Research Article
BibTex RIS Cite

Makine Öğrenme Algoritmalarına Dayalı Görüntü Özellikleri Kullanılarak Hurma Meyvelerinin Sınıflandırılması

Year 2024, Volume: 55 Issue: 1, 26 - 35, 31.01.2024

Abstract

Bilimsel olarak Phoenix dactylifera olarak bilinen hurma meyvesi, yüksek besin değeri ve temel vitamin ve minerallerin bolluğu nedeniyle önemli bir diyet bileşenidir. Doğal ortamında çok sayıda
varyasyon sergileyen bu meyvenin sınıflandırılmasını ayırt etme süreci, özel bir yetenek gerektirir. Tarımsal ürünlerin görüntülerine dayalı türlerin otomatik olarak tanınması son zamanlarda önemli
bir yaygınlık kazanmıştır. Bu amaçla, mevcut çalışma, yedi tür hurma meyvesini otomatik olarak tanımlamak için makine öğrenme algoritmalarını kullandı. Araştırmada hurma meyvelerinin
sınıflandırılması amacıyla farklı hiperparametreler ile karar ağaçları, K-En Yakın Komşu, yapay Sinir Ağları ve Destek Vektör Makinesi kullanılmıştır. Veri seti, eğitim ve test için sırasıyla %80 ve %20
oranında bölünmüştür ve eğitim sürecinde, fazla uydurmayı önlemek için 5 katlı çapraz doğrulama tekniği kullanılmıştır. Özetle, sonuçlar en iyi algoritmanın katman boyutu 25 olan Sinir Ağları
olduğunu göstermektedir. Bu çalışmada önerilen bu algoritma %93,85'lik bir test doğruluk oranı elde etmiştir. Araştırmada hesaplama karmaşıklığının olmaması göz önüne alındığında, çeşitli araçlara zahmetsizce dahil edilebilir, böylece hurma türlerinin tespiti kolaylaşır.

