to trends in nanotechnology for harvesting and packaging to provide insights into the overall applications of nanotechnology for crop production quality. This review provides the current advent of nanotechnology in agriculture, which is essential for further studies examining novel applications for sustainable agriculture.
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Affiliation(s)
- Nhat Nam Nguyen
- School of Agriculture and Aquaculture, Tra Vinh University, Tra Vinh City, 87000, Viet Nam
| | - Ngoc Trai Nguyen
- School of Agriculture and Aquaculture, Tra Vinh University, Tra Vinh City, 87000, Viet Nam
| | - Phuong Thuy Nguyen
- School of Agriculture and Aquaculture, Tra Vinh University, Tra Vinh City, 87000, Viet Nam
| | - Quoc Nam Phan
- School of Agriculture and Aquaculture, Tra Vinh University, Tra Vinh City, 87000, Viet Nam
| | - Truc Linh Le
- School of Agriculture and Aquaculture, Tra Vinh University, Tra Vinh City, 87000, Viet Nam
| | - Hoang Dang Khoa Do
- NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ward 13, District 04, Ho Chi Minh City, Viet Nam
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2
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Eskandari V, Sahbafar H, Zeinalizad L, Hadi A. A review of applications of surface-enhanced raman spectroscopy laser for detection of biomaterials and a quick glance into its advances for COVID-19 investigations. ISSS JOURNAL OF MICRO AND SMART SYSTEMS 2022; 11:363-382. [PMID: 35540110 PMCID: PMC9070975 DOI: 10.1007/s41683-022-00103-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/19/2022] [Accepted: 03/27/2022] [Indexed: 11/28/2022]
Abstract
Surface-enhanced Raman spectroscopy (SERS) is one of the most sensitive analytical tools. In some cases, it is possible to record a high-quality SERS spectrum in which even a single molecule is involved. Therefore, SERS is considered a significantly promising option as an alternative to routine analytical techniques used in food, environmental, biochemical, and medical analyzes. In this review, the definitive applications of SERS developed to identify biochemically important species (especially medical and biological) from the simplest to the most complex are briefly discussed. Moreover, the potential capability of SERS for being used as an alternative to routine methods in diagnostic and clinical cases is demonstrated. In addition, this article describes how SERS-based sensors work, addresses its advancements in the last 20 years, discusses its applications for detecting Coronavirus Disease 2019 (COVID-19), and finally describes future works. The authors hope that this article will be useful for researchers who want to enter this amazing field of research.
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Affiliation(s)
- Vahid Eskandari
- Cellular and Molecular Research Center, Yasuj University of Medical Sciences, Yasuj, Iran
| | - Hossein Sahbafar
- School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Leila Zeinalizad
- Faculty of Biomedical Engineering, Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Amin Hadi
- Cellular and Molecular Research Center, Yasuj University of Medical Sciences, Yasuj, Iran
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3
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Saleem M, Majeed MI, Nawaz H, Iqbal MA, Hassan A, Rashid N, Tahir M, Raza A, ul Hassan HM, Sabir A, Ashfaq R, Sharif S. Surface-Enhanced Raman Spectroscopy for the Characterization of the Antibacterial Properties of Imidazole Derivatives against Bacillus subtilis with Principal Component Analysis and Partial Least Squares–Discriminant Analysis. ANAL LETT 2022. [DOI: 10.1080/00032719.2022.2047997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
- Mudassar Saleem
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | | | - Haq Nawaz
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Muhammad Adnan Iqbal
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Ahmad Hassan
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Nosheen Rashid
- Department of Chemistry, University of Education, Faisalabad Campus, Faisalabad, Pakistan
| | - Muhammad Tahir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Ali Raza
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | | | - Amina Sabir
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Rayha Ashfaq
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Sana Sharif
- Department of Chemistry, University of Agriculture Faisalabad, Faisalabad, Pakistan
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Fang S, Zhao Y, Wang Y, Li J, Zhu F, Yu K. Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage. FRONTIERS IN PLANT SCIENCE 2022; 13:802761. [PMID: 35310652 PMCID: PMC8931522 DOI: 10.3389/fpls.2022.802761] [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: 10/29/2021] [Accepted: 01/14/2022] [Indexed: 06/14/2023]
Abstract
Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases.
