Identification of citrus disease using color texture features and discriminant analysis

Pydipati, R.; Burks, Thomas F.; Lee, W. S.

doi:10.1016/j.compag.2006.01.004

Cited by 307 publications

(132 citation statements)

References 7 publications

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“…Therefore, more sophisticated methods (genetic algorithm, decision tree, and some more clustering) can be used for the concerned problem.  Environmental conditions are another kind of constraint for collecting the images in controlled laboratory conditions (Pydipati et al 2006 [25] , Oberti et al 2014 [21] , etc.) different capturing angles (Xu et al 2011 [34] ) and particular size while capturing, high resolution capturing devices and precise length required.…”

Section: Discussion and Summarizationmentioning

confidence: 99%

“…Feature Analysis [Pydipati et al 2006] [25] presented an approach for identifying a citrus disease by using machine vision techniques. In this study, the CCD camera and MV Tools software used for collecting disease infected citrus plant images under an artificial light generating setup using calibration gray card and controlled laboratory conditions.…”

Section: Plant Recognition and Classification Techniquesmentioning

confidence: 99%

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Recent Studies of Image and Soft Computing Techniques for Plant Disease Recognition and Classification

Sabrol¹,

Kumar²

2015

IJCA

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The plant diseases are a normal part of nature but can cause significant economic, social and ecologic loss globally. It's difficult to monitor continuously plant health and detection of diseases. The paper presents a survey of recent studies on the area of plant disease recognition and classification from digital images using image processing and soft computing techniques. The main aim of the paper is to focus on the area of plant pathology recognition and classification only. The paper is omitting the disease severity quantification. Although the paper, considering the images of symptoms presents on plant leaves and stems only for limiting the survey. Each considered paper in the review, representing the comprehensive details of the technical implementation of an algorithm. The algorithm begins with digital image acquisition of infected and non-infected plants; perform image preprocessing, differentiate disease infected region from a non-infected region using segmentation, extract features from segmented images for recognition and classification. This survey expected to useful for researchers from plant pathology and pattern recognition field.

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Section: Discussion and Summarizationmentioning

confidence: 99%

Section: Plant Recognition and Classification Techniquesmentioning

confidence: 99%

Recent Studies of Image and Soft Computing Techniques for Plant Disease Recognition and Classification

Sabrol¹,

Kumar²

2015

IJCA

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“…Also color properties have been widely used for apple (Malus × domestica) quality evaluation, and mostly for defect detection (Leemans et al, 1998;Leemans & Destain, 2004) and tomato (Solanum lycopersicum) quality (Sarkar & Wolfe, 1985). Pydipati et al (2006) used a machine vision technology for evaluating the quality of grains and fruits. They distinguished citrus diseases using the structural characte-ristics of leaf color.…”

Section: Introductionmentioning

confidence: 99%

Identification of bean varieties according to color features using artificial neural network

Nasirahmadi

Behroozi‐Khazaei

2013

Span. j. agric. res.

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A machine vision and a multilayer perceptron artificial neural network (MLP-ANN) were applied to identify bean varieties, based on color features. Ten varieties of beans, which were grown in Iran (Khomein1, KS21108, Khomein2, Sarab1, Khomein3, KS21409, Akhtar2, Sarab2, KS21205, and G11870) were collected. Six color features of the bean and six color features of the spots were extracted and used as input for MLP-ANN classifier. In this study, 1000 data sets were used, 70% for training, 15% for validating and 15% for testing. The results showed that the applied machine vision and neural network were able to classify bean varieties with 100% sensibility and specificity, except with Sarab1 with sensibilities of 100%, 73.3%, 60% for the training, validation and testing processes, respectively and KS21108 with specificities of 100%, 79% and 71%, respectively for the aforementioned processes. Considering total sensibilities of 100%, 97.33%, 96% and also specificities of 100%, 97.9% and 97.1% for training, validation and testing of beans, respectively, the ANN could be used as a effective tool for classification of bean varieties.Additional key words: classification; Phaseolus vulgaris; image processing; machine vision.* Corresponding author: a.nasirahmadi@gmail.com Received: 08-01-13. Accepted: 23-07-13.Abbreviations used: ANN (artificial neural network); HIS (hue, saturation, intensity); LM (learning algorithm); MLP (multi-layer perceptron); MSE (mean square error).

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“…Therefore, it is easy to differentiate between weeds and sugar beet. Intent to identify citrus disease, Pydipati et al (2006) had mentioned that colour texture features is a key features (Cubero et al, 2011).…”

Section: Leaf Featuresmentioning

confidence: 99%

Leaf Features Extraction and Recognition Approaches to Classify Plant

Jabal

Hamid

Shuib

et al. 2013

Journal of Computer Science

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Plant classification based on leaf identification is becoming a popular trend. Each leaf carries substantial information that can be used to identify and classify the origin or the type of plant. In medical perspective, images have been used by doctors to diagnose diseases and this method has been proven reliable for years. Using the same method as doctors, researchers try to simulate the same principle to recognise a plant using high quality leaf images and complex mathematical formulae for computers to decide the origin and type of plants. The experiments have yielded many success stories in the lab, but some approaches have failed miserably when tested in the real world. This happens because researchers may have ignored the facts that the real world sampling may not have the luxury and complacency as what they may have in the lab. What this study intends to deliver is the ideal case approach in plant classification and recognition that not only applicable in the real world, but also acceptable in the lab. The consequence from this study is to introducing more external factors for consideration when experimenting real world sampling for leaf recognition and classification does this.

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Identification of citrus disease using color texture features and discriminant analysis

Cited by 307 publications

References 7 publications

Recent Studies of Image and Soft Computing Techniques for Plant Disease Recognition and Classification

Recent Studies of Image and Soft Computing Techniques for Plant Disease Recognition and Classification

Identification of bean varieties according to color features using artificial neural network

Leaf Features Extraction and Recognition Approaches to Classify Plant

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