The use of seed texture features for discriminating different cultivars of stored apples

Ropelewska, Ewa

doi:10.1016/j.jspr.2020.101668

Cited by 23 publications

(10 citation statements)

References 19 publications

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“…In addition to their complexity and time-consuming nature, contact (destructive) methods have other limitations, the most important of which is the possibility of damaging the sample [13]. Therefore, in previous studies, computer vision systems were usefully explored as an inexpensive, accurate, and objective approach to evaluating seed cultivars [14][15][16]. Since fruit is one of the main products in international markets and exports, its classification and grading are among the most important domains in agriculture [17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to the Authentication of Apricot Seed Cultivars Using Innovative Models Based on Image Texture Parameters

et al. 2022

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The different cultivars of apricot seeds may differ in their properties. To ensure economical and efficient seed processing, knowledge of the cultivars’ composition and physical properties may be necessary. Therefore, the correct identification of the cultivar of the apricot seeds may be very important. The objective of this study was to develop models based on selected textures of apricot seed images to distinguish different cultivars. The images of four cultivars of apricot seeds were acquired using a flatbed scanner. For each seed, approximately 1600 textures from the image, converted to the different color channels R, G, B, L, a, b, X, Y, and Z, were calculated. The models were built separately for the individual color channels; the color spaces Lab, RGB, XYZ; and all color channels combined based on selected texture parameters using different classifiers. The average accuracy of the classification of apricot seeds reached 99% (with an accuracy of 100% for the seeds of the cultivars ‘Early Orange’, ‘Bella’, and ‘Harcot’, and 96% for ‘Taja’) in the case of the set of textures selected from the color space Lab for the model built using the Multilayer Perceptron classifier. The same classifier produced high average accuracies for the color spaces RGB (90%) and XYZ (86%). For the set of textures selected from all color channels, i.e., R, G, B, L, a, b, X, Y, and Z, the average accuracy reached 96% (Multilayer Perceptron and Random Forest classifiers). In the case of individual color channels, the highest average accuracy was up to 91% for the models built based on a set of textures selected from color channel b (Multilayer Perceptron). The results proved the possibility of distinguishing apricot seed cultivars with a high probability using a non-destructive, inexpensive, and objective procedure involving image analysis.

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Section: Introductionmentioning

confidence: 99%

A Novel Approach to the Authentication of Apricot Seed Cultivars Using Innovative Models Based on Image Texture Parameters

et al. 2022

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“…In this regard, many studies present techniques for non-destructive seed classification, such as magnetic resonance imaging, electronic tongue, acoustic, electronic nose, and computer vision (Xia et al, 2019). Among these methods, computer vision and image processing can classify crops at a low cost with high analytical and computational power (Patrício and Rieder, 2018;Ropelewska, 2020). Computer vision-based classification consists of four blocks: image preprocessing, segmentation, feature extraction, and classification (Sharif et al, 2018), where feature extraction has a significant effect on classification accuracy (Iqbal et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Computer-vision classification of corn seed varieties using deep convolutional neural network

Javanmardi

Ashtiani

Verbeek

et al. 2021

Journal of Stored Products Research

105

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Automated classification of seed varieties is of paramount importance for seed producers to maintain the purity of a variety and crop yield. Traditional approaches based on computer vision and simple feature extraction could not guarantee high accuracy classification. This paper presents a new approach using a deep convolutional neural network (CNN) as a generic feature extractor. The extracted features were classified with artificial neural network (ANN), cubic support vector machine (SVM), quadratic SVM, weighted k-nearest-neighbor (kNN), boosted tree, bagged tree, and linear discriminant analysis (LDA). Models trained with CNN-extracted features demonstrated better classification accuracy of corn seed varieties than models based on only simple features. The CNN-ANN classifier showed the best performance, classifying 2250 test instances in 26.8 s with classification accuracy 98.1%, precision 98.2%, recall 98.1%, and F1-score 98.1%. This study demonstrates that the CNN-ANN classifier is an efficient tool for the intelligent classification of different corn seed varieties.

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“…Due to the different properties and chemical composition of pepper belonging to different cultivars, the correct cultivar identification may be desirable for food industry. In the available literature, there are reports on the use of machine learning and image analysis for objective evaluation of food products, including the results on cultivar discrimination (e.g., Alipasandi, Ghaffari, & Alibeyglu, 2013; Ronald & Evans, 2016; Ropelewska, 2020; Ropelewska, 2021a; Ropelewska, 2021b; Ropelewska, 2021c; Sofu, Erb, Kayacan, & Cetişli, 2016). In addition to cultivar discrimination, machine learning was successfully applied, among others, for the maturity determination of apples with the combination with visible and near‐infrared spectroscopy (Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of the discrimination of pepper (Capsicum annuum L.) based on the cross‐section and seed textures determined using image processing

Ropelewska

Szwejda‐Grzybowska

2021

J Food Process Engineering

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The aim of this study was to develop the models based on the texture parameters of images of selected parts of pepper fruit for cultivar discrimination. This study was aimed to compare the usefulness of textures from cross-sections (slices) and seeds of pepper for distinguishing the different cultivars using the discriminative classifiers.The textures from the cross-sections and seeds were calculated based on images converted to individual color channels. The texture selections and discriminant analyzes were performed separately for color spaces RGB, Lab, XYZ

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The use of seed texture features for discriminating different cultivars of stored apples

Cited by 23 publications

References 19 publications

A Novel Approach to the Authentication of Apricot Seed Cultivars Using Innovative Models Based on Image Texture Parameters

A Novel Approach to the Authentication of Apricot Seed Cultivars Using Innovative Models Based on Image Texture Parameters

Computer-vision classification of corn seed varieties using deep convolutional neural network

A comparative analysis of the discrimination of pepper (Capsicum annuum L.) based on the cross‐section and seed textures determined using image processing

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