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  2. Vol 5, No 2 (2024)
  3. Al-Kharaz

Innovative Date Fruit Classifier Based on Scatter Wavelet and Stacking Ensemble

Ali A. Al-Kharaz, Ahmed B. A. Alwahhab, Vian Sabeeh

Abstract


Dates are essential fruits loaded with vital nutrients that keep bones healthy and prevent bone-related disorders. Approximately 8.46 million tons of different types of dates are cultivated and produced annually around the globe. There are more than 400 types of dates that are time-consuming and expensive to produce. Classifying them using conventional methods is labor-intensive, and this is one of the biggest problems for the date industry. Dataset fruit classification plays a vital role in the food industry. Dates can be classified from a luxury class to a less quality class. Accordingly, the food industry needs an automotive date fruit classifier that can work in food factories. This study proposes a pioneering method to classify date fruit that relies on extracting features from the texture of dates using Scattering Wavelet Transformation (SWT). The SWT yields in numeric coefficients were found to be immune to the deformation of invariants. This feature set trains an ensemble classifier that combines a voting mechanism to eliminate overfitting. The ensemble classifier consists of a random forest, a support vector machine classifier, and a logistic regression hyper-learner. Our novel approach was tested on two benchmarked datasets. The first data set scored F1 between 0.95 and 1.0 at the same time. The second dataset registered F1 between 0.96 and 1.0 in each of the 20 date classes. Some dates are close to each other in texture, resulting in high false positives or recall, causing a lower F1 score accuracy degree. The novelty of this approach comes from the featured representative of each date class, relying on the texture of the fruit as a discriminative feature, not on the fruit shape or color, which may not be robust enough as distinguishable features, especially in date classes that are close to each other in shape.

 

Doi: 10.28991/HIJ-2024-05-02-010

Full Text: PDF


Keywords


Dates; Scatter Wavelet Transform; Stacking Ensemble Learning; Random Forest Classifier; Linear Support Vector Machine; Performance Metrics.

References


Albarrak, K., Gulzar, Y., Hamid, Y., Mehmood, A., & Soomro, A. B. (2022). A Deep Learning-Based Model for Date Fruit Classification. Sustainability (Switzerland), 14(10), 6339. doi:10.3390/su14106339.

Muhammad, G. (2015). Date fruits classification using texture descriptors and shape-size features. Engineering Applications of Artificial Intelligence, 37, 361–367. doi:10.1016/j.engappai.2014.10.001.

Haidar, A., Dong, H., & Mavridis, N. (2012). Image-based date fruit classification. International Congress on Ultra-Modern Telecommunications and Control Systems and Workshops, 357–363. doi:10.1109/ICUMT.2012.6459693.

Alharbi, K. L., Raman, J., & Shin, H. J. (2021). Date fruit and seed in nutricosmetics. Cosmetics, 8(3), 59. doi:10.3390/cosmetics8030059.

Khayer, M. A., Hasan, M. S., & Sattar, A. (2021). Arabian date classification using CNN algorithm with various pre-trained models. Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021, 1431–1436. doi:10.1109/ICICV50876.2021.9388413.

Muhammad, G. (2014). Automatic date fruit classification by using local texture descriptors and shape-size features. Proceedings - UKSim-AMSS 8th European Modelling Symposium on Computer Modelling and Simulation, EMS 2014, 174–179. doi:10.1109/EMS.2014.63.

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 (Switzerland), 12(3), 665. doi:10.3390/electronics12030665.

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. doi:10.1016/j.postharvbio.2019.04.003.

Momeny, M., Jahanbakhshi, A., Jafarnezhad, K., & Zhang, Y. D. (2020). Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach. Postharvest Biology and Technology, 166, 111204. doi:10.1016/j.postharvbio.2020.111204.

Zhang, D., Lee, D. J., Tippetts, B. J., & Lillywhite, K. D. (2014). Date maturity and quality evaluation using color distribution analysis and back projection. Journal of Food Engineering, 131, 161–169. doi:10.1016/j.jfoodeng.2014.02.002.

