Fruit-Based Tomato Grading System Using Features Fusion and Support Vector Machine

Semary, Noura A.; Tharwat, Alaa; Elhariri, Esraa; Hassanien, Aboul Ella

doi:10.1007/978-3-319-11310-4_35

Cited by 53 publications

(21 citation statements)

References 13 publications

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“…Different brands of sesame oil are classified using Support Vector Machine-Multiclass Forward Feature Selection (SVM-MFFS), Successive Projection Analysis (SPA) and Uninformative Variable Elimination (UVE) algorithms in [58]. Color moments, GLCM, and Wavelets energy and entropy is used for color and texture feature extraction in [59] for tomato grading in two classes. PCA and SVM are used for dimensionality reduction and as a classifier respectively.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Machine Vision based Fruit Classification and Grading - A Review

Naik¹,

Patel²

2017

IJCA

105

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Machine Vision based Fruit Classification and Grading - A Review

Naik¹,

Patel²

2017

IJCA

105

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“…The shape and texture features could be extracted by Scale Invariant Feature Transform (SIFT) [9] or Local Binary Patterns (LBP) [10] whereas the color features can be extracted by Color Moments, Color Histogram, etc. [11].…”

Section: Feature Extraction Methodsmentioning

confidence: 95%

Plant Identification: Two Dimensional-Based Vs. One Dimensional-Based Feature Extraction Methods

Gaber

Tharwat

Snášel

et al. 2015

Advances in Intelligent Systems and Computing

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In this paper, a plant identification approach using 2D digital leaves images is proposed. The approach made use of two methods of features extraction (one-dimensional (1D) and two-dimensional (2D) techniques) and the Bagging classifier. For the 1D-based method, PCA and LDA techniques were applied, while 2D-PCA and 2D-LDA algorithms were used for the 2D-based method. To classify the extracted features in both methods, the Bagging classifier, with the decision tree as a weak learner, was used. The proposed approach, with its four feature extraction techniques, was tested using Flavia dataset which consists of 1907 colored leaves images. The experimental results showed that the accuracy and the performance of our approach, with the 2D-PCA and 2D-LDA, was much better than using the PCA and LDA. Furthermore, it was proven that the 2D-LDA-based method gave the best plant identification accuracy and increasing the weak learners of the Bagging classifier leaded to a better accuracy. Also, a comparison with the most related work showed that our approach achieved better accuracy under the same dataset and same experimental setup.

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“…On the other hand, there are adequate works for fruit recognition by their many characteristics. Like on their color and texture [2], grading system [3], color characterization [4], classification [5], detection of defective apple [6] and many works can be done by using machine-vision. Apart from this some works of food done by using machine-vision applications in recognition and aquatic food [7,8], deep convolutional network with pre-training and fine-tuning [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine vision based local fish recognition

et al. 2019

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Bangladesh has its own abundance of water resources which helps to identify its customs that are related to freshwater fish. Due to environmental issues along with some other reasons, the amount of water resources of Bangladesh is reducing day-by-day. Consequently, many of our territorial freshwater fishes are getting abolished. Thus, the new generation people of Bangladesh lacks the knowledge of local freshwater fish. For this problem, a solution has been found with the collaboration of vision-based technology. As a solution, a machine-vision based local freshwater fish recognition system is presented that can be proceed with an image of fish captured with a mobile or handheld device and recognize the fish in order to introduce the fish. To demonstrate the utility of the proposed expert system, several experiments are performed. At first, a set of fourteen features, which consists of four types of features, are presented. Then the color image has been converted into gray-scale image and the gray-scale histogram is formed. Image segmentation takes place using histogram-based method and then the features are extracted. PCA is used for decreasing the feature numbers. Three classifiers are used for recognizing fish, where SVM gives the highest accuracy showing a value of 94.2%.Keywords Bangladeshi local fish · Machine vision · Thresholding · Principle component analysis · Support vector machine · Classifiers · k-nearest neighbor * Israt Sharmin, ima.sharmin23@gmail.com; Nuzhat Farzana Islam, nuzhat15-5316@diu.edu.bd; Israt Jahan, israt15-5461@diu.edu.bd; Tasnem Ahmed Joye, ahmed15-

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Fruit-Based Tomato Grading System Using Features Fusion and Support Vector Machine

Cited by 53 publications

References 13 publications

Machine Vision based Fruit Classification and Grading - A Review

Machine Vision based Fruit Classification and Grading - A Review

Plant Identification: Two Dimensional-Based Vs. One Dimensional-Based Feature Extraction Methods

Machine vision based local fish recognition

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