A computer vision approach based on endocarp features for the identification of olive cultivars

Martínez, S. Satorres; Gila, Diego Manuel Martínez; Beyaz, Abdullah; Ortega, Juan Gómez; García, Javier Gámez

doi:10.1016/j.compag.2018.09.017

Cited by 26 publications

(11 citation statements)

References 15 publications

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“…Therefore, olive stone characteristics tend to appear similar to olives belonging to the same variety and tend to differ in the opposite case. Martínez et al approached the problem of varietal identification by feature extraction from the analysis of endocarp images, and then using partial least square-discriminant classifier [33]. This is the first time that the research group studied the authenticity of Greek varieties of table olives, although similar research has been conducted in different countries and varieties by other authors who combined imaging and chemometrics [19,20,[30][31][32]34,35].…”

Section: Discussionmentioning

confidence: 99%

“…Several advanced analytical techniques have been used for the study of authentication of table olives, such as high-performance liquid chromatography (HPLC) [23,28], ultra-highperformance liquid chromatography-quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) [25], gas chromatography-mass spectrometry (GC-MS) [26] and nuclear magnetic resonance spectroscopy (NMR) [21]. Chemometrics is an important science which has been extensively used in food science and authenticity studies to facilitate interpretation of huge load of data, and it provides an easy way to visualize the samples [19,20,[29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

“…Best results were achieved with the last method, the bleach solution. Applying a 5% bleach solution for one hour, there were a complete absence of biological material and no damage appears in the endocarp texture [33]. Bleach solution was also used by Beyaz et al for the cleaning process of olive stones [20].…”

Section: Introductionmentioning

confidence: 99%

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Authentication and Chemometric Discrimination of Six Greek PDO Table Olive Varieties through Morphological Characteristics of Their Stones

Agriopoulou

Tarapoulouzi

Boat

et al. 2021

Foods

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Table olives, the number one consumed fermented food in Europe, are widely consumed as they contain many valuable ingredients for health. It is also a food which may be the subject of adulteration, as many different olive varieties with different geographical origin, exist all over the word. In the present study, the image analysis of stones of six main Greek protected designation of origin (PDO) table olive varieties was performed for the control of their authentication and discrimination, with cv. Prasines Chalkidikis, cv. Kalamata Olive, cv. Konservolia Stylidas, cv. Konservolia Amfissis, cv. Throuba Thassos and cv. Throuba Chios being the studied olive varieties. Orthogonal partial least square discriminant analysis (OPLS-DA) was used for discrimination and classification of the six Greek table olive varieties. With a 98.33% of varietal discrimination, the OPLS-DA model proved to be an efficient tool to authentify table olive varieties from their morphological characteristics.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Authentication and Chemometric Discrimination of Six Greek PDO Table Olive Varieties through Morphological Characteristics of Their Stones

Agriopoulou

Tarapoulouzi

Boat

et al. 2021

Foods

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“…According to research carried out in recent years, other identification techniques have been observed, using artificial vision. These have encouraging hit rates, as suggested by the authors [8,30,39]. On the other hand, these techniques always use the tree's fruit (olives and seeds) to perform the classification.…”

Section: Introductionmentioning

confidence: 88%

Machine Learning to Identify Olive-Tree Cultivars

Mendes

Lima

Costa

et al. 2022

Communications in Computer and Information Science

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The identification of olive-tree cultivars is a lengthy and expensive process, therefore, the proposed work presents a new strategy for identifying different cultivars of olive trees using their leaf and machine learning algorithms. In this initial case, four autochthonous cultivars of the Trás-os-Montes region in Portugal are identified (Cobrançosa, Madural, Negrinha e Verdeal). With the use of this type of algorithm, it is expected to replace the previous techniques, saving time and resources for farmers. Three different machine learning algorithms (Decision Tree, SVM, Random Forest) were also compared and the results show an overall accuracy rate of the best algorithm (Random Forest) of approximately 93%.

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“…The shape and size features [ 23 , 24 ] are the basic features of the image. These features will not be affected by scaling, rotation, and translation, and they have been widely used in computer vision recognition systems.…”

Section: Methodsmentioning

confidence: 99%

Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion

Zhu

Zhang

et al. 2021

Sensors

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The existing classification methods for Panax notoginseng taproots suffer from low accuracy, low efficiency, and poor stability. In this study, a classification model based on image feature fusion is established for Panax notoginseng taproots. The images of Panax notoginseng taproots collected in the experiment are preprocessed by Gaussian filtering, binarization, and morphological methods. Then, a total of 40 features are extracted, including size and shape features, HSV and RGB color features, and texture features. Through BP neural network, extreme learning machine (ELM), and support vector machine (SVM) models, the importance of color, texture, and fusion features for the classification of the main roots of Panax notoginseng is verified. Among the three models, the SVM model performs the best, achieving an accuracy of 92.037% on the prediction set. Next, iterative retaining information variables (IRIVs), variable iterative space shrinkage approach (VISSA), and stepwise regression analysis (SRA) are used to reduce the dimension of all the features. Finally, a traditional machine learning SVM model based on feature selection and a deep learning model based on semantic segmentation are established. With the model size of only 125 kb and the training time of 3.4 s, the IRIV-SVM model achieves an accuracy of 95.370% on the test set, so IRIV-SVM is selected as the main root classification model for Panax notoginseng. After being optimized by the gray wolf optimizer, the IRIV-GWO-SVM model achieves the highest classification accuracy of 98.704% on the test set. The study results of this paper provide a basis for developing online classification methods of Panax notoginseng with different grades in actual production.

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A computer vision approach based on endocarp features for the identification of olive cultivars

Cited by 26 publications

References 15 publications

Authentication and Chemometric Discrimination of Six Greek PDO Table Olive Varieties through Morphological Characteristics of Their Stones

Authentication and Chemometric Discrimination of Six Greek PDO Table Olive Varieties through Morphological Characteristics of Their Stones

Machine Learning to Identify Olive-Tree Cultivars

Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion

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