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Affiliation(s)
- Shiyan Fang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, China
| | - Yanru Zhao
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, China
| | - Yan Wang
- College of Plant Protection, Northwest A&F University, Yangling, China
| | - Junmeng Li
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, China
| | - Fengle Zhu
- School of Computer and Computing Science, Zhejiang University City College, Hangzhou, China
| | - Keqiang Yu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, China
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5
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Yang D, Wang F, Hu Y, Lan Y, Deng X. Citrus Huanglongbing Detection Based on Multi-Modal Feature Fusion Learning. FRONTIERS IN PLANT SCIENCE 2021; 12:809506. [PMID: 35027917 PMCID: PMC8751206 DOI: 10.3389/fpls.2021.809506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/06/2021] [Indexed: 06/14/2023]
Abstract
Citrus Huanglongbing (HLB), also named citrus greening disease, occurs worldwide and is known as a citrus cancer without an effective treatment. The symptoms of HLB are similar to those of nutritional deficiency or other disease. The methods based on single-source information, such as RGB images or hyperspectral data, are not able to achieve great detection performance. In this study, a multi-modal feature fusion network, combining a RGB image network and hyperspectral band extraction network, was proposed to recognize HLB from four categories (HLB, suspected HLB, Zn-deficient, and healthy). Three contributions including a dimension-reduction scheme for hyperspectral data based on a soft attention mechanism, a feature fusion proposal based on a bilinear fusion method, and auxiliary classifiers to extract more useful information are introduced in this manuscript. The multi-modal feature fusion network can effectively classify the above four types of citrus leaves and is better than single-modal classifiers. In experiments, the highest accuracy of multi-modal network recognition was 97.89% when the amount of data was not very abundant (1,325 images of the four aforementioned types and 1,325 pieces of hyperspectral data), while the single-modal network with RGB images only achieved 87.98% recognition and the single-modal network using hyperspectral information only 89%. Results show that the proposed multi-modal network implementing the concept of multi-source information fusion provides a better way to detect citrus HLB and citrus deficiency.
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Affiliation(s)
- Dongzi Yang
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Fengcheng Wang
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Yuqi Hu
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
| | - Yubin Lan
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
- Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou, China
| | - Xiaoling Deng
- College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China
- Guangdong Engineering Technology Research Center of Smart Agriculture, Guangzhou, China
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6
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Vallejo-Pérez MR, Sosa-Herrera JA, Navarro-Contreras HR, Álvarez-Preciado LG, Rodríguez-Vázquez ÁG, Lara-Ávila JP. Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition. PLANTS (BASEL, SWITZERLAND) 2021; 10:1542. [PMID: 34451590 PMCID: PMC8399098 DOI: 10.3390/plants10081542] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 07/17/2021] [Accepted: 07/22/2021] [Indexed: 12/20/2022]
Abstract
Bacterial canker of tomato is caused by Clavibacter michiganensis subsp. michiganensis (Cmm). The disease is highly destructive, because it produces latent asymptomatic infections that favor contagion rates. The present research aims consisted on the implementation of Raman spectroscopy (RS) and machine-learning spectral analysis as a method for the early disease detection. Raman spectra were obtained from infected asymptomatic tomato plants (BCTo) and healthy controls (HTo) with 785 nm excitation laser micro-Raman spectrometer. Spectral data were normalized and processed by principal component analysis (PCA), then the classifiers algorithms multilayer perceptron (PCA + MLP) and linear discriminant analysis (PCA + LDA) were implemented. Bacterial isolation and identification (16S rRNA gene sequencing) were realized of each plant studied. The Raman spectra obtained from tomato leaf samples of HTo and BCTo exhibited peaks associated to cellular components, and the most prominent vibrational bands were assigned to carbohydrates, carotenoids, chlorophyll, and phenolic compounds. Biochemical changes were also detectable in the Raman spectral patterns. Raman bands associated with triterpenoids and flavonoids compounds can be considered as indicators of Cmm infection during the asymptomatic stage. RS is an efficient, fast and reliable technology to differentiate the tomato health condition (BCTo or HTo). The analytical method showed high performance values of sensitivity, specificity and accuracy, among others.
