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

Camargo, Anyela; Smith, Jeremy S.

doi:10.1016/j.compag.2009.01.003

Cited by 263 publications

(110 citation statements)

References 13 publications

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“…Camargo [9] uses support vector machines to classify visual symptoms of cotton plant diseases, i.e. visual damages of the leaves.…”

Section: Existing Methodological Approachesmentioning

confidence: 99%

A Review of Image Processing Software Techniques for Early Detection of Plant Drought Stress

Kirongo¹

2016

IJCATR

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Water stress is one of the most important growth-limiting factors in crop production around the world, water in plants is required to permit vital processes such as nutrient uptake, photosynthesis, and respiration. Drought stress in plants causes major production losses in the agricultural industry worldwide. There is no sensor commercially available for real-time assessment of health conditions in beans. Currently, there are several methods to evaluate the effect of water stress on plants and commonly practiced method over the years for stress detection is to use information provided by remote sensing. Studies exist which determined the effect of water stress in plants grown under the different watering regime, while other studies explore the performance of the artificial neural network techniques to estimate plant yield using spectral vegetation indices. This review recognizes the need for developing a rapid cost-effective, and reliable health monitoring sensor that would facilitate advancements in agriculture

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“…Camargo [9] uses support vector machines to classify visual symptoms of cotton plant diseases, i.e. visual damages of the leaves.…”

Section: Existing Methodological Approachesmentioning

confidence: 99%

A Review of Image Processing Software Techniques for Early Detection of Plant Drought Stress

Kirongo¹

2016

IJCATR

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“…Volume 126 -No.1, September 2015 [Camargo et al 2009b] [6] proposed a method to recognize three cotton plant diseases i.e. southern green stink bug, bacterial angular and ascochyta blight.…”

Section: International Journal Of Computer Applications (0975 -8887)mentioning

confidence: 99%

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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“…Camargo at el. [17] in the paper titled -An imageprocessing based algorithm to automatically identify plant disease visual symptoms‖ described an image-processing based method that identifies the visual symptoms of plant diseases, from an analysis of coloured images. The processing algorithm starts by converting RGB image of the diseased plant or leaf, into the H, I3a and I3b colour transformations.…”

Section: Literature Surveymentioning

confidence: 99%

A Review on Efficient Identification of American Cotton Leaf Diseases through Training Set

Prashar¹,

Talwar²,

Kant³

2015

IJCA

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The cotton leaf disease detection is the process of detecting disease by analyzing their visual properties. The visual properties extraction process from the images is known as the feature extraction. The feature extraction process can be done using the various feature descriptors like SIFT, SURF or other most suitable candidate. The feature descriptors are then passed to the classifier for the evaluation of the feature. The classifier is the algorithm, which is used to classify the feature on the basis of its similarity with the training dataset. The training dataset is the collection of features previously extracted from the known objects (the leaves with specific disease in this case). The leaves with disease are classified on the basis of their similarity with the training dataset of disease samples previously described by the feature descriptors. In this paper, our aim is to solve the cotton disease detection problem using the image processing techniques automatically from the input image. The disease classification will primarily based upon the visibility of the disease on the cotton leaves, which further can be used for the identification using the classifier. The proposed model implementation would be done using the MATLAB simulator and the proposed model results would be obtained in the form of the accuracy, precision, recall, elapsed time and many other similar parameters.

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

Cited by 263 publications

References 13 publications

A Review of Image Processing Software Techniques for Early Detection of Plant Drought Stress

A Review of Image Processing Software Techniques for Early Detection of Plant Drought Stress

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

A Review on Efficient Identification of American Cotton Leaf Diseases through Training Set

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