<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Dried grapes (or Raisins) are among the most frequently grown and consumed cereal crops worldwide. They are also an important source of nutrition and nourishment in a variety of countries including Türkiye, the United States, Greece, etc. In addition to that, raisins consist of 15% water, 79% carbs (including 4% fiber), 3% protein, and very little fat. In our study, there were a total of 900 raisin grains used, with 450 pieces from each type: Kecimen and Besni raisin. Seven morphological features were taken from these images after going through several steps of pre-processing. Since machine learning algorithms can analyze large datasets quickly, automatic classification is made possible. With enough training and testing, machine learning models can attain a high degree of precision in classifying raisin grains. They are able to detect variations in size, shape, color, and texture that would be difficult for humans to detect consistently. Eleven machine learning and five different types of artificial intelligence have been used to classify these features. As part of this study, we look into different machine learning and deep learning methods: GaussianNB, Decision Tree, K-Nearest Neighbor, Random Forest, Support vector machine (SVM), XGBoost, LightGBM, and AdaBoost, Logistic Regression, Artificial Neural Network and Deep Learning Network. Study efficacy is evaluated using standard metrics as F1 score and ROC area under the curve (AUC). Using the caret, H<sub>2</sub>O, neuralnet, and keras packages, AdaBoost and LightGBM, two of the fourteen models, achieve an accuracy of 90.30% and 98.40%, respectively, and a ROC curve score of around 90%.</p>
</abstract>

Dried grapes (or Raisins) are among the most frequently grown and consumed cereal crops worldwide. They are also an important source of nutrition and nourishment in a variety of countries including Türkiye, the United States, Greece, etc. In addition to that, raisins consist of 15% water, 79% carbs (including 4% fiber), 3% protein, and very little fat. In our study, there were a total of 900 raisin grains used, with 450 pieces from each type: Kecimen and Besni raisin. Seven morphological features were taken from these images after going through several steps of pre-processing. Since machine learning algorithms can analyze large datasets quickly, automatic classification is made possible. With enough training and testing, machine learning models can attain a high degree of precision in classifying raisin grains. They are able to detect variations in size, shape, color, and texture that would be difficult for humans to detect consistently. Eleven machine learning and five different types of artificial intelligence have been used to classify these features. As part of this study, we look into different machine learning and deep learning methods: GaussianNB, Decision Tree, K-Nearest Neighbor, Random Forest, Support vector machine (SVM), XGBoost, LightGBM, and AdaBoost, Logistic Regression, Artificial Neural Network and Deep Learning Network. Study efficacy is evaluated using standard metrics as F1 score and ROC area under the curve (AUC). Using the caret, H2O, neuralnet, and keras packages, AdaBoost and LightGBM, two of the fourteen models, achieve an accuracy of 90.30% and 98.40%, respectively, and a ROC curve score of around 90%.

Prediction modeling using deep learning for the classification of grape-type dried fruits

Department of Mathematics and Computer Science

International Journal of Mathematics and Computer in Engineering

This work is licensed under the Creative Commons Attribution 4.0 International License.

Dried grapes (or Raisins) are among the most frequently grown and consumed cereal crops worldwide. They are also an important source of nutrition and...

Prediction modeling using deep learning for the classification of grape-type dried fruits

Article Category: Original Study

Data publikacji: 31 paź 2023

Zakres stron: 1 - 12

Otrzymano: 20 sie 2023

Przyjęty: 19 wrz 2023

DOI: https://doi.org/10.2478/ijmce-2024-0001

Słowa kluczowe
Raisin grains, AdaBoost, random forest, classification algorithms, LightGBM, machine learning technique

© 2024 Md Nurul Raihen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Prediction modeling using deep learning for the classification of grape-type dried fruits

Article Category: Original Study

Data publikacji: 31 paź 2023

Zakres stron: 1 - 12

Otrzymano: 20 sie 2023

Przyjęty: 19 wrz 2023

DOI: https://doi.org/10.2478/ijmce-2024-0001

Słowa kluczoweRaisin grains, AdaBoost, random forest, classification algorithms, LightGBM, machine learning technique

© 2024 Md Nurul Raihen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Raisin grains, AdaBoost, random forest, classification algorithms, LightGBM, machine learning technique