Unhealthy region of citrus leaf detection using image processing techniques

Gavhale, Kiran R.; Gawande, Ujwalla; Hajari, Kamal

doi:10.1109/i2ct.2014.7092035

Cited by 119 publications

(34 citation statements)

References 4 publications

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“…Researcher develops a method for recognize and classify plant leaf diseases. Texture feature is extracted using gray level cooccurrence matrix (GLCM) [20] for image segmentation techniques. NN is used as a classifier.…”

Section: Literature Review Kulkarni Et Al {5mentioning

confidence: 99%

A Survey on Disease Detection in Plant using Image Processing Techniques

Gupta¹

2019

IJRASET

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We are familiar with this fact that there are major production and economic losses due to the disease in plants; a disease also degrades the quality of Agricultural Products as well as the quantity of production. Now a day's farmers are facing many problems, they are always confused to use the different type of disease detection techniques. The paper shows various steps of image processing which covers Image acquisition followed by pre-processing then segmentation along with feature extraction and then classification for identification and discovering various plant diseases in the leaf's of the plants.

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Section: Literature Review Kulkarni Et Al {5mentioning

confidence: 99%

A Survey on Disease Detection in Plant using Image Processing Techniques

Gupta¹

2019

IJRASET

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“…This paper used in image processing in agricultural demands for the following purpose firstly to recognize diseased fruit, stem,leaf and second step quantify affected area by disease third to discover the shape of the affected area and the last is to determine size of fruits. MS Kiran et al [11] discussed unhealthy region of citrus leaf identification using image processing technique. Texture feature extracting using statistical GLCM and color feature by means of mean values.…”

Section: IImentioning

confidence: 99%

Disease Detection in Plant using Artificial Neural Network

Gupta¹

2019

IJRASET

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Disease means that affect the quality and quantity of the plant. This paper describes an image processing technique used to identify citrus plant malady based on the healthy and diseased parts of plants. The purpose of this work is to recognize citrus plant disease detection. It includes several steps viz. first image acquisition, second image pre-processing, third k-means based segmentation, fourth feature extraction and last classification using a neural network.

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“…Further, image segmentation is performed using the k-means clustering algorithm to identify the region of interest, and feature extraction. Furthermore, the SVM algorithm is employed to develop the classifier to detect the unhealthy region of the Citrus leaf [12].…”

Section: Related Workmentioning

confidence: 99%

Plant Disease Detection System using Bag of Visual Words

Singh¹,

Leavline²,

Abirami³

et al. 2018

IJITCS

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Plants are important to human life since plants provide the food, shelter, rain, building material, medicine, fuel such as coal, wood, etc. Therefore, planting, growing, and protecting the plants is essential for sustainable development of any nation. The plant disease can affect the growth of the plats that is caused by pathogens, living microorganisms, bacteria, fungi, nematodes, viruses, and living agents. Hence, identifying the plant disease is very essential to protect the plants in the early stage. Moreover, the plant diseases are identified from the symptoms that appear in stem, fruit, leaf, flower, root, etc. The common symptom of the plant disease can be predicted from the appearance of leaf since the appearance of leaves highly depends on the healthiness of the plant. Therefore, this paper presents a system to identify the lesion leaf from the plants in order to detect the disease occurred in the plant. This system is developed using the bag of visual words model. Moreover, the real time images are collected for various plants and tested with this system and the system produces better results for the given set of images.

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Unhealthy region of citrus leaf detection using image processing techniques

Cited by 119 publications

References 4 publications

A Survey on Disease Detection in Plant using Image Processing Techniques

A Survey on Disease Detection in Plant using Image Processing Techniques

Disease Detection in Plant using Artificial Neural Network

Plant Disease Detection System using Bag of Visual Words

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