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Journal ArticleDOI

Durian recognition based on multiple features and linear discriminant analysis

TL;DR: This work aims to contribute to a new representation method based on multiple features for effective durian recognition, and two features based on shape and texture is considered in this work.
Abstract: Many fruit recognition approaches today are designed to classify different type of fruits but there is little effort being done for content-based fruit recognition specifically focuses on durian species. Durian, known as the king of tropical fruits, have few similar characteristics between different species where the skin have almost the same colour from green to yellowish brown with just slightly different shape and pattern of thorns. Therefore, it is hard to differentiate them with the current methods. It would be valuable to have an automated content-based recognition framework that can automatically represent and recognise a durian species given a durian image as the input. Therefore, this work aims to contribute to a new representation method based on multiple features for effective durian recognition. Two features based on shape and texture is considered in this work. Simple shape signatures which include area, perimeter, and circularity are used to determine the shape of the fruit durian and its base while the texture of the fruit is constructed based on Local Binary Pattern. We extracted these features from 240 durian images and trained this proposed method using few classifiers. Based on 10-fold cross validation, it is found that Logistic Regression, Gaussian Naive Bayesian, and Linear Discriminant Analysis classifiers performed equally well with 100% achievement of accuracy, precision, recall, and F1-score. We further tested the proposed algorithm on larger dataset which consisted of 42337 fruit images (64 various categories). Experimental results based on larger and more general dataset have shown that the proposed multiple features trained on Linear Discriminant Analysis classifier able to achieve 72.38% accuracy, 73% precision, 72% recall, and 72% F1-score.
Citations
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Journal ArticleDOI
TL;DR: It was demonstrated that thermal imaging technique is useful in monitoring quality and safety inspection of food, especially in agriculture and food processing industries.
Abstract: Background Thermal imaging is a non-destructive technique which has emerged as a powerful analytical tool in the agriculture and food industries. Thermal imaging analysis converts the radiation pattern of a sample based on the temperature differences in analysis and diagnosis of the object. Scope and approach This review provides an insight on the thermal imaging principles, system components, as well as image processing and analysis. The combination of chemometrics with thermal imaging techniques has extensive use to determine the quality and safety of food products, as the analysis allows for the non-destructive and comparatively rapid testing of materials compared to conventional methods. In this review, potential applications of thermal imaging in food quality determination and safety inspection including postharvest quality, crop yield and estimation, pathogen detection, and monitoring temperature in food were discussed. Key findings and conclusions Attention to the thermal imaging device has greatly increased due to the promising alternative in the quality and safety inspection in food and agriculture. The challenges and future perspectives regarding thermal imaging techniques evaluated by chemometrics were also elucidated. This review demonstrated that thermal imaging technique is useful in monitoring quality and safety inspection of food, especially in agriculture and food processing industries.

21 citations

Journal ArticleDOI
TL;DR: In this paper, a review on the chemical composition, potential uses, health benefits, and emerging technologies for the quality evaluation of durian, with the goal of providing information for its exploitation.

19 citations

Journal ArticleDOI
TL;DR: The results showed that the fruit classification by using the extraction of Speeded up Robust Features (SURF) feature and SVM (Support Vector Machine) Classification method is quite maximal and accurate.
Abstract: Indonesia's various types of fruits can be met by the community. Many fruits that contain a source of vitamins are very beneficial to the body, or as an economic source for farmers. It's no wonder that many experts submit discoveries to increase the amount of productivity or just want to experiment with intelligent systems. Intelligent systems are specially designed machines in certain areas to adjust the capabilities made by the creators. This article provides the latest texture classification technique called Speeded up Robust Features (SURF) with the SVM (Support Vector Machine) method. In this concept, the representation of the image data is done by capturing features in the form of keys. SURF uses the determinant of the Hessian matrix to reach the point of interest in which descriptions and classifications are performed. This method delivers superior performance compared to existing methods in terms of processing time, accuracy, and durability. The results showed that the fruit classification by using the extraction of Speeded up Robust Features (SURF) feature and SVM (Support Vector Machine) Classification method is quite maximal and accurate. Result of 3 kinds of classification with SVM kernel function, SVM Gaussian with 72% accuracy, Polynomial SVM with 69.75% accuracy, and Linear SVM with 70.25% accuracy.

7 citations


Cites methods from "Durian recognition based on multipl..."

  • ...The SURF method is inspired by the SIFT (ScaleInvariant Feature Transform) method, which makes this method inspired by the SIFT method is when it comes to the Scale-space representation phase....

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  • ...SURF is the development of the Scale Invariant Feature Transform (SIFT) feature extraction algorithm [7]....

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  • ...SURF is an improved version of SIFT [3]....

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  • ...The processing of the SURF method is much faster than the SIFT (Scale Invariant Feature Transform) method....

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Journal ArticleDOI
TL;DR: In this article , infrared thermal images were used to distinguish different pineapple cultivars, i.e., MD2, Morris, and Josapine, which were subjected to different storage temperatures, and a relative humidity of 85% to 90%.
Abstract: The non-invasive ability of infrared thermal imaging has gained interest in various food classification and recognition tasks. In this work, infrared thermal imaging was used to distinguish different pineapple cultivars, i.e., MD2, Morris, and Josapine, which were subjected to different storage temperatures, i.e., 5, 10, and 25 °C and a relative humidity of 85% to 90%. A total of 14 features from the thermal images were obtained to determine the variation in terms of image parameters among the different pineapple cultivars. Principal component analysis was applied for feature reduction in order to prevent any effect of significant difference between the selected features. Several types of machine learning algorithms were compared, including linear discriminant analysis, quadratic discriminant analysis, support vector machine, k-nearest neighbour, decision tree, and naïve Bayes, to obtain the best performance for the classification of pineapple cultivars. The results showed that support vector machine achieved the best performance from the combination of optimal image parameters with the highest classification rate of 100%. The ability of infrared thermal imaging coupled with machine learning approaches can be potentially used to distinguish pineapple cultivars, which could enhance the grading and sorting processes of the fruit.

