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 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 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 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 Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elisabeth Sartoretti ◽  
Thomas Sartoretti ◽  
Michael Wyss ◽  
Carolin Reischauer ◽  
Luuk van Smoorenburg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brain Tumors ◽  
Proton Transfer ◽  
Multilayer Perceptron ◽  
Random Forest Classifier ◽  
Amide Proton ◽  
Low Grade ◽  
Who Grade ◽  
Amide Proton Transfer

AbstractWe sought to evaluate the utility of radiomics for Amide Proton Transfer weighted (APTw) imaging by assessing its value in differentiating brain metastases from high- and low grade glial brain tumors. We retrospectively identified 48 treatment-naïve patients (10 WHO grade 2, 1 WHO grade 3, 10 WHO grade 4 primary glial brain tumors and 27 metastases) with either primary glial brain tumors or metastases who had undergone APTw MR imaging. After image analysis with radiomics feature extraction and post-processing, machine learning algorithms (multilayer perceptron machine learning algorithm; random forest classifier) with stratified tenfold cross validation were trained on features and were used to differentiate the brain neoplasms. The multilayer perceptron achieved an AUC of 0.836 (receiver operating characteristic curve) in differentiating primary glial brain tumors from metastases. The random forest classifier achieved an AUC of 0.868 in differentiating WHO grade 4 from WHO grade 2/3 primary glial brain tumors. For the differentiation of WHO grade 4 tumors from grade 2/3 tumors and metastases an average AUC of 0.797 was achieved. Our results indicate that the use of radiomics for APTw imaging is feasible and the differentiation of primary glial brain tumors from metastases is achievable with a high degree of accuracy.

scholarly journals Effect of Spaceflight on Tomato Seed Quality and Biochemical Characteristics of Mature Plants

Horticulturae ◽  
2021 ◽  
Vol 7 (5) ◽  
pp. 89
Author(s):  
Elena Dzhos ◽  
Nadezhda Golubkina ◽  
Marina Antoshkina ◽  
Irina Kondratyeva ◽  
Andrew Koshevarov ◽  
...  
Keyword(s):  
Seed Quality ◽  
Space Station ◽  
Field Conditions ◽  
Tomato Seed ◽  
Tomato Plants ◽  
Biochemical Characteristics ◽  
Tomato Seeds ◽  
Genetic Modifications ◽  
Total Antioxidant

Intensive space exploration includes profound investigations on the effect of weightlessness and cosmic radiation on plant growth and development. Tomato seeds are often used in such experiments though up to date the results have given rather vague information about biochemical changes in mature plants grown from seeds subjected to spaceflight. The effect of half a year of storage in the International Space Station (ISS) on tomato seeds (cultivar Podmoskovny ranny) was studied by analyzing the biochemical characteristics and mineral content of mature plants grown from these seeds both in greenhouse and field conditions. A significant increase was recorded in ascorbic acid, polyphenol and carotenoid contents, and total antioxidant activity (AOA), with higher changes in the field conditions compared to greenhouse. Contrary to control plants, the ones derived from space-stored seeds demonstrated a significant decrease in root AOA. The latter plants also showed a higher yield, but lower content of fruit dry matter, sugars, total dissolved solids and organic acids. The fruits of plants derived from space-stored seeds demonstrated decreased levels of Fe, Cu and taste index. The described results reflect the existence of oxidative stress in mature tomato plants as a long-term consequence of the effect of spaceflight on seed quality, whereas the higher yield may be attributed to genetic modifications.

scholarly journals Development of a Safety Management System Tracking the Weight of Heavy Objects Carried by Construction Workers Using FSR Sensors

2021 ◽  
Vol 11 (4) ◽  
pp. 1378
Author(s):  
Seung Hyun Lee ◽  
Jaeho Son
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Safety Management ◽  
Tracking System ◽  
Construction Workers ◽  
Gradient Boosting ◽  
Prototype System ◽  
Construction Site ◽  
Average Accuracy ◽  
Smart Safety

It has been pointed out that the act of carrying a heavy object that exceeds a certain weight by a worker at a construction site is a major factor that puts physical burden on the worker’s musculoskeletal system. However, due to the nature of the construction site, where there are a large number of workers simultaneously working in an irregular space, it is difficult to figure out the weight of the object carried by the worker in real time or keep track of the worker who carries the excess weight. This paper proposes a prototype system to track the weight of heavy objects carried by construction workers by developing smart safety shoes with FSR (Force Sensitive Resistor) sensors. The system consists of smart safety shoes with sensors attached, a mobile device for collecting initial sensing data, and a web-based server computer for storing, preprocessing and analyzing such data. The effectiveness and accuracy of the weight tracking system was verified through the experiments where a weight was lifted by each experimenter from +0 kg to +20 kg in 5 kg increments. The results of the experiment were analyzed by a newly developed machine learning based model, which adopts effective classification algorithms such as decision tree, random forest, gradient boosting algorithm (GBM), and light GBM. The average accuracy classifying the weight by each classification algorithm showed similar, but high accuracy in the following order: random forest (90.9%), light GBM (90.5%), decision tree (90.3%), and GBM (89%). Overall, the proposed weight tracking system has a significant 90.2% average accuracy in classifying how much weight each experimenter carries.

