Disease Detection and Diagnosis on Plant using Image Processing A Review

Khairnar, Khushal; Dagade, Rahul

doi:10.5120/18973-0445

Cited by 34 publications

(16 citation statements)

References 5 publications

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“…Due to the fast technological development of varied completely different satellite sensors, a large volume of high-resolution image information sets will currently be no inheritable 8 . A way to with efficiency represent and acknowledge the scenes from such high-resolution image information has become an important task.…”

Section: Fig 4: Powdery Mildewmentioning

confidence: 99%

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2019

IJPSR

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Agriculture forms a vital part of India"s economy. More than 50% of India"s population is dependent on agriculture for their livelihood. In India, many crops are cultivated, out of which wheat is one of the most important food grains that this country cultivates and exports. Thus it can be seen that wheat forms a major part of the Indian agricultural system and India"s economy. Hence, the maintenance of the steady production of the above-stated crop is very important. The main idea of this project is to provide a novel approach based on a deformable model is proposed to handle the segmentation of the system for detecting crop diseases. The existing system is based on the segmentation method simple linear iterative clustering to detect disease in plant leaves. It also shows visual attributes such as color, gradient, texture, and shape to describe the features of leaves. The proposed system will define the cropped image of a wheat plant through image processing and feature extraction algorithms. The RGB color space is converted so that the color information contained in the images can be used effectively to differentiate image and segmentation usage of the k-means algorithm has been suggested.

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Section: Fig 4: Powdery Mildewmentioning

confidence: 99%

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2019

IJPSR

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“…Deep forward neural network [11] is used as a classifier which helps to recognizing cotton leaf spot diseases. Khairkar et.al [12] developed detection of disease and diagnosis on plant using image processing. Researcher proposed a method for detecting and diagnosis the diseases.…”

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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“…LITERATURE REVIEW Disease detection and diagnosis of the plant using image processing explained by Kushal Khairkar et al [6]. Color and texture and shape features are extracted through color space, color histogram, gray level co-occurrence matrix, Gabor filter and canny, Sobel edge detector.…”

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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Disease Detection and Diagnosis on Plant using Image Processing A Review

Cited by 34 publications

References 5 publications

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A Survey on Disease Detection in Plant using Image Processing Techniques

Disease Detection in Plant using Artificial Neural Network

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