3 citations

Proceedings ArticleDOI
04 Aug 2021
TL;DR: In this article, an analytical review on various image classification and maturity detection techniques of multiple Fruits is presented, where feature extractions are done on the different datasets based on size, shape, color and texture.
Abstract: The objective of this paper is to review the classification of multiple fruits in different environments. The tropical fruits are grown mostly in South East Asia, Thailand and in Malaysia. Most of the fruits are usually recognized by its strong aroma and unique taste. These fruits are identified as the most precious fruit in India for the effective health nourishment benefits. The appearance of the fruits generally identifies the quality and ripeness of the fruits. The Quality of the fruit is also determined by the strength of color, texture, aroma and the maturity level of the fruit. This identification of the maturity levels when done manually leads to many flaws. Thus techniques like computer vision, machine learning are used for easy classification of the fruits in to various stages. A detailed review of the multiple fruit classification methods are done with both Machine Learning and Deep Learning Techniques and a comparison is made on the accuracy obtained by these techniques. The feature extractions are done on the different datasets based on size, shape, color and texture. In image processing techniques there are significant research areas to work on the image but classification of image is one of the most complex area. The classification of multiple fruit images are categorized in two main ways of Supervised and Unsupervised methods in Machine Learning Technique. Both the methods have their properties and functions. The main challenge is to obtain a proper and desired result from a noisy fruit image as compared to that of the normal image. Fruit maturity detection is divided into different stages depending on the type of detection method used. In this study we present an analytical review on various image classification and maturity detection techniques of Multiple Fruits. This review paper describes the description of the non-destructive techniques to estimate the quality of the different fruit and to improve the classification based on maturity level.

2 citations

References
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Journal ArticleDOI
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Abstract: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

37,861 citations


"Durian recognition based on multipl..." refers methods in this paper

  • ...SVM classifier is created by Corinna Cortes and Vladimir Vapik [30]....

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Journal ArticleDOI
TL;DR: Applied Logistic Regression, Third Edition provides an easily accessible introduction to the logistic regression model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
Abstract: \"A new edition of the definitive guide to logistic regression modeling for health science and other applicationsThis thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data. A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion. Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines\"--

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Book
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TL;DR: This paper presents a meta-analysis of the literature on comparative qualitative and quantitative approaches to quantitative qualitative research and concludes with a call for further research into these techniques.
Abstract: Brief Contents PART I THE FUNDAMENTALS Chapter 1 THE NATURE AND TOOLS OF RESEARCH PART II FOCUSING YOUR RESEARCH EFFORTS Chapter 2 THE PROBLEM: THE HEART OF THE RESEARCH PROCESS Chapter 3 REVIEW OF THE RELATED LITERATURE Chapter 4 PLANNING YOUR RESEARCH PROJECT Chapter 5 WRITING THE RESEARCH PROPOSAL PART III QUANTITATIVE RESEARCH Chapter 6 DESCRIPTIVE RESEARCH Chapter 7 EXPERIMENTAL, QUASI-EXPERIMENTAL, AND EX POST FACTO DESIGNS Chapter 8 ANALYZING QUANTITATIVE DATA PART IV QUALITATIVE RESEARCH Chapter 9 QUALITATIVE RESEARCH METHODS Chapter 10 HISTORICAL RESEARCH Chapter 11 ANALYZING QUALITATIVE DATA PART V MIXED-METHODS RESEARCH Chapter 12 MIXED-METHODS DESIGNS PART VI RESEARCH REPORTS Chapter 13 PLANNING AND PREPARING A FINAL RESEARCH REPORT APPENDICES Appendix A USING A SPREADSHEET: MICROSOFT EXCEL Appendix B USING SPSS

10,491 citations


"Durian recognition based on multipl..." refers background in this paper

  • ...According to Leedy and Ormrod [35], experimental study is done to investigate the possible influences that one factor or condition may have on another factor or condition....

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Posted Content
TL;DR: This paper abandon the normality assumption and instead use statistical methods for nonparametric density estimation for kernel estimation, which suggests that kernel estimation is a useful tool for learning Bayesian models.
Abstract: When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality assumption and instead use statistical methods for nonparametric density estimation. For a naive Bayesian classifier, we present experimental results on a variety of natural and artificial domains, comparing two methods of density estimation: assuming normality and modeling each conditional distribution with a single Gaussian; and using nonparametric kernel density estimation. We observe large reductions in error on several natural and artificial data sets, which suggests that kernel estimation is a useful tool for learning Bayesian models.

3,071 citations

Proceedings ArticleDOI
23 Aug 1999
TL;DR: In this article, a non-linear classification technique based on Fisher's discriminant is proposed and the main ingredient is the kernel trick which allows the efficient computation of Fisher discriminant in feature space.
Abstract: A non-linear classification technique based on Fisher's discriminant is proposed. The main ingredient is the kernel trick which allows the efficient computation of Fisher discriminant in feature space. The linear classification in feature space corresponds to a (powerful) non-linear decision function in input space. Large scale simulations demonstrate the competitiveness of our approach.

2,896 citations

Trending Questions (1)
How can image processing and artificial neural network be used to classify durian species?

The paper does not mention the use of image processing or artificial neural networks for durian species classification.