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. In 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 235–239). IEEE.
- Al-Dashti, Y. A., Holt, R. R., Keen, C. L., & Hackman, R. M. (2021). Date palm fruit (Phoenix dactylifera): Effects on vascular health and future research directions. International journal of molecular sciences, 22(9), 4665.
- Altaheri, H., Alsulaiman, M., & Muhammad, G. (2019). Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning. IEEE Access, vol. 7, pp. 117115–117133.
- Alturki, A. S., Islam, M., Alsharekh, M. F., Almanee, M. S., & Ibrahim, A. H. (2020). Date fruits grading and sorting classification algorithm using colors and shape features. Int. J. Eng. Res. Technol, 13(8), 1917–1920.
- Aslan, M. F., Sabanci, K., Durdu, A., & Unlersen, M. F. (2022). COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization. Computers in biology and medicine, 142, 105244.
- Aslan, M. F., Unlersen, M. F., Sabanci, K., & Durdu, A. (2021). CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection. Applied Soft Computing, 98, 106912.
- Cong, Q., Feng, Z., Li, F., Xiang, Y., Rao, G., Tao, C. (2018). XA-BiLSTM: a deep learning approach for depression detection in imbalanced data. In 2018 IEEE international conference on bioinformatics and biomedicine (BIBM) (pp. 1624–1627). IEEE.
- Faisal, M., Albogamy, F., Elgibreen, H., Algabri, M., & Alqershi, F. A. (2020). Deep learning and computer vision for estimating date fruits type, maturity level, and weight. IEEE Access, 8, 206770-206782.
- FAO STAT. https://www.fao.org/faostat/fr/#data/QCL (consulted by 21 October 2022).
- Ghnimi, S., Umer, S., Karim, A., & Kamal-Eldin, A. (2017). Date fruit (Phoenix dactylifera L.): An underutilized food seeking industrial valorization. NFS journal, 6, 1–10.
- He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.
- Hossain, M.S., Muhammad, G., & Amin, S.U. (2016). Improving consumer satisfaction in smart cities using edge computing and caching: A case study of date fruits classification," Future Generation Computer Systems, vol. 88, pp. 333–341.
- Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
- Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700–4708).
- Idowu, A. T., Igiehon, O. O., Adekoya, A. E., & Idowu, S. (2020). Dates palm fruits: A review of their nutritional components, bioactivities and functional food applications. AIMS Agriculture and Food, 5(4), 734–755.
- Kamal-Eldin, A., & Ghnimi, S. (2018). Classification of date fruit (Phoenix dactylifera, L.) based on chemometric analysis with multivariate approach. Journal of Food Measurement and Characterization, 12(2), 1020–1027.
- Khalid, S., Khalid, N., Khan, R. S., Ahmed, H., & Ahmad, A. (2017). A review on chemistry and pharmacology of Ajwa date fruit and pit. Trends in food science & technology, 63, 60–69.
- Kira, K., & Rendell, L. A. (1992). A practical approach to feature selection. In Machine learning proceedings 1992 (pp. 249–256). Morgan Kaufmann.
- 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.
- Manickavasagan, A., Essa, M. M., & Sukumar, E. (2012). Dates: production, processing, food, and medicinal values. CRC Press.
- Muhammad, G., (2014). Automatic date fruit classification by using local texture descriptors and shape-size features. In 2014 European Modelling Symposium, 174–179, IEEE.
- Muhammad, G., (2015). Date fruits classification using texture descriptors and shape-size features. Engineering Applications of Artificial Intelligence, 37, 361–367.
- 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.
- Noutfia, Y., & Ropelewska, E. (2023a). Comprehensive Characterization of Date Palm Fruit ‘Mejhoul’ (Phoenix dactylifera L.) Using Image Analysis and Quality Attribute Measurements. Agriculture, 13(1), 74.
- Noutfia, Y., & Ropelewska, E. (2023b). 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, 13(1), 26.
- Noutfia, Y., Alem, C., & Filali Zegzouti, Y. (2019). Assessment of physico-chemical and sensory properties of two date (Phoenix dactylifera L.) cultivars under commercial cold storage conditions. Journal of Food Processing and Preservation, 43(12), e14228.
- O'Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
- Otsu, N. 1979. A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62–66.
- Pérez-Pérez, B.D., Garcia Vazquez, J. P., Salomón-Torres, R. (2021). Evaluation of convolutional neural networks’ hyperparameters with transfer learning to determine sorting of ripe medjool dates. Agriculture, 11(2), 115.
- Rahmani, A. H., Aly, S. M., Ali, H., Babiker, A. Y., & Srikar, S. (2014). Therapeutic effects of date fruits (Phoenix dactylifera) in the prevention of diseases via modulation of anti-inflammatory, anti-oxidant and anti-tumour activity. International journal of clinical and experimental medicine, 7(3), 483.
- Ropelewska, E., Cai, X., Zhang, Z., Sabanci, K., Aslan, M. F. (2022b). Benchmarking machine learning approaches to evaluate the cultivar differentiation of plum (Prunus domestica L.) kernels. Agriculture, 12(2), 285.
- Ropelewska, E., Sabanci, K., & Aslan, M. F. (2022a). Authentication of tomato (Solanum lycopersicum L.) cultivars using discriminative models based on texture parameters of flesh and skin images. European Food Research and Technology, 248(8), 1959–1976.
- Ropelewska, E., Sabanci, K., & Aslan, M.F. (2021). Discriminative power of geometric parameters of different cultivars of sour cherry pits determined using machine learning. Agriculture, 11(12), 1212.
- Sabanci, K., Aslan, M. F., & Durdu, A. (2020). Bread and durum wheat classification using wavelet-based image fusion. Journal of the Science of Food and Agriculture, 100(15), 5577–5585.
- Sabanci, K., Aslan, M. F., Ropelewska, E., Unlersen, M. F., & Durdu, A. (2022). A novel convolutional-recurrent hybrid network for sunn pest–damaged wheat grain detection. Food Analytical Methods, 15(6), 1748–1760.
- Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510–4520).
- Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... and Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1–9).
- Thareja, G., Mathew, S., Mathew, L. S., Mohamoud, Y. A., Suhre, K., & Malek, J. A. (2018). Genotypingby-sequencing identifies date palm clone preference in agronomics of the State of Qatar. PLoS one, 13(12), e0207299.
- Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018). Relief-based feature selection: Introduction and review. Journal of biomedical informatics, 85, 189–203.
- Zielinska, M., Ropelewska, E., & Markowski, M. (2017). Thermophysical properties of raw, hot-air and microwave-vacuum dried cranberry fruits (Vaccinium macrocarpon). LWT-Food Science and Technology, 85, 204–211.