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Affiliation(s)
- Moisés Roberto Vallejo-Pérez
- Consejo Nacional de Ciencia y Tecnología-Universidad Autónoma de San Luis Potosí, CIACYT, Alvaro Obregon 64, Col. Centro, San Luis Potosí 78000, Mexico
- Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Col Lomas 2a. Sección, San Luis Potosí 78210, Mexico; (H.R.N.-C.); (L.G.Á.-P.); (Á.G.R.-V.)
| | - Jesús Antonio Sosa-Herrera
- Consejo Nacional de Ciencia y Tecnología-Centro de Investigación en Ciencias de Información Geoespacial A. C., Laboratorio Nacional de Geointeligencia, Aguascalientes 20313, Mexico;
| | - Hugo Ricardo Navarro-Contreras
- Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Col Lomas 2a. Sección, San Luis Potosí 78210, Mexico; (H.R.N.-C.); (L.G.Á.-P.); (Á.G.R.-V.)
| | - Luz Gabriela Álvarez-Preciado
- Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Col Lomas 2a. Sección, San Luis Potosí 78210, Mexico; (H.R.N.-C.); (L.G.Á.-P.); (Á.G.R.-V.)
| | - Ángel Gabriel Rodríguez-Vázquez
- Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Col Lomas 2a. Sección, San Luis Potosí 78210, Mexico; (H.R.N.-C.); (L.G.Á.-P.); (Á.G.R.-V.)
| | - José Pablo Lara-Ávila
- Facultad de Agronomía y Veterinaria, Universidad Autónoma de San Luis Potosí, Km. 14.5 Carretera San Luis Potosí, Matehuala, Ejido Palma de la Cruz, Soledad de Graciano Sánchez, San Luis Potosí 78321, Mexico;
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Krivitsky V, Granot E, Avidor Y, Borberg E, Voegele RT, Patolsky F. Rapid Collection and Aptamer-Based Sensitive Electrochemical Detection of Soybean Rust Fungi Airborne Urediniospores. ACS Sens 2021; 6:1187-1198. [PMID: 33507747 PMCID: PMC8023804 DOI: 10.1021/acssensors.0c02452] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 01/20/2021] [Indexed: 02/07/2023]
Abstract
Plants are the central source of food for humans around the world. Unfortunately, plants can be negatively affected by diverse kinds of diseases that are responsible for major economic losses worldwide. Thus, monitoring plant health and early detection of pathogens are essential to reduce disease spread and facilitate effective management practices. Various detection approaches are currently practiced. These methods mainly include visual inspection and laboratory tests. Nonetheless, these methods are labor-intensive, time-consuming, expensive, and inefficient in the early stages of infection. Thus, it is extremely important to detect diseases at the early stages of the epidemic. Here, we would like to present a fast, sensitive, and reliable electrochemical sensing platform for the detection of airborne soybean rust spores. The suspected airborne soybean rust spores are first collected and trapped inside a carbon 3D electrode matrix by high-capacity air-sampling means. Then, a specific biotinylated aptamer, suitable to target specific sites of soybean rust spores is applied. This aptamer agent binds to the surface of the collected spores on the electrode. Finally, spore-bound aptamer units are incubated with a streptavidin-alkaline phosphatase agent leading to the enzymatic formation of p-nitrophenol, which is characterized by its unique electrochemical properties. Our method allows for the rapid (ca. 2 min), selective, and sensitive collection and detection of soybean rust spores (down to the limit of 100-200 collected spores per cm2 of electrode area). This method could be further optimized for its sensitivity and applied to the future multiplex early detection of various airborne plant diseases.
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Affiliation(s)
- Vadim Krivitsky
- School
of Chemistry, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Eran Granot
- School
of Chemistry, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | | | - Ella Borberg
- School
of Chemistry, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ralf T. Voegele
- Institute
of Phytomedicine, University of Hohenheim, Stuttgart 70599, Germany
| | - Fernando Patolsky
- School
of Chemistry, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel
- Department
of Materials Science and Engineering, the Iby and Aladar Fleischman
Faculty of Engineering, Tel Aviv University, Tel Aviv 69978, Israel
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Weng S, Hu X, Wang J, Tang L, Li P, Zheng S, Zheng L, Huang L, Xin Z. Advanced Application of Raman Spectroscopy and Surface-Enhanced Raman Spectroscopy in Plant Disease Diagnostics: A Review. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2021; 69:2950-2964. [PMID: 33677962 DOI: 10.1021/acs.jafc.0c07205] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Plant diseases result in 20-40% of agricultural loss every year worldwide. Timely detection of plant diseases can effectively prevent the development and spread of diseases and ensure the agricultural yield. High-throughput and rapid methods are in great demand. This review investigates the advanced application of Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) in the detection of plant diseases. The determination of bacterial diseases and stress-induced diseases, fungal diseases, viral diseases, pests in beans, and mycotoxins related to plant diseases using RS and SERS are discussed in detail. Then, biomarkers for RS and SERS detection are analyzed with regard to plant disease diagnosis. Finally, the advantages and challenges are further illustrated. Additionally, potential alternatives are proposed for the challenges. The review is expected to provide a reference and guidance for the use of RS and SERS in plant disease diagnostics.