scholarly journals Random forest and multilayer perceptron for predicting the dielectric loss of polyimide nanocomposite films

RSC Advances ◽  
2017 ◽  
Vol 7 (49) ◽  
pp. 30999-31008
Author(s):  
H. Guo ◽  
J. Y. Zhao ◽  
J. H. Yin
Keyword(s):  
Random Forest ◽  
Dielectric Loss ◽  
Multilayer Perceptron ◽  
Experimental Results ◽  
Nanocomposite Films ◽  
Polyimide Nanocomposite

A random forest and multilayer perceptron for predicting the dielectric loss of polyimide nanocomposite films. As shown in the experimental results, the error between the predicted value and the measured value is small.

scholarly journals Species-specific audio detection: A comparison of three template-based classification algorithms using random forests

2017 ◽  
Author(s):  
Carlos J Corrada Bravo ◽  
Rafael Álvarez Berríos ◽  
T. Mitchell Aide
Keyword(s):  
Random Forest ◽  
Random Forests ◽  
Random Forest Classifier ◽  
Classification Algorithms ◽  
Statistical Features ◽  
Web Based ◽  
Average Accuracy ◽  
Species Specific ◽  
Web Based System

We developed a web-based cloud-hosted system that allow users to archive, listen, visualize, and annotate recordings. The system also provides tools to convert these annotations into datasets that can be used to train a computer to detect the presence or absence of a species. The algorithm used by the system was selected after comparing the accuracy and efficiency of three variants of a template-based classification. The algorithm computes a similarity vector by comparing a template of a species call with time increments across the spectrogram. Statistical features are extracted from this vector and used as input for a Random Forest classifier that predicts presence or absence of the species in the recording. The fastest algorithm variant had the highest average accuracy and specificity; therefore, it was implemented in the ARBIMON web-based system.

scholarly journals Effect of lacto-fermentation and freeze-drying on the quality of beetroot evaluated using machine vision and sensory analysis

2021 ◽  
Author(s):  
Ewa Ropelewska ◽  
Anna Wrzodak ◽  
Kadir Sabanci ◽  
Muhammet Fatih Aslan
Keyword(s):  
Freeze Drying ◽  
Sensory Quality ◽  
Bitter Taste ◽  
Sensory Attributes ◽  
Discrimination Accuracy ◽  
Flatbed Scanner ◽  
Color Channel ◽  
Texture Parameters ◽  
Freeze Dried

AbstractThis study was aimed at evaluating the effect of freeze-drying and lacto-fermentation on the texture parameters of images and sensory attributes of beetroots. The samples were imaged using a flatbed scanner, and textures from images converted to color channels L, a, b, R, G, B, X, Y, Z were computed. The discrimination of raw and processed beetroots was performed using models based on textures selected for each color channel. The sensory quality of processed samples was determined using the attributes related to smell, color, texture and taste. The highest discrimination accuracy of 97.25% was obtained for the model built for color channel b. The accuracies for other channels were equal to 96.25% for channel a, 95.25% for channel R, 95% for channel Y, 94.75% for channel B, 94.5% for channel X, 94% for channel L, 92.5% for channel G, 88.25% for channel Z. In the case of some models, the raw and lacto-fermented beetroots were discriminated with 100% correctness. The freeze-dried and freeze-dried lacto-fermented samples were also the most similar in terms of sensory attributes, such as off-odor, attractiveness color, beetroot color, crunchiness, hardness, bitter taste, overall quality. The results indicated that the image parameters and sensory attributes may be related.

scholarly journals Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2927
Author(s):  
Jiyeong Hong ◽  
Seoro Lee ◽  
Joo Hyun Bae ◽  
Jimin Lee ◽  
Woon Ji Park ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Multilayer Perceptron ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Dam Inflow

Predicting dam inflow is necessary for effective water management. This study created machine learning algorithms to predict the amount of inflow into the Soyang River Dam in South Korea, using weather and dam inflow data for 40 years. A total of six algorithms were used, as follows: decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), recurrent neural network–long short-term memory (RNN–LSTM), and convolutional neural network–LSTM (CNN–LSTM). Among these models, the multilayer perceptron model showed the best results in predicting dam inflow, with the Nash–Sutcliffe efficiency (NSE) value of 0.812, root mean squared errors (RMSE) of 77.218 m3/s, mean absolute error (MAE) of 29.034 m3/s, correlation coefficient (R) of 0.924, and determination coefficient (R2) of 0.817. However, when the amount of dam inflow is below 100 m3/s, the ensemble models (random forest and gradient boosting models) performed better than MLP for the prediction of dam inflow. Therefore, two combined machine learning (CombML) models (RF_MLP and GB_MLP) were developed for the prediction of the dam inflow using the ensemble methods (RF and GB) at precipitation below 16 mm, and the MLP at precipitation above 16 mm. The precipitation of 16 mm is the average daily precipitation at the inflow of 100 m3/s or more. The results show the accuracy verification results of NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, and R2 0.859 in RF_MLP, and NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, and R2 0.831 in GB_MLP, which infers that the combination of the models predicts the dam inflow the most accurately. CombML algorithms showed that it is possible to predict inflow through inflow learning, considering flow characteristics such as flow regimes, by combining several machine learning algorithms.

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