scholarly journals Flesh of pumpkin from ecological farming as part of fruit suitable for non-destructive cultivar classification using computer vision

2021 ◽  
Author(s):  
Ewa Ropelewska ◽  
Wioletta Popińska ◽  
Kadir Sabanci ◽  
Muhammet Fatih Aslan
Keyword(s):  
Computer Vision ◽  
Outer Surface ◽  
Color Space ◽  
Discriminative Models ◽  
Ecological Farming ◽  
Cultivar Classification ◽  
The Individual ◽  
Non Destructive ◽  
Total Accuracy ◽  
Color Channels

AbstractThe aim of this study was to build the discriminative models for distinguishing the different cultivars of flesh of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ based on selected textures of the outer surface of images of cubes. The novelty of research involved the use of about 2000 different textures for one image. The highest total accuracy (98%) of discrimination of pumpkin ‘Bambino’, ‘Butternut’, ‘Uchiki Kuri’ and ‘Orange’ was determined for models built based on textures selected from the color space Lab and the IBk classifier and some of the individual cultivars were classified with the correctness of 100%. The total accuracy of up to 96% was observed for color space RGB and 97.5% for color space XYZ. In the case of color channels, the total accuracies reached 91% for channel b, 89.5% for channel X, 89% for channel Z.

scholarly journals Effect of boiling on classification performance of potatoes determined by computer vision

2021 ◽  
Author(s):  
Ewa Ropelewska
Keyword(s):  
Outer Surface ◽  
Color Space ◽  
Classification Performance ◽  
Color Spaces ◽  
Color Channel ◽  
Discriminative Models ◽  
Model Combining ◽  
Processed Potatoes ◽  
Cultivar Discrimination ◽  
Color Channels

AbstractThe aim of this study was to evaluate the effect of potato boiling on the correctness of cultivar discrimination. The research was performed in an objective, inexpensive and fast manner using the image analysis technique. The textures of the outer surface of slice images of raw and boiled potatoes were calculated. The discriminative models based on a set of textures selected from all color channels (R, G, B, L, a, b, X, Y, Z, U, V, S), textures selected for color spaces and textures selected for individual color channels were developed. In the case of discriminant analysis of raw potatoes of cultivars ‘Colomba’, ‘Irga’ and ‘Riviera’, the accuracies reached 94.33% for the model built based on a set of textures selected from all color channels, 94% for Lab and XYZ color spaces, 92% for color channel b and 92.33% for a set of combined textures selected from channels B, b, and Z. The processed potatoes were characterized by the accuracy of up to 98.67% for the model including the textures selected from all color channels, 98% for RGB color space, 95.33% for color channel b, 96.67% for the model combining the textures selected from channels B, b, and Z. In the case of raw and processed potatoes, the cultivar ‘Irga’ differed in 100% from other potato cultivars. The results revealed an increase in cultivar discrimination accuracy after the processing of potatoes. The textural features of the outer surface of slice images have proved useful for cultivar discrimination of raw and processed potatoes.

scholarly journals The Application of Machine Learning for Cultivar Discrimination of Sweet Cherry Endocarp

Agriculture ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Ewa Ropelewska
Keyword(s):  
Sweet Cherry ◽  
Color Space ◽  
Geometric Parameters ◽  
Geometric Features ◽  
Color Spaces ◽  
Discriminative Models ◽  
Sweet Cherries ◽  
Rgb Color Space ◽  
Cultivar Discrimination ◽  
Color Channels

The aim of this study was to evaluate the usefulness of the texture and geometric parameters of endocarp (pit) for distinguishing different cultivars of sweet cherries using image analysis. The textures from images converted to color channels and the geometric parameters of the endocarp (pits) of sweet cherry ‘Kordia’, ‘Lapins’, and ‘Büttner’s Red’ were calculated. For the set combining the selected textures from all color channels, the accuracy reached 100% when comparing ‘Kordia’ vs. ‘Lapins’ and ‘Kordia’ vs. ‘Büttner’s Red’ for all classifiers. The pits of ‘Kordia’ and ‘Lapins’, as well as ‘Kordia’ and ‘Büttner’s Red’ were also 100% correctly discriminated for discriminative models built separately for RGB, Lab and XYZ color spaces, G, L and Y color channels and for models combining selected textural and geometric features. For discrimination ‘Lapins’ and ‘Büttner’s Red’ pits, slightly lower accuracies were determined—up to 93% for models built based on textures selected from all color channels, 91% for the RGB color space, 92% for the Lab and XYZ color spaces, 84% for the G and L color channels, 83% for the Y channel, 94% for geometric features, and 96% for combined textural and geometric features.