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Affiliation(s)
- Shizhuang Weng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei 230601, People's Republic of China
| | - Xujin Hu
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei 230601, People's Republic of China
| | - Jinghong Wang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei 230601, People's Republic of China
| | - Le Tang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei 230601, People's Republic of China
| | - Pan Li
- Hefei Institute of Physical Science, Chinese Academy of Sciences, 350 Shushanhu Road, Hefei 230031, People's Republic of China
| | - Shouguo Zheng
- Hefei Institute of Physical Science, Chinese Academy of Sciences, 350 Shushanhu Road, Hefei 230031, People's Republic of China
| | - Ling Zheng
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei 230601, People's Republic of China
| | - Linsheng Huang
- National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, 111 Jiulong Road, Hefei 230601, People's Republic of China
| | - Zhenghua Xin
- College of Information Engineering, Suzhou University, 1769 Xuefu Avenue, Suzhou, People's Republic of China
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9
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Payne WZ, Kurouski D. Raman-Based Diagnostics of Biotic and Abiotic Stresses in Plants. A Review. FRONTIERS IN PLANT SCIENCE 2021; 11:616672. [PMID: 33552109 PMCID: PMC7854695 DOI: 10.3389/fpls.2020.616672] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 12/17/2020] [Indexed: 05/11/2023]
Abstract
Digital farming is a novel agricultural philosophy that aims to maximize a crop yield with the minimal environmental impact. Digital farming requires the development of technologies that can work directly in the field providing information about a plant health. Raman spectroscopy (RS) is an emerging analytical technique that can be used for non-invasive, non-destructive, and confirmatory diagnostics of diseases, as well as the nutrient deficiencies in plants. RS is also capable of probing nutritional content of grains, as well as highly accurate identification plant species and their varieties. This allows for Raman-based phenotyping and digital selection of plants. These pieces of evidence suggest that RS can be used for chemical-free surveillance of plant health directly in the field. High selectivity and specificity of this technique show that RS may transform the agriculture in the US. This review critically discusses the most recent research articles that demonstrate the use of RS in diagnostics of abiotic and abiotic stresses in plants, as well as the identification of plant species and their nutritional analysis.
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Affiliation(s)
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
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10
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Root samples provide early and improved detection of Candidatus Liberibacter asiaticus in Citrus. Sci Rep 2020; 10:16982. [PMID: 33046775 PMCID: PMC7550583 DOI: 10.1038/s41598-020-74093-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 09/23/2020] [Indexed: 11/08/2022] Open
Abstract
Huanglongbing (HLB), or Citrus Greening, is one of the most devastating diseases affecting agriculture today. Widespread throughout Citrus growing regions of the world, it has had severe economic consequences in all areas it has invaded. With no treatment available, management strategies focus on suppression and containment. Effective use of these costly control strategies relies on rapid and accurate identification of infected plants. Unfortunately, symptoms of the disease are slow to develop and indistinct from symptoms of other biotic/abiotic stressors. As a result, diagnosticians have focused on detecting the pathogen, Candidatus Liberibacter asiaticus, by DNA-based detection strategies utilizing leaf midribs for sampling. Recent work has shown that fibrous root decline occurs in HLB-affected trees before symptom development among leaves. Moreover, the pathogen, Ca. Liberibacter asiaticus, has been shown to be more evenly distributed within roots than within the canopy. Motivated by these observations, a longitudinal study of young asymptomatic trees was established to observe the spread of disease through time and test the relative effectiveness of leaf- and root-based detection strategies. Detection of the pathogen occurred earlier, more consistently, and more often in root samples than in leaf samples. Moreover, little influence of geography or host variety was found on the probability of detection.
