Advanced methods of plant disease detection. A review

By F. Martinelli, R. Scalenghe, S. Davino, S. Panno, G. Scuderi, P. Ruisi, P. Villa, D. Stroppiana, Mirco Boschetti, L. Goulart and 2 more. Agronomy for Sustainable Development, 2014

823 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/5a20a8e48857364effac144233be9be72b1440df

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Citations in the corpus

An In-field Automatic Wheat Disease Diagnosis System (2017, background): “…of computer vision, concerns are growing about the image-based detection technologies for crop disease (Camargo and Smith, 2009; Arivazhagan et al., 2013; Barbedo, 2014; Rastogi et al., 2015), which gets rid of the shackles of time cost and molecular analysis equipments (Martinelli et al., 2015).”
In‐field molecular diagnosis of plant pathogens: recent trends and future perspectives (2018, methodology): “Immunological techniques such as enzyme-linked immunosorbent assay (ELISA) (plate-trapped antigen-ELISA and double antibody sandwich-ELISA), immunofluorescence and multiplex immunodetection are also widely used; however, their costs are usually higher than PCR-based technologies and the shell life o”
Unmanned Aerial Vehicle (UAV)-based remote sensing to monitor grapevine leaf stripe disease within a vineyard affected by esca complex (2016, background): “Climate was identified as an important distinguishing element between the two growing seasons investigated in this study, and meteorological parameters are known to have a crucial influence on vegetative response of grapevines (Matese et al., 2012).”
Convolutional Neural Networks for the Automatic Identification of Plant Diseases (2019, methodology): “For more information on these techniques (see Sankaran et al., 2010; Mahlein et al., 2012; Martinelli et al., 2015; Barbedo, 2016).”
A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition (2017, methodology): “These include using direct methods closely related to the chemical analysis of the infected area of the plant [14–16], and indirect methods employing physical techniques, such as imaging and spectroscopy [17,18], to determine plant properties and stress-based disease detection.”
Plants Disease Identification and Classification Through Leaf Images: A Survey (2018, background): “Different plants disease detection techniques are proposed and a survey of traditional and innovative techniques is also presented in literature [13, 14].”
An advanced deep learning models-based plant disease detection: A review of recent research (2023, background): “In the past, researchers captured photos of plant pests and illnesses using indoor lightboxes (Martinelli et al., 2015).”
Diagnosis of plant diseases using the Nanopore sequencing platform (2018, background): “Various immunological and molecular diagnostic methods are commonly used to detect plant pathogens (De Boer & L opez, 2012; Martinelli et al., 2015).”