Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimation

Shrivastava, Sourabh; Singh, Satish Kumar; Hooda, D. S.

doi:10.1007/s11042-014-2239-0

Cited by 54 publications

(26 citation statements)

References 9 publications

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“…As we know that each input image consists three channels in which one channel is luminance denoted by Y and two Chrominance channels Icb and Icr. These three Y, Icb and Icr are extracted by following equation [58]. One Y, Cb, Cr channel are extracted from R, G, B image, and then object is separated from background with following steps.…”

Section: Proposed Methods For Bachground Subtractionmentioning

confidence: 99%

A Framework with OTSU’S Thresholding Method for Fruits and Vegetables Image Segmentation

Tripathi¹,

Maktedar²

2018

IJCA

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An accurate technique for segmentation of fruits and vegetables image is vital and major challenges in computer vision. Various segmentation techniques are available in digital image processing. In this paper, we introduce a framework for fruits and vegetables background subtraction employing Otsu"s algorithm. This method is widely used in various image segmentation applications. The Otsu"s method is useful in subtraction of background under the partial effect of occlusion, cropping, noisy and blurred images. Our proposed method was experimented by employing fruit and vegetable images acquired locally. Our experimental results confirm that, Otsu"s threshold based method is able to extract fruit and vegetable objects with good accuracy.

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Section: Proposed Methods For Bachground Subtractionmentioning

confidence: 99%

A Framework with OTSU’S Thresholding Method for Fruits and Vegetables Image Segmentation

Tripathi¹,

Maktedar²

2018

IJCA

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“…The proposed algorithm uses the HSV color space for discriminating crop, weeds and soil. [5] Foliar disease in soybean plants and their severity is discussed in [6]. Primary pitfalls in plant disease detection such as Lacking Lightning/resolution, Complex /Busy Background, Images with Shadow which still remains challenging for plant pathologists are discussed in [7].…”

Section: Aliterature Reviewmentioning

confidence: 99%

“…Next the ROI, diseased chunk from our input image is extracted and highlighted. [6] Calculates disease severity in soybean. Also [7] and [8] discusses image processing techniques and calculates severity of disease for cotton plants.…”

Section: Disease Detectionmentioning

confidence: 99%

Maize Leaf Disease Severity Analysis using Integrated Color Filtering and Threshold Masking

Priya¹,

Tamilselvi²

2019

IJRTE

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As the color is a dominant factor for isolation of the diseased part of corn maize, the detachment of disease affected parts in maize plant leaf is achieved using integrated Color Filtering followed by a threshold masking. The Particular HSV from a color image of maize plant is extracted .Major steps involved here is to initially convert an RGB image of disease affected maize plant to HSV and second is to apply a threshold mask to filter out the green color of healthy maize plant and detach the brown and yellow diseased area thereof. This method is applied and tested with around 30 maize leaves, and the results found that the proposed methodology performs well with overlapped healthy maize leaf compared to K-means Clustering algorithm. False Positive is produced in K-means method and this Proposed system as integrates with Color Filtering and thresholding works well with overlapped images so that it increases True negative as the Accuracy of the proposed method increases. This proposed methodology identifies well with perfect maize leaf images and misclassifications occur only with images with dark shadows, light illuminations and sanded background.

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“…Some of the images are have weakness such as blurred or poor contrast. To make the quality, the images to be enhanced by mapping the pixel of lower threshold value and upper threshold value to new pixel value [15]. To improve the visual impact pixel brightness can be improved for further processing and analysis task.…”

Section: Analysis Of Image To Segment the Infected Leavesmentioning

confidence: 99%

Automatic Rice Leaf Disease Segmentation Using Image Processing Techniques

Archana¹,

Sahayadhas²

2018

IJET

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Agriculture productivity mainly depends on Indian economy. Hence, Disease prediction plays a important role in agriculture field. In image analyzing the symptoms is an essential part for feature extraction and classification. However, some of the challenges are still lacking to predict the disease. To meet those challenges, the proposed algorithm focuses on a specific problem to predict the disease from early symptoms. Bacterial Leaf Blight and Brown Spot are a major bacterial and fungal disease respectively in rice (Oryza sativa) crops, it causes yield loss and reduce the grains quality. This research work focused on automatic detection method for image segmentation on rice leaves under wide range of environmental condition for further analysis. Various hybrid techniques for image segmentation and classification algorithms were analyzed and an automatic detection method has been proposed for identifying the specified diseases in rice leaves under different environmental condition.

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Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimation

Cited by 54 publications

References 9 publications

A Framework with OTSU’S Thresholding Method for Fruits and Vegetables Image Segmentation

A Framework with OTSU’S Thresholding Method for Fruits and Vegetables Image Segmentation

Maize Leaf Disease Severity Analysis using Integrated Color Filtering and Threshold Masking

Automatic Rice Leaf Disease Segmentation Using Image Processing Techniques

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