Computer vision technique to classify dates based on hardness

Manickavasagan, Annamalai; Al-Shekaili, Naeema H.; Al-Mezeini, Nawal K.; Rahman, Mohammad Shafiur; Guizani, Negib

doi:10.24200/jams.vol22iss1pp36-41

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…According to the results, the accuracy of the model was 99%. Manickavasagan et al (2018) classified palm varieties grown in various regions of Saudi Arabia based on their hardness. Instead of the traditional expensive and time-consuming methods used to determine the quality of dried fruits, they developed an artificial neural network and linear discriminant analysis methods by subtracting histogram and tissue properties from monochrome images of 1800 samples to classify dried fruits as soft, semi-hard, and hard.…”

Section: Academicpres Notulae Botanicae Horti Cluj-napoca Agrobotanicimentioning

confidence: 99%

Classification of hazelnut cultivars: comparison of DL4J and ensemble learning algorithms

Koç

Gerdan

Eminoğlu

et al. 2020

Not Bot Horti Agrobo

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Classification of hazelnuts is one of the values adding processes that increase the marketability and profitability of its production. While traditional classification methods are used commonly, machine learning and deep learning can be implemented to enhance the hazelnut classification processes. This paper presents the results of a comparative study of machine learning frameworks to classify hazelnut (Corylus avellana L.) cultivars (‘Sivri’, ‘Kara’, ‘Tombul’) using DL4J and ensemble learning algorithms. For each cultivar, 50 samples were used for evaluations. Maximum length, width, compression strength, and weight of hazelnuts were measured using a caliper and a force transducer. Gradient boosting machine (Boosting), random forest (Bagging), and DL4J feedforward (Deep Learning) algorithms were applied in traditional machine learning algorithms. The data set was partitioned into a 10-fold-cross validation method. The classifier performance criteria of accuracy (%), error percentage (%), F-Measure, Cohen’s Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values are provided in the results section. The results showed classification accuracies of 94% for Gradient Boosting, 100% for Random Forest, and 94% for DL4J Feedforward algorithms.

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Section: Academicpres Notulae Botanicae Horti Cluj-napoca Agrobotanicimentioning

confidence: 99%

Classification of hazelnut cultivars: comparison of DL4J and ensemble learning algorithms

Koç

Gerdan

Eminoğlu

et al. 2020

Not Bot Horti Agrobo

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“…Many previous works implemented multiple artificial intelligence techniques to automate datesclassificationusingmultipletypesoffeatures.In2012,15featuresareusedtoautomatethe classificationofsevendifferentcategoriesofdates (Haidar,Dong&Mavridis,2012).Inthissystem, Nearest Neighbor, Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) classifiersaretestedforcomparisonpurposes.DateclassificationusingANNstudiedin (Alrajeh &Alzohairy,2012)achievedamaximumclassificationaccuracyof91.1%.In2014,texture,color andshapefeaturesareextractedfromthedatesimages (Muhammad,2014).Inthiswork,Fisher discrimination Ratio (FDR) is used for dimensionality reduction of features and Support Vector Machine(SVM)wasselectedforclassification.Anautomaticdateclassifierisalsodevelopedin (Manickavasagan et al, 2017). This system uses histogram and texture features and implements LDAandANNclassifiers.In2018,anautomatedsystemthatidentifiesdifferentdatefruitmaturity statusandclassifiestheircategoriesisdeveloped.Thissystemextractscolor,sizeandskintexture featuresfromimages,countsthenumberofdates,classifythemintodifferentclassesandidentify thedefects (Najeeb&Safar,2018).…”

Section: Introductionmentioning

confidence: 99%

Hardware/Software Co-Design of a Vision System for Automatic Classification of Date Fruits

Khriji

Ammari

Awadalla

2020

International Journal of Embedded and Real-Time Communication Systems

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This paper proposes a hardware/software (HW/SW) co-design of an automatic classification system of Khalas, Khunaizi, Fardh, Qash, Naghal, and Maan dates fruit varieties in Oman. Three artificial intelligence (AI) techniques are used for qualitative comparisons: artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN). The accuracy performance of all AI classifiers is characterized for multiple color, shape, size, and texture feature combinations and for different critical parameter settings of the classifiers. In total, 600 date samples (100 dates/variety) are selected and imaged each sample individually. The system starts with preprocessing and segmentation of the colored input images. A total of 19 features are extracted from each image for use in classification models. The ANN classifier is shown to outperform all other classifiers. 97.26% highest classification accuracy is achieved using a combination of 15 color and shape-size features.

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“…For date's classification relying on hardness, a system equipped with a monochrome camera was presented in 2016. This study used histogram and texture features in their system and LDA and ANN were implemented as classifiers [14][15][16]. In 2018, an automated system that identifies different date fruit maturity status and classifies their categories is developed.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligent Techniques for Palm Date Varieties Classification

Khriji¹,

Ammari²,

Awadalla³

2020

IJACSA

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The demand on high quality palm dates is increasing due to its energy value and nutrient content, which are of great importance in human diet. To meet consumer and market standards with large-scale production, in Oman as among the top date producer, an inline classification system is of great importance. This paper addresses the potentiality of using Machine-Learning (ML) techniques in classifying automatically, without any physical measurement, the six most popular date fruit varieties in Oman. The effect of color, shape, size, and texture features and the critical parameters of the classifiers on the classification efficiency has been endeavored. Three different ML techniques have been used for automatic classification and qualitative comparison: (i) Artificial Neural Networks (ANN), (ii) Support Vector Machine (SVM), and (iii) K-Nearest Neighbor (KNN). Based on the merge of color, shape and size features contributes to achieve the highest accuracy. Experimental results show that the ANN classifier outperforms both SVM and KNN with the highest classification accuracy of 99.2%. This developed vision system in this paper can be successfully integrated in the packaging date factories.

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Computer vision technique to classify dates based on hardness

Cited by 6 publications

References 17 publications

Classification of hazelnut cultivars: comparison of DL4J and ensemble learning algorithms

Classification of hazelnut cultivars: comparison of DL4J and ensemble learning algorithms

Hardware/Software Co-Design of a Vision System for Automatic Classification of Date Fruits

Artificial Intelligent Techniques for Palm Date Varieties Classification

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