Image pattern classification for the identification of disease causing agents in plants

By Anyela Camargo, Jeremy S. Smith. 2009

346 citations recorded by the source corpus.

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Plants Disease Identification and Classification Through Leaf Images: A Survey (2018, background): “It trains SVM on several combinations of features to identify the best one [79].”
Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach (2021, neutral): “Some popular classiﬁers are K-nearest neighbor (KNN) [6], support vector machine (SVM) [7], decision tree, random forest (RF) [8], naive Bayes (NB), logistic regression (LR), rule generation [9], artiﬁcial neural networks (ANNs), and Deep CNN. KNN is a simple supervised-machine-learning algorithm…”
Advanced methods of plant disease detection. A review (2014, neutral): “…influence vegetation conditions (Grisham et al. 2010; Malthus and Madeira 1993; Naidu et al. 2009; Bravo et al. 2003; Reynolds et al. 2012; Hadjiloucas et al. 2009; Lichtenthaler et al.1996; Camargo and Smith 2009; Agati et al. 2013; Bock et al. 2008), while some studies address both conditions.”
Digital image processing techniques for detecting, quantifying and classifying plant diseases (2013, methodology): “Camargo and Smith (2009a) proposed a method to identify regions of leaves containing lesions caused by diseases.”
Current and future applications of statistical machine learning algorithms for agricultural machine vision systems (2019, background): “Camargo and Smith (2009) utilized SVMs to identify visual symptoms of cotton diseases in images.”