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Advanced methods of plant disease detection. A review

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Agronomy for Sustainable Development Aims and scope Submit manuscript

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.

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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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Acknowledgments

We are grateful to Chiara Nepi for providing iconographic materials. We thank Minghua Zhang and colleagues for permission to use their data in our Fig. 4 and Jorge Torres-Sánchez and colleagues for permission to use their picture in our Fig. 5. CED was supported by the California Citrus Research Board (CRB), the Industry-University Cooperative Research Program (UC Discovery), the Florida Citrus Production Advisory Council (FCPRAC), and the National Science Foundation (no. 1255915).

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Authors and Affiliations

  1. Department of Agricultural and Forest Sciences, University of Palermo, viale delle Scienze, 90128, Palermo, Italy

    Federico Martinelli, Riccardo Scalenghe, Salvatore Davino & Paolo Ruisi

  2. I.E.ME.S.T. Istituto Euro Mediterraneo di Scienza e Tecnologia, Via Emerico Amari 123, 90139, Palermo, Italy

    Federico Martinelli, Salvatore Davino, Stefano Panno & Giuseppe Scuderi

  3. Department of Agri-food and Environmental Systems Management, University of Catania, Via S. Sofia 100, 95123, Catania, Italy

    Giuseppe Scuderi

  4. Institute for Electromagnetic Sensing of the Environment, National Research Council (IREA-CNR), Via Bassini 15, 20133, Milano, Italy

    Paolo Villa, Daniela Stroppiana & Mirco Boschetti

  5. Laboratory of Nanobiotechnology, Institute of Genetics and Biochemistry, Universidade Federal de Uberlandia, 38400-902, Uberlandia, MG, Brazil

    Luiz R. Goulart

  6. Mechanical and Aerospace Engineering Department, University of California, Davis, CA, 95616, USA

    Cristina E. Davis

  7. Department of Plant Sciences, University of California, One Shields Avenue, Mail Stop 4, Davis, CA, 95616, USA

    Abhaya M. Dandekar

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  1. Federico Martinelli
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  2. Riccardo Scalenghe
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  3. Salvatore Davino
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  4. Stefano Panno
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  5. Giuseppe Scuderi
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  6. Paolo Ruisi
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  7. Paolo Villa
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  8. Daniela Stroppiana
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  9. Mirco Boschetti
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  10. Luiz R. Goulart
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  11. Cristina E. Davis
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  12. Abhaya M. Dandekar
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Corresponding author

Correspondence to Federico Martinelli.

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Martinelli, F., Scalenghe, R., Davino, S. et al. Advanced methods of plant disease detection. A review. Agron. Sustain. Dev. 35, 1–25 (2015). https://doi.org/10.1007/s13593-014-0246-1

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