Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging

By Clive H. Bock, Gavin Poole, P. Parker, T. Gottwald. 2010

836 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Cell Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/adc8070273326ca56315dfbbf3f93b04f5409dea

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Citations in the corpus

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases (2021, background): “Moreover, it can result in erroneous predictions due to visual illusions and biased decisions [3].”
Digital image processing techniques for detecting, quantifying and classifying plant diseases (2013, background): “A large amount of information on the subject can be found in the papers by Bock et al. (2010), Mahlein et al. (2012) and Sankaran et al. (2010).”
Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning (2017, background): “The rapid, accurate diagnosis of disease severity will help to reduce yield losses [1].”
Advanced methods of plant disease detection. A review (2014, neutral): “…RS approaches for PDD, from the early works of Nutter (1989) and the overview of capabilities shown by West et al. (2003) to the seminal review of methods performed by Bock et al. (2010), and more recently by the works of Sankaran et al. (2010), Mahlein et al. (2012b), and Prabhakar et al. (2012).”
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, background): “Similarly, in [39], file reduction and subsetting, spectral library definition (e.”
Plants Disease Identification and Classification Through Leaf Images: A Survey (2018, methodology): “Although image analysis and hyperspectral imaging techniques are better than methods that rates disease severity visually [103].”
An explainable deep machine vision framework for plant stress phenotyping (2018, background): “C onventional plant stress identiﬁcation and classiﬁcation have invariably relied on human experts identifying visual symptoms as a means of categorization (1).”
A discussion on disease severity index values. Part I: warning on inherent errors and suggestions to maximise accuracy (2017, methodology): “Bock et al. (2010a) also stated ‘Originally, Horsfall & Barratt (1945) and Horsfall (1945) recommended averaging the grade values prior to using a calibration curve to read actual disease on the y-axis (a process that removed heterogeneity of variance, because each grade is given equal weight) but…”
HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging (2015, methodology): “Therefore, the machine-based classification method (in our case the HyperART system) was found to be more precise and reliable in quantifying disease severity, as compared to assessments based on visual inspection [29].”
Impacts of climate change on plant diseases—opinions and trends (2012, background): “(Bock et al. 2010; Norton and Taylor 2010).”