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11
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Farber C, Mahnke M, Sanchez L, Kurouski D. Advanced spectroscopic techniques for plant disease diagnostics. A review. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2019.05.022] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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12
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The Early, Rapid, and Non-Destructive Detection of Citrus Huanglongbing (HLB) Based on Microscopic Confocal Raman. FOOD ANAL METHOD 2019. [DOI: 10.1007/s12161-019-01598-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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13
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Mandrile L, Rotunno S, Miozzi L, Vaira AM, Giovannozzi AM, Rossi AM, Noris E. Nondestructive Raman Spectroscopy as a Tool for Early Detection and Discrimination of the Infection of Tomato Plants by Two Economically Important Viruses. Anal Chem 2019; 91:9025-9031. [PMID: 31265250 DOI: 10.1021/acs.analchem.9b01323] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Global population forecasts dictate a rapid adoption of multifaceted approaches to fulfill increasing food requirements, ameliorate food dietary value and security using sustainable and economically feasible agricultural processes. Plant pathogens induce up to 25% losses in vegetable crops and their early detection would contribute to limit their spread and economic impact. As an alternative to time-consuming, destructive, and expensive diagnostic procedures, such as immunological assays and nucleic acid-based techniques, Raman spectroscopy (RS) is a nondestructive rapid technique that generates a chemical fingerprinting of a sample, at low operating costs. Here, we assessed the suitability of RS combined to chemometric analysis to monitor the infection of an important vegetable crop plant, tomato, by two dangerous and peculiarly different viral pathogens, Tomato yellow leaf curl Sardinia virus (TYLCSV) and Tomato spotted wilt virus (TSWV). Experimentally inoculated plants were monitored over 28 days for symptom occurrence and subjected to RS analysis, alongside with measuring the virus amount by quantitative real-time PCR. RS allowed to discriminate mock inoculated (healthy) from virus-infected specimens, reaching an accuracy of >70% after only 14 days after inoculation for TYLCSV and >85% only after 8 days for TSWV, demonstrating its suitability for early detection of virus infection. Importantly, RS also highlighted spectral differences induced by the two viruses, providing specific information on the infecting agent.
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Affiliation(s)
- Luisa Mandrile
- Istituto Nazionale di Ricerca Metrologica , Strada delle Cacce, 91 , 10135 , Torino , Italy
| | - Silvia Rotunno
- Institute for Sustainable Plant Protection, National Research Council of Italy , Strada delle Cacce, 73 , 10135 , Torino , Italy
| | - Laura Miozzi
- Institute for Sustainable Plant Protection, National Research Council of Italy , Strada delle Cacce, 73 , 10135 , Torino , Italy
| | - Anna Maria Vaira
- Institute for Sustainable Plant Protection, National Research Council of Italy , Strada delle Cacce, 73 , 10135 , Torino , Italy
| | - Andrea M Giovannozzi
- Istituto Nazionale di Ricerca Metrologica , Strada delle Cacce, 91 , 10135 , Torino , Italy
| | - Andrea M Rossi
- Istituto Nazionale di Ricerca Metrologica , Strada delle Cacce, 91 , 10135 , Torino , Italy
| | - Emanuela Noris
- Institute for Sustainable Plant Protection, National Research Council of Italy , Strada delle Cacce, 73 , 10135 , Torino , Italy
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Rapid and noninvasive diagnostics of Huanglongbing and nutrient deficits on citrus trees with a handheld Raman spectrometer. Anal Bioanal Chem 2019; 411:3125-3133. [PMID: 30989272 DOI: 10.1007/s00216-019-01776-4] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 03/11/2019] [Indexed: 12/17/2022]
Abstract
Huanglongbing (HLB) or citrus greening is a devastating disease of citrus trees that is caused by the gram-negative Candidatus Liberibacter spp. bacteria. The bacteria are phloem limited and transmitted by the Asian citrus psyllid, Diaphorina citri, and the African citrus psyllid, Trioza erytreae, which allows for a wider dissemination of HLB. Infected trees exhibit yellowing of leaves, premature leaf and fruit drop, and ultimately the death of the entire plant. Polymerase chain reaction (PCR) and antibody-based assays (ELISA and/or immunoblot) are commonly used methods for HLB diagnostics. However, they are costly, time-consuming, and destructive to the sample and often not sensitive enough to detect the pathogen very early in the infection stage. Raman spectroscopy (RS) is a noninvasive, nondestructive, analytical technique which provides insight into the chemical structures of a specimen. In this study, by using a handheld Raman system in combination with chemometric analyses, we can readily distinguish between healthy and HLB (early and late stage)-infected citrus trees, as well as plants suffering from nutrient deficits. The detection rate of Raman-based diagnostics of healthy vs HLB infected vs nutrient deficit is ~ 98% for grapefruit and ~ 87% for orange trees, whereas the accuracy of early- vs late-stage HLB infected is 100% for grapefruits and ~94% for oranges. This analysis is portable and sample agnostic, suggesting that it could be utilized for other crops and conducted autonomously. Graphical abstract.