Ammari, A. C., Khriji, L., & Awadalla, M. (2020). HW/SW Co-’esign for Dates Classification on Xilinx Zynq SoC. Conference of Open Innovation Association, FRUCT, 2020-April, 10–15. doi:10.23919/FRUCT48808.2020.9087548.

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. doi:10.1109/ACCESS.2019.2936536.

Ishikawa, T., Hayashi, A., Nagamatsu, S., Kyutoku, Y., Dan, I., Wada, T., Oku, K., Saeki, Y., Uto, T., Tanabata, T., Isobe, S., & Kochi, N. (2018). Classification of strawberry fruit shape by machine learning. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(2), 463–470. doi:10.5194/isprs-archives-XLII-2-463-2018.

Rybacki, P., Niemann, J., Derouiche, S., Chetehouna, S., Boulaares, I., Seghir, N. M., Diatta, J., & Osuch, A. (2024). Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits (Phoenix dactylifera L.). Sensors, 24(2), 558. doi:10.3390/s24020558.

Fadel, M. (2007). Date fruits classification using probabilistic neural networks. Agricultural Engineering International: CIGR Journal, 9, 1-11.

M.Alrajeh, K., & A. A. Alzohairy, T. (2012). Date Fruits Classification using MLP and RBF Neural Networks. International Journal of Computer Applications, 41(10), 36–41. doi:10.5120/5579-7686.

Sahidullah, M., Nayan, N. M., Morshed, M. S., Hossain, M. M., & Islam, M. U. (2023). Date Fruit Classification with Machine Learning and Explainable Artificial Intelligence. International Journal of Computer Applications, 184(50), 1–5. doi:10.5120/ijca2023922617.

ÖZALTIN, Ö. (2024). Date Fruit Classification by Using Image Features Based on Machine Learning Algorithms. Research in Agricultural Sciences, 55(1), 26–35. doi:10.5152/auaf.2024.23171.

Ghazal, S., Qureshi, W. S., Khan, U. S., Iqbal, J., Rashid, N., & Tiwana, M. I. (2021). Analysis of visual features and classifiers for Fruit classification problem. Computers and Electronics in Agriculture, 187, 106267. doi:10.1016/j.compag.2021.106267.

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. Proceedings of the 7th International Conference on Computing for Sustainable Global Development, INDIACom 2020, 235–239. doi:10.23919/INDIACom49435.2020.9083706.

Koklu, M., Kursun, R., Taspinar, Y. S., & Cinar, I. (2021). Classification of Date Fruits into Genetic Varieties Using Image Analysis. Mathematical Problems in Engineering, 2021, 1–13. doi:10.1155/2021/4793293.

Noutfia, Y., & Ropelewska, E. (2023). Innovative Models Built Based on Image Textures Using Traditional Machine Learning Algorithms for Distinguishing Different Varieties of Moroccan Date Palm Fruit (Phoenix dactylifera L.). Agriculture (Switzerland), 13(1), 26. doi:10.3390/agriculture13010026.

Magsi, A., Ahmed Mahar, J., & Danwar, S. H. (2019). Date Fruit Recognition using Feature Extraction Techniques and Deep Convolutional Neural Network. Indian Journal of Science and Technology, 12(32), 1–12. doi:10.17485/ijst/2019/v12i32/146441.

Almomen, M., Al-Saeed, M., & Ahmad, H. F. (2023). Date Fruit Classification Based on Surface Quality Using Convolutional Neural Network Models. Applied Sciences (Switzerland), 13(13), 7821. doi:10.3390/app13137821.

Alhamdan, W. S. N., & Howe, J. M. (2021). Classification of Date Fruits in a Controlled Environment Using Convolutional Neural Networks. Advances in Intelligent Systems and Computing, 1339, 154–163. doi:10.1007/978-3-030-69717-4_16.