scholarly journals Discrimination of tomato seeds belonging to different cultivars using machine learning

2021 ◽  
Author(s):  
Ewa Ropelewska ◽  
Jan Piecko
Keyword(s):  
Random Forest ◽  
Outer Surface ◽  
Multilayer Perceptron ◽  
Tomato Seed ◽  
Color Channel ◽  
Discriminative Models ◽  
Tomato Seeds ◽  
Average Accuracy ◽  
Texture Parameters ◽  
Color Channels

AbstractThis study was aimed at developing the discriminant models for distinguishing the tomato seeds based on texture parameters of the outer surface of seeds calculated from the images (scans) converted to individual color channels R, G, B, L, a, b, X, Y, Z. The seeds of tomatoes ‘Green Zebra’, ‘Ożarowski’, ‘Pineapple’, Sacher F1 and Sandoline F1 were discriminated in pairs. The highest results were observed for models built based on sets of textures selected individually from color channels R, L and X and sets of textures selected from all color channels. In all cases, the tomato seeds ‘Green Zebra’ and ‘Ożarowski’ were discriminated with the highest average accuracy equal to 97% for the Multilayer Perceptron classifier and 96.25% for Random Forest for color channel R, 95.25% (Multilayer Perceptron) and 95% (Random Forest) for color channel L, 93% (Multilayer Perceptron) and 95% (Random Forest) for color channel X, 99.75% (Multilayer Perceptron) and 99.5% (Random Forest) for a set of textures selected from all color channels (R, G, B, L, a, b, X, Y, X). The highest average accuracies for other pairs of cultivars reached 98.25% for ‘Ożarowski’ vs. Sacher F1, 95.75% for ‘Pineapple’ vs. Sandoline F1, 97.5% for ‘Green Zebra’ vs. Sandoline F1, 97.25% for Sacher F1 vs. Sandoline F1 for models built based on textures selected from all color channels. The obtained results may be used in practice for the identification of cultivar of tomato seeds. The developed models allow to distinguish the tomato seed cultivars in an objective and fast way using digital image processing. The results confirmed the usefulness of texture parameters of the outer surface of tomato seeds for classification purposes. The discriminative models allow to obtain a very high probability and may be applied to authenticate and detect seed adulteration.

scholarly journals Analyzing of Coatings on Steel - Reinforced Concrete Elements by Mobile NMR

2016 ◽  
Vol 62 (1) ◽  
pp. 65-82 ◽  
Author(s):  
J. Orlowsky
Keyword(s):  
Reinforced Concrete ◽  
Potential Effect ◽  
Building Site ◽  
Steel Reinforced Concrete ◽  
Mobile Nmr ◽  
The Individual ◽  
First Time ◽  
Concrete Elements ◽  
Non Destructive

Abstract A large number of infrastructural concrete buildings are protected against aggressive environments by coating systems. The functionality of these coating systems is mainly affected by the composition and thickness of the individual polymeric layers. For the first time ever, a mobile nuclear magnetic resonance (NMR) sensor allows a non-destructive determination of these important parameters on the building site. However, before this technique can be used on steel-reinforced concrete elements, the potential effect of the reinforcement on the measurement, i.e. the NMR signal, needs to be studied. The results show a shift of the NMR profile as well as an increase of the signals amplitude in the case of the reinforced samples, while calculating the thickness of concrete coating leading to identical results.

Computer vision coupled with laser backscattering for non-destructive colour evaluation of papaya during drying

2017 ◽  
Vol 11 (4) ◽  
pp. 2142-2150 ◽  
Author(s):  
Patchimaporn Udomkun ◽  
Marcus Nagle ◽  
Dimitrios Argyropoulos ◽  
Alexander Nimo Wiredu ◽  
Busarakorn Mahayothee ◽  
...  
Keyword(s):  
Computer Vision ◽  
Non Destructive

Identification and Classification of Fungal Colonies in Moldy Paddy Based on Computer Vision

2018 ◽  
Vol 61 (5) ◽  
pp. 1497-1504
Author(s):  
Zhenjie Wang ◽  
Ke Sun ◽  
Lihui Du ◽  
Jian Yuan ◽  
Kang Tu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Discriminant Analysis ◽  
Texture Features ◽  
Fungal Species ◽  
Projection Algorithm ◽  
Support Vector ◽  
Linear Discriminant ◽  
Sequential Selection ◽  
The Individual