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Rao G, Huang L, Liu M, Chen T, Chen J, Luo Z, Xu F, Xu X, Yao M. Identification of Huanglongbing-infected navel oranges based on laser-induced breakdown spectroscopy combined with different chemometric methods. APPLIED OPTICS 2018; 57:8738-8742. [PMID: 30461952 DOI: 10.1364/ao.57.008738] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 09/17/2018] [Indexed: 06/09/2023]
Abstract
In order to realize rapid identification of Gannan navel oranges infected by Huanglongbing (HLB), a full optical diagnostic method of laser-induced breakdown spectroscopy (LIBS) was proposed. All navel oranges were collected from Ganzhou, Jiangxi, China, and samples contain healthy and HLB-infected navel oranges. The LIBS spectra of the plasma plume were collected directly from the epidermis of these navel oranges. The navel orange LIBS spectra in the wavelength range of 200-1050 nm were pretreated with smoothing and multiple scatter correction; on the basis of 10×10-fold cross validation, a random forest (RF) model based on continuous wavelet transform (CWT) and principal component analysis (PCA) were analyzed to identify the navel orange of HLB. The results showed that the PCA-RF and CWT-RF models coupled with suitable methods in preprocessing data can identify HLB-infected navel oranges. The average accuracy obtained from the CWT-RF model was 96.86% in the training set and 97.45% in the test set; the average accuracy by the PCA-RF model was 97.64% in the training set and 97.89% in the test set. The overall results demonstrate that LIBS combined with CWT-RF or PCA-RF, as a valuable analytical tool, could be used for HLB-infected navel orange identification.
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Pérez MRV, Contreras HRN, Sosa Herrera JA, Ávila JPL, Tobías HMR, Martínez FDB, Ramírez RF, Vázquez ÁGR. Detection of Clavibacter michiganensis subsp. michiganensis Assisted by Micro-Raman Spectroscopy under Laboratory Conditions. THE PLANT PATHOLOGY JOURNAL 2018; 34:381-392. [PMID: 30369848 PMCID: PMC6200046 DOI: 10.5423/ppj.oa.02.2018.0019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 05/10/2018] [Accepted: 05/31/2018] [Indexed: 06/08/2023]
Abstract
Clavibacter michiganensis subsp. michiganesis (Cmm) is a quarantine-worthy pest in México. The implementation and validation of new technologies is necessary to reduce the time for bacterial detection in laboratory conditions and Raman spectroscopy is an ambitious technology that has all of the features needed to characterize and identify bacteria. Under controlled conditions a contagion process was induced with Cmm, the disease epidemiology was monitored. Micro-Raman spectroscopy (532 nm λ laser) technique was evaluated its performance at assisting on Cmm detection through its characteristic Raman spectrum fingerprint. Our experiment was conducted with tomato plants in a completely randomized block experimental design (13 plants × 4 rows). The Cmm infection was confirmed by 16S rDNA and plants showed symptoms from 48 to 72 h after inoculation, the evolution of the incidence and severity on plant population varied over time and it kept an aggregated spatial pattern. The contagion process reached 79% just 24 days after the epidemic was induced. Micro-Raman spectroscopy proved its speed, efficiency and usefulness as a non-destructive method for the preliminary detection of Cmm. Carotenoid specific bands with wavelengths at 1146 and 1510 cm-1 were the distinguishable markers. Chemometric analyses showed the best performance by the implementation of PCA-LDA supervised classification algorithms applied over Raman spectrum data with 100% of performance in metrics of classifiers (sensitivity, specificity, accuracy, negative and positive predictive value) that allowed us to differentiate Cmm from other endophytic bacteria (Bacillus and Pantoea). The unsupervised KMeans algorithm showed good performance (100, 96, 98, 91 y 100%, respectively).
