Identification of citrus disease using color texture features and discriminant analysis

By R. Pydipati, T. Burks, Won Suk Lee. 2006

417 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/86e1a32754582bcb664c92d8969229a1cb276695

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In Plant Science

Citations in the corpus

Digital image processing techniques for detecting, quantifying and classifying plant diseases (2013, methodology): “Pydipati et al. (2006) method aims to detect and classify three different types of citrus diseases.”
Deep learning for plant identification using vein morphological patterns (2016, background): “Some works are focused on leaf shape [8–14], some use shape and texture [15], while others consider color and texture [16].”
A review on the main challenges in automatic plant disease identification based on visible range images (2016, background): “…Duchêne, Merdinoglu, & Mestre, 2011), using scanners (Berner & Paxson, 2003; Boissard, Martin, & Moisan, 2008; Romualdo et al. 2014), containers (Pydipati et al., 2006; Wijekoon, Goodwin, & Hsiang, 2008), boards (Wiwart et al., 2009), closed boxes (De Coninck et al., 2011; Xu, Zhang, Shah, Ye,…”
The potential of automatic methods of classification to identify leaf diseases from multispectral images (2011, methodology): “Recently, several approaches have proved to be efficient in finding approximate solutions to the above problem, Szeliski et al. (2008). In our experiments we adopted an approach based on convex relaxation, in which the original problem is approximated by a convex one that can be solved efficiently.”
Plants Disease Identification and Classification Through Leaf Images: A Survey (2018, methodology): “Five such models considering various color components (HSI, HS, H, S, and I) are compared.”
Classification of grapefruit peel diseases using color texture feature analysis (2009, methodology): “Pydipati et al. (2006)[10] utilized the color co-occurrence method to extract various textural features from the color RGB images of citrus leaves.”
A review of neural networks in plant disease detection using hyperspectral data (2018, background): “In a similar study, Zhu et al. [54] tested BPNN along with SVM, ELM, LS-SVM, Partial Least Squares-Discrimination Analysis (PLS-DA), LDA, and Random Forest (RF) to process the hyperspectral images for presymptomatic detection and classiﬁcation of TMV in tobacco leaves.”