Transfer Learning-Based Approach for Citrus Disease Detection

Rahman, Md. Ashekur; Amin, Samadul; Alvi, Md. Mahbub-Ul- Huq

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dc.contributor.author	Rahman, Md. Ashekur
dc.contributor.author	Amin, Samadul
dc.contributor.author	Alvi, Md. Mahbub-Ul- Huq
dc.date.accessioned	2023-05-04T04:42:14Z
dc.date.available	2023-05-04T04:42:14Z
dc.date.issued	2022-05-30
dc.identifier.citation	1. Jahanbakhshi, Ahmad, et al. “Classification of Sour Lemons Based on Apparent Defects Using Stochastic Pooling Mechanism in Deep Convolutional Neural Networks.” Scientia Horticulturae, Elsevier, 17 Dec. 2019, https://www.sciencedirect.com/science/article/abs/pii/S0304423819310192. 2. K.Lalitha, K.Muthulakshmi, A.Vinothini(2015) “ Proficient acquaintance based system for citrus leaf disease recognition and categorization”, 3. Bulanon, Duke M., et al. “Citrus Black Spot Detection Using Hyperspectral Image Analysis.” Agricultural Engineering International: CIGR Journal, 2013, https://cigrjournal.org/index.php/Ejounral/article/view/2382. 4. Kotze, Johannes Marthinus. “Studies on the Black Spot Disease of Citrus Caused by Guignardia Citricarpa Kiely with Particular Reference to Its Epiphytology and Control at Letaba.” UPSpace Home, University of Pretoria, 18 Jan. 2007, https://repository.up.ac.za/handle/2263/23891. 5. Lins, Emery C., et al. “Detection of Citrus Canker in Citrus Plants Using Laser-Induced Fluorescence Spectroscopy - Precision Agriculture.” SpringerLink, Springer US, 17 June 2009, https://link.springer.com/article/10.1007/s11119-009-9124-2. 6. Rauf, Hafiz Tayyab, et al. “A Citrus Fruits and Leaves Dataset for Detection and Classification of Citrus Diseases through Machine Learning.” Data in Brief, vol. 26, 2019, p. 104340., https://doi.org/10.1016/j.dib.2019.104340. 7. Sunny, Shoby and Dr. M. P. Indra Gandhi. “An Efficient Citrus Canker Detection Method based on Contrast Limited Adaptive Histogram Equalization Enhancement.” (2018). 8. Thangadurai, K. & Padmavathi, K. (2019). Citrus Canker Disease Detection Using Genetic Algorithm in Citrus Plants. 9. Sharif, Muhammad, et al. “Detection and Classification of Citrus Diseases in Agriculture Based on Optimized Weighted Segmentation and Feature Selection.” Computers and Electronics in Agriculture, vol. 150, 2018, pp. 220–234., https://doi.org/10.1016/j.compag.2018.04.023. 10. Doh, Benjamin, et al. “Automatic Citrus Fruit Disease Detection by Phenotyping Using Machine Learning.” 2019 25th International Conference on Automation and Computing (ICAC), 2019, https://doi.org/10.23919/iconac.2019.8895102. 46 11. Pan, Wenyan, et al. “A Smart Mobile Diagnosis System for Citrus Diseases Based on Densely Connected Convolutional Networks.” IEEE Access, vol. 7, 2019, pp. 87534– 87542., https://doi.org/10.1109/access.2019.2924973. 12. Russakovsky, Olga, et al. “Imagenet Large Scale Visual Recognition Challenge.” International Journal of Computer Vision, vol. 115, no. 3, 2015, pp. 211–252., https://doi.org/10.1007/s11263-015-0816-y. 13. Krizhevsky, Alex, et al. “ImageNet Classification with Deep Convolutional Neural Networks.” Communications of the ACM, vol. 60, no. 6, 2017, pp. 84–90., https://doi.org/10.1145/3065386. 14. Alzubaidi, Laith, et al. “Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions.” Journal of Big Data, vol. 8, no. 1, 2021, https://doi.org/10.1186/s40537-021-00444-8. 15. Howard, Andrew G., et al. “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.” ArXiv.org, 17 Apr. 2017, https://arxiv.org/abs/1704.04861. 16. Huang, Gao, et al. “Densely Connected Convolutional Networks.” ArXiv.org, 28 Jan. 2018, https://arxiv.org/abs/1608.06993. 17. Szegedy, Christian, et al. “Inception-V4, Inception-Resnet and the Impact of Residual Connections on Learning.” ArXiv.org, 23 Aug. 2016, https://arxiv.org/abs//1602.07261. 18. Zoph, Barret, et al. “Learning Transferable Architectures for Scalable Image Recognition.” ArXiv.org, 11 Apr. 2018, https://arxiv.org/abs/1707.07012. 19. Simonyan, Karen, and Andrew Zisserman. “Very Deep Convolutional Networks for LargeScale Image Recognition.” ArXiv.org, 10 Apr. 2015, https://arxiv.org/abs/1409.1556. 20. Czakon, Jakub. “24 Evaluation Metrics for Binary Classification (and When to Use Them).” Neptune.ai, 5 Jan. 2022, https://neptune.ai/blog/evaluation-metrics-binaryclassification. 21. Brownlee, Jason. “How to Calculate Precision, Recall, and F-Measure for Imbalanced Classification.” Machine Learning Mastery, 1 Aug. 2020, https://machinelearningmastery.com/precision-recall-and-f-measure-for-imbalancedclassification/. 22. Aniruddha. “AUC-Roc Curve in Machine Learning Clearly Explained.” Analytics Vidhya, 20 July 2020, https://www.analyticsvidhya.com/blog/2020/06/auc-roc-curve-machinelearning/. 