Abstract. In this study, computer vision was used for the identification and classification of fungi on moldy paddy. To develop a rapid and efficient method for the classification of common fungal species found in stored paddy, computer vision was used to acquire images of individual colonies of growing fungi for three consecutive days. After image processing, the color, shape, and texture features were acquired and used in a subsequent discriminant analysis. Both linear (i.e., linear discriminant analysis and partial least squares discriminant analysis) and nonlinear (i.e., random forest and support vector machine [SVM]) pattern recognition models were employed for the classification of fungal colonies, and the results were compared. The results indicate that when using all of the features for three consecutive days, the performance of the nonlinear tools was superior to that of the linear tools, especially in the case of the SVM models, which achieved an accuracy of 100% on the calibration sets and an accuracy of 93.2% to 97.6% on the prediction sets. After sequential selection of projection algorithm, ten common features were selected for building the classification models. The results showed that the SVM model achieved an overall accuracy of 95.6%, 98.3%, and 99.0% on the prediction sets on days 2, 3, and 4, respectively. This work demonstrated that computer vision with several features is suitable for the identification and classification of fungi on moldy paddy based on the form of the individual colonies at an early growth stage during paddy storage. Keywords: Classification, Computer vision, Fungal colony, Feature selection, SVM.

scholarly journals AlexNet convolutional neural network to classify the types of Indonesian coffee beans

2021 ◽  
Vol 905 (1) ◽  
pp. 012059
Author(s):  
Y Hendrawan ◽  
B Rohmatulloh ◽  
F I Ilmi ◽  
M R Fauzy ◽  
R Damayanti ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Sensitivity Analysis ◽  
Convolutional Neural Network ◽  
Confusion Matrix ◽  
Learning Rate ◽  
Agricultural Products ◽  
Coffee Bean ◽  
Coffee Beans ◽  
Non Destructive

Abstract Various types of Indonesian coffee are already popular internationally. Recently, there are still not many methods to classify the types of typical Indonesian coffee. Computer vision is a non-destructive method for classifying agricultural products. This study aimed to classify three types of Indonesian Arabica coffee beans, i.e., Gayo Aceh, Kintamani Bali, and Toraja Tongkonan, using computer vision. The classification method used was the AlexNet convolutional neural network with sensitivity analysis using several variations of the optimizer such as SGDm, Adam, and RMSProp and the learning rate of 0.00005 and 0.0001. Each type of coffee used 500 data for training and validation with the distribution of 70% training and 30% validation. The results showed that all AlexNet models achieved a perfect validation accuracy value of 100% in 1,040 iterations. This study also used 100 testing-set data on each type of coffee bean. In the testing confusion matrix, the accuracy reached 99.6%.

scholarly journals Web Application for Emotion-based Music Player

2021 ◽  
Vol 9 (12) ◽  
pp. 11-14
Author(s):  
Kamal Naina Soni
Keyword(s):  
Computer Vision ◽  
Human Brain ◽  
Facial Expressions ◽  
Web Application ◽  
Emotional State ◽  
Current State ◽  
The Face ◽  
Current Mood ◽  
The Individual ◽  
Music Player

Abstract: Human expressions play an important role in the extraction of an individual's emotional state. It helps in determining the current state and mood of an individual, extracting and understanding the emotion that an individual has based on various features of the face such as eyes, cheeks, forehead, or even through the curve of the smile. A survey confirmed that people use Music as a form of expression. They often relate to a particular piece of music according to their emotions. Considering these aspects of how music impacts a part of the human brain and body, our project will deal with extracting the user’s facial expressions and features to determine the current mood of the user. Once the emotion is detected, a playlist of songs suitable to the mood of the user will be presented to the user. This can be a big help to alleviate the mood or simply calm the individual and can also get quicker song according to the mood, saving time from looking up different songs and parallel developing a software that can be used anywhere with the help of providing the functionality of playing music according to the emotion detected. Keywords: Music, Emotion recognition, Categorization, Recommendations, Computer vision, Camera

Non-destructive and contactless quality evaluation of table grapes by a computer vision system

2019 ◽  
Vol 156 ◽  
pp. 558-564 ◽  
Author(s):  
Dario Pietro Cavallo ◽  
Maria Cefola ◽  
Bernardo Pace ◽  
Antonio Francesco Logrieco ◽  
Giovanni Attolico
Keyword(s):  
Computer Vision ◽  
Quality Evaluation ◽  
Vision System ◽  
Computer Vision System ◽  
Table Grapes ◽  
Non Destructive
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