References

  • Abi Sen, A. A., Bahbouh, N. M., Alkhodre, A. B., Aldhawi, A. M., Aldham, F. A., & Aljabri, M. I. (2020). A classification algorithm for date fruits. 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom). [CrossRef]
  • Adige, S., Kurban, R., Durmuş, A., & Karaköse, E. (2023). Classification of apple images using support vector machines and deep residual networks. Neural Computing and Applications, 35(16), 12073–12087. [CrossRef]
  • Albarrak, K., Gulzar, Y., Hamid, Y., Mehmood, A., & Soomro, A. B. (2022). A deep learning-based model for date fruit classification. Sustainability, 14(10), 6339. [CrossRef]
  • Alhadhrami, N., Abobakr, A., Alhammadi, A., & Shatnawi, M. (2023). Multiple classifications of date fruit using transfer learning. 2023 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates, 1-5, [CrossRef]
  • Alsirhani, A., Siddiqi, M. H., Mostafa, A. M., Ezz, M., & Mahmoud, A. A. (2023). A novel classification model of date fruit dataset using deep transfer learning. Electronics, 12(3), 665. [CrossRef]
  • Altaheri, H., Alsulaiman, M., & Muhammad, G. (2019). Date fruit classification for robotic harvesting in a natural environment using deep learning. IEEE Access, 7, 117115–117133. [CrossRef]
  • Arshaghi, A., Ashourian, M., & Ghabeli, L. (2023). Potato diseases detection and classification using deep learning methods. Multimedia Tools and Applications, 82(4), 5725–5742. [CrossRef]
  • Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory, 144-152, [CrossRef]
  • Bourquin, J., Schmidli, H., van Hoogevest, P., & Leuenberger, H. (1997). Basic concepts of artificial neural networks (ANN) modeling in the application to pharmaceutical development. Pharmaceutical Development and Technology, 2(2), 95–109. [CrossRef]
  • Chicco, D., Warrens, M. J., & Jurman, G. (2021). The Matthews correlation coefficient (MCC) is more informative than Cohen’s Kappa and Brier score in binary classification assessment. IEEE Access, 9, 78368–78381. [CrossRef]
  • Chomboon, K., Chujai, P., Teerarassammee, P., Kerdprasop, K., & Kerdprasop, N. (2015). An empirical study of distance metrics for k-nearest neighbor algorithm. Proceedings of the 3rd international conference on industrial application engineering, Japan, 280-285, [CrossRef]
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. [CrossRef]
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. [CrossRef]
  • Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. [CrossRef]
  • Dongare, A., Kharde, R., & Kachare, A. D. (2012). Introduction to artificial neural network. International Journal of Engineering and Innovative Technology (IJEIT), 2(1), 189–194.
  • Faisal, M., Alsulaiman, M., Arafah, M., & Mekhtiche, M. A. (2020). IHDS: Intelligent harvesting decision system for date fruit based on maturity stage using deep learning and computer vision. IEEE Access, 8, 167985–167997. [CrossRef]
  • Garcia, L. J., Timmermans, M., Pozuelos, F. J., Ducrot, E., Gillon, M., Delrez, L., Wells, R. D., & Jehin, E. (2021). prose: A Python framework for modular astronomical images processing. Monthly Notices of the Royal Astronomical Society, 509(4), 4817–4828. [CrossRef]
  • Gencturk, B., Arsoy, S., Taspinar, Y. S., Cinar, I., Kursun, R., Yasin, E. T., & Koklu, M. (2023). Detection of hazelnut varieties and development of mobile application with CNN data fusion feature reduction-based models. European Food Research and Technology, 1–14. [CrossRef]
  • Haidar, A., Dong, H., & Mavridis, N. (2012). Image-based date fruit classification. 2012 IV International Congress on Ultra Modern Telecommunications and Control Systems, IEEE, St. Petersburg, Russia, 357-363, [CrossRef]
  • Hecht-Nielsen, R. (1988). Neurocomputing: Picking the human brain. IEEE Spectrum, 25(3), 36–41. [CrossRef]
  • Keller, J. M., Gray, M. R., & Givens, J. A. (1985). A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics, SMC–15(4), 580–585. [CrossRef]
