Differentiation of peach cultivars by image analysis based on the skin, flesh, stone and seed textures

Ropelewska, Ewa; Rutkowski, K.

doi:10.1007/s00217-021-03797-9

Cited by 18 publications

(10 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth noting that due to the development of new instrumentation and analysis tools, horticultural products can now be analyzed using image processing techniques. The usefulness of models based on features selected from images that were employed to perform cultivar discrimination of seeds, pits, and stones was also reported in the literature, e.g., for seeds and stones of peach [22], seeds of pepper [23], pits of sweet [24] and sour cherries [25], seeds of apples [16], and seeds of wheat [26]. Based on the promising results in the literature on the effectiveness of image analysis and machine learning to distinguish seeds and pits, the following research hypotheses were formulated to be tested by the present study:…”

Section: Introductionmentioning

confidence: 80%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 80%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The CFS is an attribute subset evaluator assessing the degree of redundancy among the attributes and the predictive value of each attribute [26]. The Best First and CFS were successively used in the previous studies and allowed selecting the texture sets appropriate for the development of the models providing the highest discrimination accuracies [27][28][29]…”

Section: Discussionmentioning

confidence: 99%

Preservation effects evaluated using innovative models developed by machine learning on cucumber flesh

Ropelewska

Sabancı

Aslan

2022

Eur Food Res Technol

Self Cite

View full text Add to dashboard Cite

The objective of this study was to compare the slice image features of cucumbers preserved in different ways and fresh cucumbers. The models for discrimination of spontaneous lacto-fermented, pickled using a vinegar solution and fresh cucumber slices were built based on selected textures extracted from digital images converted to color channels L, a, b, R, G, B, U, V, X, Y, Z. The average accuracies were high of up to 98% for the Random Forest and Logistic classifiers for a model built based on textures selected from a set including textures from all color channels. The discrimination accuracies for individual samples were equal to 100% for fresh cucumber (Bayes Net, Logistic and IBk classifiers), 98% for vinegar-pickled cucumber (Random Forest, Logistic), 97% for lacto-fermented cucumber (Random Forest). The values of TP Rate, Precision, PRC Area, ROC Area and F-Measure were the highest for fresh cucumber and were equal to 1.000.

show abstract

“…compared the classification results of different color channels and different discriminant models by acquiring images of peach skin, flesh, stone and seed texture. ( Ropelewska and Rutkowski, 2021 ). The results show that the texture features based on different color channels can better complete the identification of peach varieties.…”

Section: Phenotypic Information Acquisition and Related Applications ...mentioning

confidence: 99%

A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology

Huang

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

Fruit phenotypic information reflects all the physical, physiological, biochemical characteristics and traits of fruit. Accurate access to phenotypic information is very necessary and meaningful for post-harvest storage, sales and deep processing. The methods of obtaining phenotypic information include traditional manual measurement and damage detection, which are inefficient and destructive. In the field of fruit phenotype research, image technology is increasingly mature, which greatly improves the efficiency of fruit phenotype information acquisition. This review paper mainly reviews the research on phenotypic information of Prunoideae fruit based on three imaging techniques (RGB imaging, hyperspectral imaging, multispectral imaging). Firstly, the classification was carried out according to the image type. On this basis, the review and summary of previous studies were completed from the perspectives of fruit maturity detection, fruit quality classification and fruit disease damage identification. Analysis of the advantages and disadvantages of various types of images in the study, and try to give the next research direction for improvement.

show abstract

Differentiation of peach cultivars by image analysis based on the skin, flesh, stone and seed textures

Cited by 18 publications

References 25 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

Preservation effects evaluated using innovative models developed by machine learning on cucumber flesh

A comprehensive review on acquisition of phenotypic information of Prunoideae fruits: Image technology

Contact Info

Product

Resources

About