Durian cultivar recognition using discriminant function

Pensiri, Fuangfar; Visusak, Porawat

doi:10.1109/incit.2017.8257887

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Fruit recognition uses fruit image's content for analysis, identification, and classification in the agriculture domain. Some existing works such as recognition of various fruits [16][17][18][19][20][21] or even focusing on specific fruits such as olive [22], persimmon [23], almond [24], apple [25], papaya [26], and durian [27][28].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In regards to content-based recognition focusing on durian domain, we managed to find only two related works. Authors in [27] recognise two types of durian (Chanee and Monthong) by analysing the peak of the fruit's thorns and its geometric outlines. 40 Durian images are used for evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The feature descriptor will also need to be invariant to rotation and translation or illumination to increase the robustness of the proposed work. From the literature review and to our knowledge, there is very little exploration being done for content-based durian species recognition [27][28]. For durian species images, suitable feature representations will also need to be determined.…”

Section: Literature Reviewmentioning

confidence: 99%

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Durian Recognition Based on Multiple Features and Linear Discriminant Analysis

Mustaffa

Abdullah

et al. 2018

MJCS

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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 Naïve 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.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Durian Recognition Based on Multiple Features and Linear Discriminant Analysis

Mustaffa

Abdullah

et al. 2018

MJCS

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“…However, while more than 20 durian varieties are under cultivation, only a few important varieties, including Durio zibethinus Murr., cv. Monthong and Chanee are popular among durian growers and consumers ( Pensiri & Visutsak, 2017 ). The shapes of durian fruits are either round or oval, while their peel is thick.…”

Section: Introductionmentioning

confidence: 99%

Antioxidant and anti-inflammatory activities of durian (Durio zibethinusMurr.) pulp, seed and peel flour

Charoenphun

Klangbud

2022

PeerJ

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The unripe pulp, inner peel and seed of durian were used in this study. These are generally not considered edible and must be disposed of as waste. However, they are good sources of bioactive compounds. Flour extracts from the unripe pulp, inner peel, and seed of two durian (Durio zibethinus Murr.) varieties, namely, Monthong and Chanee, were analyzed chemically to determine their total phenolic content (TPC), antioxidant, and anti-inflammatory capacities. Chanee pulp (CPu) contained a higher TPC (5285.37 ± 517.65 mg GAE/g) than Monthong pulp (MPu), Monthong peel (MP), Monthong seed (MS), Chanee peel (CP) and Chanee seed (CS) (p = 0.0027, 0.0042, 0.0229, 0.0069 and 0.36), respectively. The antioxidant activity of each durian extract was determined against ABTS, nitric oxide, superoxide, hydroxyl, and metal ions. The results indicated that the pulp, inner peel and seed of these durian varieties had antioxidant capacities. Murine Raw 264.7 macrophages were used to determine the cytotoxicity of the flour extracts. The extract of CS flour had the lowest cytotoxicity followed by MP, CPu, CP, MPu and MS (p = 0.5926, 0.44, 0.3191, 0.1471 and 0.0014), respectively. The anti-inflammatory activity was tested by anti-nitric oxide (NO) production in lipopolysaccharide (LPS) stimulated cells by co-treating the Raw 264.7 cells with each durian flour extract and LPS. The extract of MP flour had the lowest IC50 against NO production, indicating the highest anti-NO production activity followed by CS, CPu, MPu, CP and MS (p = 0.7473, 0.0104, < 0.0001, 0.0002 and < 0.0001, respectively). The information obtained in this study is useful for researchers to explore more durian varieties in Southeast Asia to find bioactive compounds that might be novel nutraceuticals for antioxidant, anti-inflammation and therapeutic functional food.

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“…Although the increasing popularity of durian in world market that caused the price had risen up to 20 times over in four years [3], there is no repository to keep the technical aspects of durian production in the right place. The knowledge of durian production is still based on experience from generation to generation, especially the knowledge of durian pests and diseases control [4], [5]. This paper presents the ontology knowledge based for durian pests and diseases retrieval system.…”

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confidence: 99%

Ontology-Based Semantic Retrieval for Durian Pests and Diseases Control System

Visutsak¹

2021

IJMLC

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In Southeast Asia, durian is affectionately called the king of fruit. Durian is the most popular crop planted in eastern and southern of Thailand. The total crop is around 600,000 tons per year; among this, 500,000 tons of the total production were exported worldwide. In Thailand, the knowledge of durian production is based on experience from generation to generation, especially the knowledge of durian pests and diseases control. This paper presents the ontology knowledge based for durian pests and diseases retrieval system. The major contributions of the system consist of 1) the stored knowledge of durian pests and diseases and 2) the diagnosis of durian diseases and the suggestions for the treatments. The ontology knowledge consists of 8 main classes: 1) diseases, 2) pests, 3) cultivars, 4) symptoms of bunch, 5) leaf area symptoms, 6) symptoms of the branches and trunk, 7) symptoms of fruit, and 8) symptoms of root and growth. The experimental results yielded 100% of precision, 88.33% of recall, and 93.8% of overall performance.

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Durian cultivar recognition using discriminant function

Cited by 6 publications

References 12 publications

Durian Recognition Based on Multiple Features and Linear Discriminant Analysis

Durian Recognition Based on Multiple Features and Linear Discriminant Analysis

Antioxidant and anti-inflammatory activities of durian (Durio zibethinusMurr.) pulp, seed and peel flour

Ontology-Based Semantic Retrieval for Durian Pests and Diseases Control System

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