Remotely sensed image thresholding using OTSU &amp; differential evolution approach

Sehgal, Smriti; Kumar, Sushil; Bindu, M. Hima

doi:10.1109/confluence.2017.7943138

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…The second stage in this research is the segmentation of tomato leaf images. Segmentation was carried out using the Otsu thresholding method [21]. The first step in this segmentation process is to make a histogram.…”

Section: Segmentationmentioning

confidence: 99%

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Texture-Based Feature Extraction Using Gabor Filters to Detect Diseases of Tomato Leaves

Wiharto¹,

Nashrullah²,

Suryani³

et al. 2021

RIA

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The disease in tomato plants, especially on tomato leaves will have an impact on the quality and quantity of tomatoes produced. Handling disease on tomato leaves that must be done is to detect the type of disease as early as possible, then determine the treatment that must be done. Detection of its types of tomato plant diseases requires sufficient knowledge and experience. The problem is that many beginner farmers in growing tomatoes do not have much knowledge, so they have failed in growing tomatoes. Based on these cases, this study proposes a model for the early detection of disease in tomato leaves based on image processing. The research method used is divided into 5 stages, namely preprocessing, segmentation, feature extraction, classification, and performance evaluation. The feature extraction stage used is texture-based with Gabor filters and color-based filters. The final decision is determined by the Support Vector Machine (SVM) classification algorithm with the Radial Basis Function (RBF) kernel. The test results of the tomato leaf disease detection system produced an average performance parameter of 98.83% specificity, 90.37% sensitivity, 90.34% F1-score, 90.37% accuracy, and 94.60% area under the curve (AUC). Referring to the resulting of the AUC performance, the tomato leaf disease detection system is in the very good category.

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Section: Segmentationmentioning

confidence: 99%

“…The Rayleigh estimator can also be calculated directly using the Gaussian estimator if it has been obtained previously using Eq. (21).…”

Section: Feature Extraction Texture-basedmentioning

confidence: 99%

Texture-Based Feature Extraction Using Gabor Filters to Detect Diseases of Tomato Leaves

Wiharto¹,

Nashrullah²,

Suryani³

et al. 2021

RIA

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“…In general, using a binarization threshold appropriate reflecting the limits of high and low contrast in the image. In the proposed system, Otsu method [7] is used to convert the input Arabic image into binary. In addition, Median filter is used to remove unwanted pixels from the binary image.…”

Section: Image Binarizationmentioning

confidence: 99%

Header-Words Based for Printed Arabic Document Images Retrieval System

2017

ijs

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Printed Arabic document image retrieval is a very important and needed system for many companies, governments and various users. In this paper, a printed Arabic document images retrieval system based on spotting the header words of official Arabic documents is proposed. The proposed system uses an efficient segmentation, preprocessing methods and an accurate proposed feature extraction method in order to prepare the document for classification process. Besides that, Support Vector Machine (SVM) is used for classification. The experiments show the system achieved best results of accuracy that is 96.8% by using polynomial kernel of SVM classifier.

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