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Classification of Rice Varieties Using a Deep Neural Network Model

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Advances in Intelligent Manufacturing and Service System Informatics (IMSS 2023)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

Deep learning is a machine learning approach that has been widely used in many different fields in recent years. It is used in agriculture for various purposes, such as product classification and diagnosis of agricultural diseases. In this study, we propose a deep-learning model for the classification of rice species. Rice is an agricultural product that is widely consumed in Turkey as well as in the world. In our study, a rice data set that contains 7 morphological features obtained by using 3810 rice grains belonging to two species is used. Our model consists of three hidden layers and two dropouts (3H2D) added to these layers to prevent overfitting in classification. The success of the model is compared with Logistic Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Ada Boost (AB), Bagging (BG), and Voting (VT) classifiers. The success rates of these methods are as follows: 93.02%, 92.86%, 92.83%, 92.49%, 92.39%, 91.71%, 88.58%, 92.34%, 91.68%, and 90.52% respectively. The success rate of the proposed method is 94.09%. According to the results obtained, the proposed method is more successful than all of these machine learning methods.

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Author information

Authors and Affiliations

  1. Sakarya University, 54050, Serdivan/Sakarya, Turkey

    Nuran Peker

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  1. Nuran Peker
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Correspondence to Nuran Peker .

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Editors and Affiliations

  1. Istanbul Medipol University, Istanbul, Türkiye

    Zekâi Şen

  2. Department of Industrial Engineering, Sakarya University, Serdivan, Sakarya, Türkiye

    Özer Uygun

  3. Faculty of Applied Sciences, Sakarya University of Applied Sciences, Kaynarca, Sakarya, Türkiye

    Caner Erden

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Peker, N. (2024). Classification of Rice Varieties Using a Deep Neural Network Model. In: Şen, Z., Uygun, Ö., Erden, C. (eds) Advances in Intelligent Manufacturing and Service System Informatics. IMSS 2023. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-6062-0_47

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  • DOI: https://doi.org/10.1007/978-981-99-6062-0_47

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6061-3

  • Online ISBN: 978-981-99-6062-0

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