Document Type : Research Article
Authors
1 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.
2 Computers and Control Engineering Deptartment, Faculty of Engineering, Tanta University, Tanta, Egypt.
Abstract
Date fruits are considered essential food and the most important agricultural crop in Saudi Arabia. Where Saudi Arabia produces many of the types of dates per year. Collecting large data for date fruits is a difficult task and consumed
time, besides some of the date types are seasonal. Wherein convolutional neural networks (CNN) model needs large datasets to achieve high classification accuracy and avoid the overfitting problem. In this paper, an augmented date fruits dataset was developed using deep convolutional generative adversarial networks techniques (DCGAN). The dataset contains 600 images for three varieties of dates (Sukkari, Suggai and Ajwa). The performance of DCGAN was evaluated using Keras and MobileNet models. An extensive simulation shows the classify using DCGAN with the MobileNet model achieved 88% of accuracy. Whilst 44% for the Keras. Besides, MobileNet achieved better classification in the original dataset.
Keywords
[2] Food and Agriculture Organization Statistics. Top 10 country production of dates, 2018. Accessed 18 February 2022.
[3] Mohammed Faisal, Mansour Alsulaiman, Mohammed Arafah, and Mohamed Amine Mekhtiche. Ihds: Intelligent harvesting decision system for date fruit based on maturity stage using deep learning and computer vision. IEEE Access, 8:167985–167997, 2020.
[4] National Center of Palm Dates. Report of the national center for palms and dates 2018, 2018. Accessed 18 February 2022.
[5] O Aiadi, B Khaldi, and ML Kherfi. An automated system for date fruit recognition through images= un systeme automatique pour la reconnaissance des dattes a partir des images. Revue des Bioressources, 257(5757):1–8, 2016.
[6] Hamdi Altaheri, Mansour Alsulaiman, Ghulam Muhammad, Syed Umar Amin, Mohamed Bencherif, and Mohamed Mekhtiche. Date fruit dataset for intelligent harvesting. Data in brief, 26:104514, 2019.
[7] J Arun Pandian, G Geetharamani, and B Annette. Data augmentation on plant leaf disease image dataset using image manipulation and deep learning techniques. In 2019 IEEE 9th International Conference on Advanced Computing(IACC), pages 199–204. IEEE, 2019.
[8] Rutu Gandhi, Shubham Nimbalkar, Nandita Yelamanchili, and Surabhi Ponkshe. Plant disease detection using cnns and gans as an augmentative approach. In 2018 IEEE International Conference on Innovative Research and Development (ICIRD), pages 1–5. IEEE, 2018.
[9] Min Zhang, Shuheng Liu, Fangyun Yang, and Ji Liu. Classification of canker on small datasets using improved deep convolutional generative adversarial networks. IEEE Access, 7:49680–49690, 2019.
[10] Endang Suryawati, Vicky Zilvan, R Sandra Yuwana, Ana Heryana, Dadan Rohdiana, and Hilman F Pardede. Deep convolutional adversarial network-based feature learning for tea clones identifications. In 2019 3rd International Conference on Informatics and Computational Sciences(ICICoS), pages 1–5. IEEE, 2019.
[11] Amin Nasiri, Amin Taheri-Garavand, and YuDong Zhang. Image-based deep learning automated sorting of date fruit. Postharvest biology and technology, 153:133–141, 2019.
[12] Dina M. Ibrahim Nada M. Elshennawy. Improving date fruit classification using cyclegangenerated dataset. Computer Modeling in Engineering & Sciences, 131(1):331–348, 2022.
[13] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.
[14] Rikiya Yamashita, Mizuho Nishio, Richard Kinh Gian Do, and Kaori Togashi. Convolutional neural networks: an overview and application in radiology. Insights into imaging, 9(4):611–629, 2018.
[15] Jos´e Naranjo-Torres, Marco Mora, Ruber Hern´andez-Garc´ıa, Ricardo J Barrientos, Claudio Fredes, and Andres Valenzuela. A review of convolutional neural network applied to fruit image processing. Applied Sciences, 10(10):3443,2020.
[16] Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.
[17] S Ashwinkumar, S Rajagopal, V Manimaran, and B Jegajothi. Automated plant leaf disease detection and classification using optimal mobilenet based convolutional neural networks. Materials Today: Proceedings, 2021.
[18] Rika Sustika, Agus Subekti, Hilman F Pardede, Endang Suryawati, Oka Mahendra, and Sandra Yuwana. Evaluation of deep convolutional neural network architectures for strawberry quality inspection. Int. J. Eng. Technol, 7(4):75–80, 2018.
[19] Raheel Siddiqi. Comparative performance of various deep learning based models in fruit image classification. In Proceedings of the 11th International Conference on Advances in Information Technology, pages 1–9, 2020.
[20] Jing Zhang, Lu Chen, Li Zhuo, Xi Liang, and Jiafeng Li. An efficient hyperspectral image retrieval method: Deep spectral-spatial feature extraction with dcgan and dimensionality reduction using t-sne-based nm hashing. Remote Sensing, 10(2):271, 2018.
[21] Zehua Zhang, Xiangqian Liu, and Yan Cui. Multi-phase offline signature verification system using deep convolutional generative adversarial networks. In 2016 9th international Symposium on Computational Intelligence and Design (ISCID), volume 2, pages 103–107. IEEE, 2016.
[22] Nikhil Ketkar and Eder Santana. Deep learning with python, volume 1. Springer, 2017.
[23] Zheng Qin, Zhaoning Zhang, Xiaotao Chen, Changjian Wang, and Yuxing Peng. Fdmobilenet: Improved mobilenet with a fast downsampling strategy. In 2018 25th IEEE International Conference on Image Processing (ICIP), pages 1363–1367. IEEE, 2018.
[24] Quan Huu Cap, Hiroyuki Uga, Satoshi Kagiwada, and Hitoshi Iyatomi. Leafgan: An effective data augmentation method for practical plant disease diagnosis. IEEE Transactions on Automation Science and Engineering, 2020.
[25] Rongcheng Sun, Min Zhang, Kun Yang, and Ji Liu. Data enhancement for plant disease classification using generated lesions. Applied Sciences, 10(2):466, 2020.
[26] Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. Privacy risk in machine learning: Analyzing the connection to overfitting. In 2018 IEEE 31st Computer Security Foundations Symposium (CSF), pages 268–282. IEEE, 2018.
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