Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Detecting plant pathogens and diagnosing diseases are critical components of successful pest management. These key areas have undergone significant advancements driven by breakthroughs in molecular biology and remote sensing technologies within the realm of precision agriculture. Notably, nucleic acid amplification techniques, with recent emphasis on sequencing procedures, particularly next-generation sequencing, have enabled improved DNA or RNA amplification detection protocols that now enable previously unthinkable strategies aimed at dissecting plant microbiota, including the disease-causing components. Simultaneously, the domain of remote sensing has seen the emergence of cutting-edge imaging sensor technologies and the integration of powerful computational tools, such as machine learning. These innovations enable spectral analysis of foliar symptoms and specific pathogen-induced alterations, making imaging spectroscopy and thermal imaging fundamental tools for large-scale disease surveillance and monitoring. These technologies contribute significantly to understanding the temporal and spatial dynamics of plant diseases.
Detecting plant pathogens and diagnosing diseases are critical components of successful pest management. These key areas have undergone significant advancements driven by breakthroughs in molecular biology and remote sensing technologies within the realm of precision agriculture. Notably, nucleic acid amplification techniques, with recent emphasis on sequencing procedures, particularly next-generation sequencing, have enabled improved DNA or RNA amplification detection protocols that now enable previously unthinkable strategies aimed at dissecting plant microbiota, including the disease-causing components. Simultaneously, the domain of remote sensing has seen the emergence of cutting-edge imaging sensor technologies and the integration of powerful computational tools, such as machine learning. These innovations enable spectral analysis of foliar symptoms and specific pathogen-induced alterations, making imaging spectroscopy and thermal imaging fundamental tools for large-scale disease surveillance and monitoring. These technologies contribute significantly to understanding the temporal and spatial dynamics of plant diseases.
Ensuring the reliability of diagnostic activities is an essential cornerstone of Plant Health strategies to reduce the risk of entry and spread of plant pests in a region and ultimately their impacts. Diagnostic tests should be validated to ensure that they are fit for purpose. Validation is usually done by diagnostic laboratories although companies commercializing diagnostic kits also produce validation data for their products. Due to the high number of pest , matrix and method combinations and given the significant resources required to validate tests, it is essential that validation data are shared with the entire diagnostic community and produced in a harmonized way to facilitate their use by different stakeholders. Indeed, the selection of tests to be used in specific contexts is not the sole responsibility of diagnostic laboratories and also involve National Plant Protection Organizations. The VALITEST EU project (2018-2021) was established to tackle all these issues. New validation data for tests targeting important pests for the EPPO region were produced. Guidelines to improve and harmonize the validation framework were developed. Sharing of validation data and experience was ensured through the development of new or existing databases, the organization of training courses and the dissemination of the project outputs in scientific publications and Standards. Finally, the involvement of researchers, diagnosticians, policy makers, inspectors, industries etc. and the establishment of the European Plant Diagnostic Industry Association were important actions to strengthen the interactions between Plant Health stakeholders.
In 2022, a test performance study (TPS) assessing the influence of different master mixes on the performance of the tetraplex real-time PCR (TqPCR) assay was organized. TqPCR allows for the specific detection and identification of Xylella fastidiosa (Xf) subspecies in a single reaction. Seventeen official laboratories of the Italian National Plant Protection Organization received a panel of 12 blind samples, controls, primers, probes, and different master mixes to participate in the TPS. Furthermore, the Research Centre for Plant Protection and Certification of the Council for Agricultural Research and Economics performed an intra-laboratory study (ITS) on spiked plant matrices to evaluate the analytical sensitivity of TqPCR employing the selected master mixes with the best performance. Naturally infected samples were analyzed for subspecies identification via TqPCR compared with the official multilocus-sequence-typing (MLST) method. The best results in this comparative study were obtained using Fast Universal PCR Master Mix (Applied Biosystems) and Brilliant multiplex QPCR Master Mix (Agilent), and they confirmed that the TqPCR test is reliable, offering the advantage of identifying this subspecies at the same time, thus saving time and resources. The TqPCR assay is among the tests to be used by laboratories performing the official diagnosis of Xf to support the activities of official monitoring.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.