http://www.cnr.it/ontology/cnr/individuo/prodotto/ID285036

Advanced methods of plant disease detection. A review (Articolo in rivista)

Type

Prodotto della ricerca (Classe)
Articolo in rivista (Classe)

Label

Advanced methods of plant disease detection. A review (Articolo in rivista) (literal)

Anno

2014-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1007/s13593-014-0246-1 (literal)

Alternative label

Federico Martinelli (1) (2), Riccardo Scalenghe (1), Salvatore Davino (1) (2), Stefano Panno (2), Giuseppe Scuderi (2) (3), Paolo Ruisi (1), Paolo Villa (4), Daniela Stroppiana (4), Mirco Boschetti (4), Luiz R. Goulart (5), Cristina E. Davis (6), Abhaya M. Dandekar (7) (2014)
Advanced methods of plant disease detection. A review
in Agronomy for sustainable development; Springer Paris, Paris (Francia)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Federico Martinelli (1) (2), Riccardo Scalenghe (1), Salvatore Davino (1) (2), Stefano Panno (2), Giuseppe Scuderi (2) (3), Paolo Ruisi (1), Paolo Villa (4), Daniela Stroppiana (4), Mirco Boschetti (4), Luiz R. Goulart (5), Cristina E. Davis (6), Abhaya M. Dandekar (7) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

http://link.springer.com/article/10.1007/s13593-014-0246-1 (literal)

Rivista

Agronomy for sustainable development (Rivista)

Note

Google Scholar (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

(1) Department of Agricultural and Forest Sciences, University of Palermo, viale delle Scienze, 90128, Palermo, Italy (2) I.E.ME.S.T. Istituto Euro Mediterraneo di Scienza e Tecnologia, Via Emerico Amari 123, 90139, Palermo, Italy (3) Department of Agri-food and Environmental Systems Management, University of Catania, Via S. Sofia 100, 95123, Catania, Italy (4) Institute for Electromagnetic Sensing of the Environment, National Research Council (IREA-CNR), Via Bassini 15, 20133, Milano, Italy (5) Laboratory of Nanobiotechnology, Institute of Genetics and Biochemistry, Universidade Federal de Uberlandia, 38400-902, Uberlandia, MG, Brazil (6) Mechanical and Aerospace Engineering Department, University of California, Davis, CA, 95616, USA (7) Department of Plant Sciences, University of California, One Shields Avenue, Mail Stop 4, Davis, CA, 95616, USA (literal)

Titolo

Advanced methods of plant disease detection. A review (literal)

Abstract

Plant diseases are responsible for major economic losses in the agricultural industry worldwide. Monitoring plant health and detecting pathogen early are essential to reduce disease spread and facilitate effective management practices. DNA-based and serological methods now provide essential tools for accurate plant disease diagnosis, in addition to the traditional visual scouting for symptoms. Although DNA-based and serological methods have revolutionized plant disease detection, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic diffusion. They need at least 1-2 days for sample harvest, processing, and analysis. Here, we describe modern methods based on nucleic acid and protein analysis. Then, we review innovative approaches currently under development. Our main findings are the following: (1) novel sensors based on the analysis of host responses, e.g., differential mobility spectrometer and lateral flow devices, deliver instantaneous results and can effectively detect early infections directly in the field; (2) biosensors based on phage display and biophotonics can also detect instantaneously infections although they can be integrated with other systems; and (3) remote sensing techniques coupled with spectroscopy-based methods allow high spatialization of results, these techniques may be very useful as a rapid preliminary identification of primary infections. We explain how these tools will help plant disease management and complement serological and DNA-based methods. While serological and PCR-based methods are the most available and effective to confirm disease diagnosis, volatile and biophotonic sensors provide instantaneous results and may be used to identify infections at asymptomatic stages. Remote sensing technologies will be extremely helpful to greatly spatialize diagnostic results. These innovative techniques represent unprecedented tools to render agriculture more sustainable and safe, avoiding expensive use of pesticides in crop protection. (literal)

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