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Affiliation(s)
- Moisés Roberto Vallejo Pérez
- CONACyT-Universidad Autónoma de San Luis Potosí. Álvaro Obregón #64, Col. Centro, C.P. 78000, San Luis Potosí, S.L.P.
México
| | - Hugo Ricardo Navarro Contreras
- Universidad Autónoma de San Luis Potosí. Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACyT). Av. Sierra Leona #550, Col. Lomas 2a. Sección, C.P. 78210, S.L.P.,
México
| | - Jesús A. Sosa Herrera
- CONACyT-Centro de Investigación en Ciencias de Información Geoespacial A.C. Circuito Tecnopolo Norte 117, Col. Fraccionamiento Tecnopolo Pocitos, CP. 20313, Aguascalientes, Ags.
México
| | - José Pablo Lara Ávila
- Universidad Autónoma de San Luis Potosí. Facultad de Agronomía y Veterinaria. Km. 14.5 Carretera San Luis Potosí, Matehuala, Ejido Palma de la Cruz, Soledad de Graciano Sánchez, C.P. 78321. S.L.P.
México
| | - Hugo Magdaleno Ramírez Tobías
- Universidad Autónoma de San Luis Potosí. Facultad de Agronomía y Veterinaria. Km. 14.5 Carretera San Luis Potosí, Matehuala, Ejido Palma de la Cruz, Soledad de Graciano Sánchez, C.P. 78321. S.L.P.
México
| | - Fernando Díaz-Barriga Martínez
- Universidad Autónoma de San Luis Potosí. Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACyT). Av. Sierra Leona #550, Col. Lomas 2a. Sección, C.P. 78210, S.L.P.,
México
| | - Rogelio Flores Ramírez
- CONACyT-Universidad Autónoma de San Luis Potosí. Álvaro Obregón #64, Col. Centro, C.P. 78000, San Luis Potosí, S.L.P.
México
| | - Ángel Gabriel Rodríguez Vázquez
- Universidad Autónoma de San Luis Potosí. Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACyT). Av. Sierra Leona #550, Col. Lomas 2a. Sección, C.P. 78210, S.L.P.,
México
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Ramírez-Elías MG, Kolosovas-Machuca E, Kershenobich D, Guzmán C, Escobedo G, González FJ. Evaluation of liver fibrosis using Raman spectroscopy and infrared thermography: A pilot study. Photodiagnosis Photodyn Ther 2017; 19:278-283. [DOI: 10.1016/j.pdpdt.2017.07.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Revised: 06/22/2017] [Accepted: 07/24/2017] [Indexed: 12/18/2022]
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Zhao YR, Yu KQ, Li X, He Y. Detection of Fungus Infection on Petals of Rapeseed (Brassica napus L.) Using NIR Hyperspectral Imaging. Sci Rep 2016; 6:38878. [PMID: 27958386 PMCID: PMC5153619 DOI: 10.1038/srep38878] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 11/15/2016] [Indexed: 11/08/2022] Open
Abstract
Infected petals are often regarded as the source for the spread of fungi Sclerotinia sclerotiorum in all growing process of rapeseed (Brassica napus L.) plants. This research aimed to detect fungal infection of rapeseed petals by applying hyperspectral imaging in the spectral region of 874-1734 nm coupled with chemometrics. Reflectance was extracted from regions of interest (ROIs) in the hyperspectral image of each sample. Firstly, principal component analysis (PCA) was applied to conduct a cluster analysis with the first several principal components (PCs). Then, two methods including X-loadings of PCA and random frog (RF) algorithm were used and compared for optimizing wavebands selection. Least squares-support vector machine (LS-SVM) methodology was employed to establish discriminative models based on the optimal and full wavebands. Finally, area under the receiver operating characteristics curve (AUC) was utilized to evaluate classification performance of these LS-SVM models. It was found that LS-SVM based on the combination of all optimal wavebands had the best performance with AUC of 0.929. These results were promising and demonstrated the potential of applying hyperspectral imaging in fungus infection detection on rapeseed petals.
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Affiliation(s)
- Yan-Ru Zhao
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Ke-Qiang Yu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, 712100, China
| | - Xiaoli Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Yong He
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
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