TY - JOUR
T1 - Plant Viral Disease Detection
T2 - From Molecular Diagnosis to Optical Sensing Technology—A Multidisciplinary Review
AU - Wang, Yeniu Mickey
AU - Ostendorf, Bertram
AU - Gautam, Deepak
AU - Habili, Nuredin
AU - Pagay, Vinay
N1 - Funding Information:
This research was funded by South Australia Australian Grapevine Foundation Planting Service Inc. (Grant number: DVCR711278), Riverland Wine Industry Development Council (Grant number 194388), and Wine Australia (Grant number: PPA002864). Y.M.W.?s study is supported by the Research Training Program, The University of Adelaide.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - Plant viral diseases result in productivity and economic losses to agriculture, necessitating accurate detection for effective control. Lab-based molecular testing is the gold standard for providing reliable and accurate diagnostics; however, these tests are expensive, time-consuming, and labour-intensive, especially at the field-scale with a large number of samples. Recent advances in optical remote sensing offer tremendous potential for non-destructive diagnostics of plant viral diseases at large spatial scales. This review provides an overview of traditional diagnostic methods followed by a comprehensive description of optical sensing technology, including camera systems, platforms, and spectral data analysis to detect plant viral diseases. The paper is organized along six multidisciplinary sections: (1) Impact of plant viral disease on plant physiology and consequent phenotypic changes, (2) direct diagnostic methods, (3) traditional indirect detection methods, (4) optical sensing technologies, (5) data processing techniques and modelling for disease detection, and (6) comparison of the costs. Finally, the current challenges and novel ideas of optical sensing for detecting plant viruses are discussed.
AB - Plant viral diseases result in productivity and economic losses to agriculture, necessitating accurate detection for effective control. Lab-based molecular testing is the gold standard for providing reliable and accurate diagnostics; however, these tests are expensive, time-consuming, and labour-intensive, especially at the field-scale with a large number of samples. Recent advances in optical remote sensing offer tremendous potential for non-destructive diagnostics of plant viral diseases at large spatial scales. This review provides an overview of traditional diagnostic methods followed by a comprehensive description of optical sensing technology, including camera systems, platforms, and spectral data analysis to detect plant viral diseases. The paper is organized along six multidisciplinary sections: (1) Impact of plant viral disease on plant physiology and consequent phenotypic changes, (2) direct diagnostic methods, (3) traditional indirect detection methods, (4) optical sensing technologies, (5) data processing techniques and modelling for disease detection, and (6) comparison of the costs. Finally, the current challenges and novel ideas of optical sensing for detecting plant viruses are discussed.
KW - plant viruses
KW - remote sensing
KW - hyperspectral imaging
KW - disease prediction modelling
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85127551313&partnerID=8YFLogxK
U2 - 10.3390/rs14071542
DO - 10.3390/rs14071542
M3 - Review article
SN - 2072-4292
VL - 14
SP - 1
EP - 24
JO - Remote Sensing
JF - Remote Sensing
IS - 7
M1 - 1542
ER -