47 23. Fao. “Citrus Fruit Fresh and Processed Statistical Bulletin 2020.” Policy Commons, FAO, 13 Sept. 2021, https://policycommons.net/artifacts/1813113/citrus-fruit-fresh-andprocessed-statistical-bulletin-2020/2549212/. 24. https://www.sciencedirect.com/science/article/abs/pii/S0168169917306373. 25. Prevalence and Severity of Different Citrus Diseases in Sylhet Region. https://www.researchgate.net/publication/341319002_Prevalence_and_severity_of_differ ent_citrus_diseases_in_Sylhet_region. 26. Uddin, H., Latif, A., Huq, F., Mia, A. T., Latif, A. and Ahmed, S. (2014). Pest Risk Analysis (PRA) of Citrus under Strengthening Phytosanitary Capacity in Bangladesh Project (SPCB), DAE. 27. Automatic Citrus Fruit Disease Detection by Phenotyping Using Machine ... https://www.researchgate.net/publication/337212837_Automatic_Citrus_Fruit_Disease_ Detection_by_Phenotyping_Using_Machine_Learning. 28.G. Csurka, “A comprehensive survey on domain adaptation for visual applications,” in Domain adaptation in computer vision applications. Springer, 2017, pp. 1–35. 29.A. Farahani, S. Voghoei, K. Rasheed, and H. R. Arabnia, “A brief review of domain adaptation,” arXiv preprint arXiv:2010.03978, 2020. 30.Farahani, Abolfazl, et al. “A Concise Review of Transfer Learning.” ArXiv.org, 5 Apr. 2021, https://arxiv.org/abs/2104.02144v1. 31.BAR, Y. et al. Deep learning with non-medical training used for chest pathology identification. In: SPIE medical imaging, Orlando, v. 9414, p. 7, 2015. 32.BUETTI-DINHA, A. et al. Deep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition. Biotechnology Reports, v. 22, p. 108–119. 2019. Available in: https://doi.org/10.1016/j.btre.2019.e00321. Access: 22 Ago. 2020. 48 33.GAD, A. F. Practical computer vision applications using deep learning with CNNs: With Detailed Examples in Python Using TensorFlow and Kivy. New York: Apress, 2018. E-book. ISBN: 978-1-4842-4167-7Available in: https://doi.org/10.1007/978-1-4842-4167-7. Access: 29 ago. 2020. 34.A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012. 35.O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. Imagenet large scale visual recognition challenge. 2014. 36.K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015. 37.C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.00567, 2015. 38.M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane,´ R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. War- ´ den, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org. 39.J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, A. Senior, P. Tucker, K. Yang, Q. V. Le, et al. Large scale distributed deep networks. In Advances in Neural Information Processing Systems, pages 1223–1231, 2012. 40.Szegedy, Christian, et al. “Inception-V4, Inception-Resnet and the Impact of Residual Connections on Learning.” ArXiv.org, 23 Aug. 2016, https://arxiv.org/abs/1602.07261v2. 41.F. Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/1874
dc.description	Supervised by Prof.Dr. Md. Ashraful Hoque, Department of Electrical and Electronic Engineering (EEE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2022.	en_US
dc.description.abstract	Fruit diseases have a devastating impact on the food economy, wreaking havoc on the global food business. Therefore, in agriculture and food security, disease detection and diagnosis systems must be efficient and dependable. Technology supported by computers can be used to detect illnesses. Convolutional neural network (CNN) approaches have recently demonstrated impressive performance in image categorization applications. CNN can operate relatively efficiently with high-volume datasets and extracts more circumstantial features. This paper offers a strategy that employs deep convolutional neural networks with transfer learning to identify and categorize citrus crop diseases. Useful features were identified from fine-tuned, pre-trained deep learning models that were retrained using an image dataset of citrus fruits with infections. On a large dataset (ImageNet), the InceptionResNetV2, DenseNet169, MobileNet, NasNetLarge, NASNetMobile, and VGG19 models have been pre-trained to increase prediction accuracy. The VGG19 model has a classification accuracy of 97.54 percent, which is superior to current methods.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Electronic Engineering, Islamic University of Technology (IUT) The Organization of Islamic Cooperation (OIC) Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	Transfer Learning, Machine Learning, Convolutional neural network, Image data, Citrus, Disease, Black Spot, Canker, ImageNet, InceptionResNetV2, DenseNet169, MobileNet, NasNetLarge, NASNetMobile, and VGG19.	en_US
dc.title	Transfer Learning-Based Approach for Citrus Disease Detection	en_US
dc.type	Thesis	en_US