Agronomy for Sustainable Development

, Volume 35, Issue 1, pp 1–25 | Cite as

Advanced methods of plant disease detection. A review

  • Federico Martinelli
  • Riccardo Scalenghe
  • Salvatore Davino
  • Stefano Panno
  • Giuseppe Scuderi
  • Paolo Ruisi
  • Paolo Villa
  • Daniela Stroppiana
  • Mirco Boschetti
  • Luiz R. Goulart
  • Cristina E. Davis
  • Abhaya M. Dandekar
Review Article

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.

Keywords

DNA-based methods Immunological assays Spectroscopy Biophotonics Plant disease Remote sensing Volatile organic compounds Commercial kits 

Abbreviations

ANN

Artificial neural networks

APAR

Absorbed photosynthetic active radiation

ARDRA

Amplified 16S ribosomal DNA restriction analysis

AVIRIS

Airborne visible/infrared imaging spectrometer

BAW

Beet armyworms

BLAST

Basic local alignment search tool

CDR

Complementary determining regions

CMV

Cucumber mosaic virus

Co-PCR

Cooperative PCR

DMNT

Dimethylonatriene

DMS

Differential mobility spectrometry

dNTP

Nucleoside triphosphates containing deoxyribose

dsDNA

Double-stranded DNA

ELISA

Enzyme-linked immunosorbent assay

EnMAP

Environmental mapping and analysis program

EO

Earth observation

EPPO

European and Mediterranean Plant Protection Organization

FAIMS

High field asymmetric waveform ion mobility spectrometry

FAO

Food and Agriculture Organization

FISH

Fluorescence in situ hybridization

GC-MS

Gas chromatography mass spectrometry

ICA-PCA

Independent-principal components analysis

LAMP

Loop-mediated isothermal amplification

LAI

Leaf Area Index

Landsat TM

Earth Resources Technology Satellite Thematic Mapper

LFM

Lateral flow microarrays

M-PCR

Multiplex PCR

MVA

Multivariate data analysis

NASBA

Nucleic acid sequence-based amplification

NIR

Near-infrared wavelength

NMR

Nuclear magnetic resonance

nPCR

Nested PCR

PCA

Principal component analysis

PFGE

Pulsed-field gel electrophoresis

PCR

Polymerase chain reaction

PDD

Plant disease detection

PLRV

Potato leafroll virus

PPV

Plum pox potyvirus

PRISMA

PRecursore IperSpettrale della Missione Applicativa

PTR-MS

Proton-transfer-reaction mass spectrometry

RAPD

Random amplified polymorphic DNA

rep-PCR

Repetitive-sequence PCR

RFLP

Restriction fragment length polymorphism

RS

Remote sensing

RTM

Radiative transfer modeling

RT-PCR

Real-time PCR

SAIL

Scattering by arbitrarily inclined leaves

SAM

Spectral angle mapper classification

SBSE

Stir bar sorptive extraction

scFv

Single-chain variable fragment

SELEX

Systematic evolution of ligands by exponential enrichment

SIFT-MS

Selected ion flow tube mass spectrometry

SMA

Spectral mixture analysis

SPME

Solid-phase microextraction

ssDNA

Single-stranded DNA

SNP

Single nucleotide polymophisms

SSEM

Serologically specific electron microscopy

ssRNA

Single-stranded RNA

STR

Short tandem repeats

SVIs

Spectral vegetation indices

SVM

Support vector machine

SWIR

Shortwave infrared wavelength

TIR

Thermal infrared wavelength

TMTT

trimethyltridecatetraene

TYLCD

Tomato yellow leaf curl disease

UAV

Unmanned aerial vehicle

VI

Vegetation indices

VIS

Visible wavelength

VOC

Volatile organic compounds

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Copyright information

© INRA and Springer-Verlag France 2014

Authors and Affiliations

  • 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
  1. 1.Department of Agricultural and Forest SciencesUniversity of PalermoPalermoItaly
  2. 2.I.E.ME.S.T. Istituto Euro Mediterraneo di Scienza e TecnologiaPalermoItaly
  3. 3.Department of Agri-food and Environmental Systems ManagementUniversity of CataniaCataniaItaly
  4. 4.Institute for Electromagnetic Sensing of the EnvironmentNational Research Council (IREA-CNR)MilanoItaly
  5. 5.Laboratory of Nanobiotechnology, Institute of Genetics and BiochemistryUniversidade Federal de UberlandiaUberlandiaBrazil
  6. 6.Mechanical and Aerospace Engineering DepartmentUniversity of CaliforniaDavisUSA
  7. 7.Department of Plant SciencesUniversity of CaliforniaDavisUSA

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