  • Kohonen, T. (1988). An introduction to neural computing. Neural Networks, 1(1), 3–16. [CrossRef]
  • Koklu, M., Cinar, I., & Taspinar, Y. S. (2021a). Classification of rice varieties with deep learning methods. Computers and Electronics in Agriculture, 187, 106285. [CrossRef]
  • Koklu, M., Kursun, R., Taspinar, Y. S., & Cinar, I. (2021b). Classification of date fruits into genetic varieties using image analysis. Mathematical Problems in Engineering, 2021, 1–13. [CrossRef]
  • Koklu, M., & Ozkan, I. A. (2020). Multiclass classification of dry beans using computer vision and machine learning techniques. Computers and Electronics in Agriculture, 174, 105507. [CrossRef]
  • Mammone, A., Turchi, M., & Cristianini, N. (2009). Support vector machines. WIREs Computational Statistics, 1(3), 283–289. [CrossRef]
  • Matthews, B. W. (1975). Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta, 405(2), 442–451. [CrossRef]
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4), 115–133. [CrossRef]
  • Muhammad, G. (2015). Date fruits classification using texture descriptors and shape-size features. Engineering Applications of Artificial Intelligence, 37, 361–367. [CrossRef]
  • Nasiri, A., Taheri-Garavand, A., & Zhang, Y.-D. (2019). Image-based deep learning automated sorting of date fruit. Postharvest Biology and Technology, 153, 133–141. [CrossRef]
  • Osisanwo, F., Akinsola, J., Awodele, O., Hinmikaiye, J., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: Classification and comparison. International Journal of Computer Trends and Technology, 48(3), 128–138. [CrossRef]
  • Ozaltin, O., Coskun, O., Yeniay, O., & Subasi, A. (2022). A deep learning approach for detecting stroke from brain CT images using OzNet. Bioengineering, 9(12), 783. [CrossRef]
  • Ozaltin, O., & Yeniay, Ö. (2023a). Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics, 72(2), 482–499. [CrossRef]
  • Ozaltin, O., & Yeniay, O. (2023b). A novel proposed CNN–SVM architecture for ECG scalograms classification. Soft Computing, 27(8), 4639–4658. [CrossRef]
  • Ozaltin, O., Yeniay, O., & Subasi, A. (2023a). Artificial intelligence-based brain hemorrhage detection. In Accelerating strategic changes for digital transformation in the healthcare industry (pp. 179–199). Elsevier.
  • Ozaltin, O., Yeniay, O., & Subasi, A. (2023b). OzNet: A new deep learning approach for automated classification of COVID-19 computed tomography scans. Big Data. [CrossRef]
  • Rajinikanth, V., Joseph Raj, A. N., Thanaraj, K. P., & Naik, G. R. (2020). A customized VGG19 network with concatenation of deep and hand-crafted features for brain tumor detection. Applied Sciences, 10(10), 3429. [CrossRef]
  • Sharifrazi, D., Alizadehsani, R., Roshanzamir, M., Joloudari, J. H., Shoeibi, A., Jafari, M., Hussain, S., Sani, Z. A., Hasanzadeh, F., Khozeimeh, F., Khosravi, A., Nahavandi, S., Panahiazar, M., Zare, A., Islam, S. M. S., & Acharya, U. R. (2021). Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images. Biomedical Signal Processing and Control, 68, 102622. [CrossRef]
  • Singh, D., Taspinar, Y. S., Kursun, R., Cinar, I., Koklu, M., Ozkan, I. A., & Lee, H.-N. (2022). Classification and analysis of pistachio species with pre-trained deep learning models. Electronics, 11(7), 981. [CrossRef]
  • Wang, J., Yang, Y., & Xia, B. (2019). A simplified Cohen’s Kappa for use in binary classification data annotation tasks. IEEE Access, 7, 164386–164397. [CrossRef]
  • Wróbel, M., Stan-Kłeczek, I., Marciniak, A., Majdański, M., Kowalczyk, S., Nawrot, A., & Cader, J. (2022). Integrated geophysical imaging and remote sensing for enhancing geological interpretation of landslides with uncertainty estimation—A case study from Cisiec, Poland. Remote Sensing, 15(1), 238. [CrossRef]
  • Zhou, Z.-H. (2021). Machine learning. Springer Nature.