Al-Sabaawi, A., Hasan, R. I., Fadhel, M. A., Al-Shamma, O., & Alzubaidi, L. (2021). Employment of Pre-trained Deep Learning Models for Date Classification: A Comparative Study. Intelligent Systems Design and Applications: 20th International Conference on Intelligent Systems Design and Applications (ISDA 2020), December 12-15, 2020, 181–189. doi:10.1007/978-3-030-71187-0_17.

Alresheedi, K. M., Aladhadh, S., Khan, R. U., & Qamar, A. M. (2022). Dates fruit recognition: From classical fusion to deep learning. Computer Systems Science and Engineering, 40(1), 151–166. doi:10.32604/CSSE.2022.017931.

Ibrahim, D. M., & Elshennawy, N. M. (2022). Improving Date Fruit Classification Using CycleGAN-Generated Dataset. CMES - Computer Modeling in Engineering and Sciences, 130(3), 331-348. doi:10.32604/cmes.2022.016419.

Nadhif, M. F., & Dwiasnati, S. (2023). Classification of Date Fruit Types Using CNN Algorithm Based on Type. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(1), 36–42. doi:10.57152/malcom.v3i1.724.

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. doi:10.1109/ACCESS.2020.3023894.

Indriani, O. R., Kusuma, E. J., Sari, C. A., Rachmawanto, E. H., & Setiadi, D. R. I. M. (2017). Tomatoes classification using K-NN based on GLCM and HSV color space. Proceedings - 2017 International Conference on Innovative and Creative Information Technology: Computational Intelligence and IoT, ICITech 2017, 2018-January, 1–6. doi:10.1109/INNOCIT.2017.8319133.

Mallat, S. (2012). Group Invariant Scattering. Communications on Pure and Applied Mathematics, 65(10), 1331–1398. doi:10.1002/cpa.21413.

Bruna, J., & Mallat, S. (2013). Invariant scattering convolution networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1872–1886. doi:10.1109/TPAMI.2012.230.

Aiadi, O., Khaldi, B., Kherfi, M. L., Mekhalfi, M. L., & Alharbi, A. (2022). Date Fruit Sorting Based on Deep Learning and Discriminant Correlation Analysis. IEEE Access, 10, 79655–79668. doi:10.1109/ACCESS.2022.3194550.

Jin, Y., & Duan, Y. (2020). Wavelet scattering network-based machine learning for ground penetrating radar imaging: Application in pipeline identification. Remote Sensing, 12(21), 1–24. doi:10.3390/rs12213655.

Soro, B., & Lee, C. (2019). A wavelet scattering feature extraction approach for deep neural network based indoor fingerprinting localization. Sensors (Switzerland), 19(8), 1790. doi:10.3390/s19081790.

Mienye, I. D., & Sun, Y. (2022). A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects. IEEE Access, 10, 99129–99149. doi:10.1109/ACCESS.2022.3207287.

Kumar, M., Singhal, S., Shekhar, S., Sharma, B., & Srivastava, G. (2022). Optimized Stacking Ensemble Learning Model for Breast Cancer Detection and Classification Using Machine Learning. Sustainability (Switzerland), 14(21), 13998. doi:10.3390/su142113998.

Taspinar, Y. S., Cinar, I., & Koklu, M. (2022). Classification by a stacking model using CNN features for COVID-19 infection diagnosis. Journal of X-Ray Science and Technology, 30(1), 73–88. doi:10.3233/XST-211031.

Alhamdan, W. S. N. (2024). Date Fruit Image Dataset in Controlled Environment: Single date fruit images taken in a controlled environment. Available online: https://www.kaggle.com/datasets/wadhasnalhamdan/date-fruit-image-dataset-in-controlled-environment?resource=download (accessed on March 2024).


Full Text: PDF

DOI: 10.28991/HIJ-2024-05-02-010

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Copyright (c) 2024 Ali Abdulmunim Al-kharaz, Ahmed Bahaaulddin A. Alwahhab, Vian Talal Sabeeh