Date Fruit Classification by Using Image Features Based on Machine Learning Algorithms

Year 2024, Volume: 55 Issue: 1, 26 - 35, 31.01.2024

Abstract

The date fruit, scientifically known as Phoenix dactylifera, is a significant dietary component due to its high nutritional value and abundance of essential vitamins and minerals. The process of
discerning the classification of this fruit, which exhibits a multitude of variations within its natural domain, needs a specialized skill set. The automated recognition of species based on images of
agricultural goods has gained significant prevalence in recent times. In this objective, the present study employed machine learning algorithms to automatically identify seven types of date
fruit. In the investigation, decision tree, K-nearest neighbor, artificial neural networks, and support vector machine through their different hyperparameters are employed for the purpose of
classifying date fruit. The dataset was divided into ratios of 80% and 20% for training and testing, respectively, and the training process employed the five-fold cross-validation technique to avoid
overfitting. In summary, the results indicate that the best algorithm is neural network with a layer size of 25. In this study, this proposed algorithm achieved a test accuracy rate of 93.85%. Given the absence of computational complexity in the investigation, it can be effortlessly incorporated into diverse tools, thereby facilitating the identification of the types of date fruit.

References

  • Abi Sen, A. A., Bahbouh, N. M., Alkhodre, A. B., Aldhawi, A. M., Aldham, F. A., & Aljabri, M. I. (2020). A classification algorithm for date fruits. 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom). [CrossRef]
  • Adige, S., Kurban, R., Durmuş, A., & Karaköse, E. (2023). Classification of apple images using support vector machines and deep residual networks. Neural Computing and Applications, 35(16), 12073–12087. [CrossRef]
  • Albarrak, K., Gulzar, Y., Hamid, Y., Mehmood, A., & Soomro, A. B. (2022). A deep learning-based model for date fruit classification. Sustainability, 14(10), 6339. [CrossRef]
  • Alhadhrami, N., Abobakr, A., Alhammadi, A., & Shatnawi, M. (2023). Multiple classifications of date fruit using transfer learning. 2023 Advances in Science and Engineering Technology International Conferences (ASET), Dubai, United Arab Emirates, 1-5, [CrossRef]
  • Alsirhani, A., Siddiqi, M. H., Mostafa, A. M., Ezz, M., & Mahmoud, A. A. (2023). A novel classification model of date fruit dataset using deep transfer learning. Electronics, 12(3), 665. [CrossRef]
  • Altaheri, H., Alsulaiman, M., & Muhammad, G. (2019). Date fruit classification for robotic harvesting in a natural environment using deep learning. IEEE Access, 7, 117115–117133. [CrossRef]
  • Arshaghi, A., Ashourian, M., & Ghabeli, L. (2023). Potato diseases detection and classification using deep learning methods. Multimedia Tools and Applications, 82(4), 5725–5742. [CrossRef]
  • Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory, 144-152, [CrossRef]
  • Bourquin, J., Schmidli, H., van Hoogevest, P., & Leuenberger, H. (1997). Basic concepts of artificial neural networks (ANN) modeling in the application to pharmaceutical development. Pharmaceutical Development and Technology, 2(2), 95–109. [CrossRef]
  • Chicco, D., Warrens, M. J., & Jurman, G. (2021). The Matthews correlation coefficient (MCC) is more informative than Cohen’s Kappa and Brier score in binary classification assessment. IEEE Access, 9, 78368–78381. [CrossRef]
  • Chomboon, K., Chujai, P., Teerarassammee, P., Kerdprasop, K., & Kerdprasop, N. (2015). An empirical study of distance metrics for k-nearest neighbor algorithm. Proceedings of the 3rd international conference on industrial application engineering, Japan, 280-285, [CrossRef]
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. [CrossRef]
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. [CrossRef]
  • Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. [CrossRef]
  • Dongare, A., Kharde, R., & Kachare, A. D. (2012). Introduction to artificial neural network. International Journal of Engineering and Innovative Technology (IJEIT), 2(1), 189–194.
  • Faisal, M., Alsulaiman, M., Arafah, M., & Mekhtiche, M. A. (2020). IHDS: Intelligent harvesting decision system for date fruit based on maturity stage using deep learning and computer vision. IEEE Access, 8, 167985–167997. [CrossRef]
  • Garcia, L. J., Timmermans, M., Pozuelos, F. J., Ducrot, E., Gillon, M., Delrez, L., Wells, R. D., & Jehin, E. (2021). prose: A Python framework for modular astronomical images processing. Monthly Notices of the Royal Astronomical Society, 509(4), 4817–4828. [CrossRef]
  • Gencturk, B., Arsoy, S., Taspinar, Y. S., Cinar, I., Kursun, R., Yasin, E. T., & Koklu, M. (2023). Detection of hazelnut varieties and development of mobile application with CNN data fusion feature reduction-based models. European Food Research and Technology, 1–14. [CrossRef]
  • Haidar, A., Dong, H., & Mavridis, N. (2012). Image-based date fruit classification. 2012 IV International Congress on Ultra Modern Telecommunications and Control Systems, IEEE, St. Petersburg, Russia, 357-363, [CrossRef]
  • Hecht-Nielsen, R. (1988). Neurocomputing: Picking the human brain. IEEE Spectrum, 25(3), 36–41. [CrossRef]
  • Keller, J. M., Gray, M. R., & Givens, J. A. (1985). A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics, SMC–15(4), 580–585. [CrossRef]
  • Kohonen, T. (1988). An introduction to neural computing. Neural Networks, 1(1), 3–16. [CrossRef]
  • Koklu, M., Cinar, I., & Taspinar, Y. S. (2021a). Classification of rice varieties with deep learning methods. Computers and Electronics in Agriculture, 187, 106285. [CrossRef]
  • Koklu, M., Kursun, R., Taspinar, Y. S., & Cinar, I. (2021b). Classification of date fruits into genetic varieties using image analysis. Mathematical Problems in Engineering, 2021, 1–13. [CrossRef]
  • Koklu, M., & Ozkan, I. A. (2020). Multiclass classification of dry beans using computer vision and machine learning techniques. Computers and Electronics in Agriculture, 174, 105507. [CrossRef]
  • Mammone, A., Turchi, M., & Cristianini, N. (2009). Support vector machines. WIREs Computational Statistics, 1(3), 283–289. [CrossRef]
  • Matthews, B. W. (1975). Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta, 405(2), 442–451. [CrossRef]
  • McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4), 115–133. [CrossRef]
  • Muhammad, G. (2015). Date fruits classification using texture descriptors and shape-size features. Engineering Applications of Artificial Intelligence, 37, 361–367. [CrossRef]
  • Nasiri, A., Taheri-Garavand, A., & Zhang, Y.-D. (2019). Image-based deep learning automated sorting of date fruit. Postharvest Biology and Technology, 153, 133–141. [CrossRef]
  • Osisanwo, F., Akinsola, J., Awodele, O., Hinmikaiye, J., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: Classification and comparison. International Journal of Computer Trends and Technology, 48(3), 128–138. [CrossRef]
  • Ozaltin, O., Coskun, O., Yeniay, O., & Subasi, A. (2022). A deep learning approach for detecting stroke from brain CT images using OzNet. Bioengineering, 9(12), 783. [CrossRef]
  • Ozaltin, O., & Yeniay, Ö. (2023a). Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics, 72(2), 482–499. [CrossRef]
  • Ozaltin, O., & Yeniay, O. (2023b). A novel proposed CNN–SVM architecture for ECG scalograms classification. Soft Computing, 27(8), 4639–4658. [CrossRef]
  • Ozaltin, O., Yeniay, O., & Subasi, A. (2023a). Artificial intelligence-based brain hemorrhage detection. In Accelerating strategic changes for digital transformation in the healthcare industry (pp. 179–199). Elsevier.
  • Ozaltin, O., Yeniay, O., & Subasi, A. (2023b). OzNet: A new deep learning approach for automated classification of COVID-19 computed tomography scans. Big Data. [CrossRef]
  • Rajinikanth, V., Joseph Raj, A. N., Thanaraj, K. P., & Naik, G. R. (2020). A customized VGG19 network with concatenation of deep and hand-crafted features for brain tumor detection. Applied Sciences, 10(10), 3429. [CrossRef]
  • Sharifrazi, D., Alizadehsani, R., Roshanzamir, M., Joloudari, J. H., Shoeibi, A., Jafari, M., Hussain, S., Sani, Z. A., Hasanzadeh, F., Khozeimeh, F., Khosravi, A., Nahavandi, S., Panahiazar, M., Zare, A., Islam, S. M. S., & Acharya, U. R. (2021). Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images. Biomedical Signal Processing and Control, 68, 102622. [CrossRef]
  • Singh, D., Taspinar, Y. S., Kursun, R., Cinar, I., Koklu, M., Ozkan, I. A., & Lee, H.-N. (2022). Classification and analysis of pistachio species with pre-trained deep learning models. Electronics, 11(7), 981. [CrossRef]
  • Wang, J., Yang, Y., & Xia, B. (2019). A simplified Cohen’s Kappa for use in binary classification data annotation tasks. IEEE Access, 7, 164386–164397. [CrossRef]
  • Wróbel, M., Stan-Kłeczek, I., Marciniak, A., Majdański, M., Kowalczyk, S., Nawrot, A., & Cader, J. (2022). Integrated geophysical imaging and remote sensing for enhancing geological interpretation of landslides with uncertainty estimation—A case study from Cisiec, Poland. Remote Sensing, 15(1), 238. [CrossRef]
  • Zhou, Z.-H. (2021). Machine learning. Springer Nature.

Details

Primary Language English
Subjects Agricultural Engineering (Other)
Journal Section Research Articles
Authors

Öznur ÖZALTIN 0000-0001-9841-1702

Early Pub Date January 29, 2024
Publication Date January 31, 2024
Published in Issue Year 2024 Volume: 55 Issue: 1

Cite

APA ÖZALTIN, Ö. (2024). Date Fruit Classification by Using Image Features Based on Machine Learning Algorithms. Research in Agricultural Sciences, 55(1), 26-35.

Content of this journal is licensed under a Creative Commons Attribution NonCommercial 4.0 International License

29919