Дослідження методів діагностики захворювань рослин за допомогою глибокого навчання

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dc.citation.epage48
dc.citation.issue1
dc.citation.journalTitleКомп’ютерні системи проектування. Теорія і практика
dc.citation.spage37
dc.contributor.affiliationНаціональний університет “Львівська політехніка”
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorГуменюк, Роман
dc.contributor.authorПопович, Іван
dc.contributor.authorHumeniuk, Roman
dc.contributor.authorPopovych, Ivan
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2025-03-11T09:52:38Z
dc.date.created2024-02-27
dc.date.issued2024-02-27
dc.description.abstractУ статті досліджується використання згорткових нейронних мереж (CNN) у процесі діагностики та ідентифікації хвороб та шкідників рослин. Розглянуто різні методи діагностики хвороб рослин, особливості наборів даних, а також проблеми, що існують у даному напрямку досліджень. У статті обговорюється п'ятикрокова методологія для визначення хвороб рослин, включаючи збір даних, попередню обробку, сегментацію, виділення ознак та класифікацію. Досліджуються різні архітектури глибокого навчання, які дозволяють здійснювати швидку та ефективну діагностику хвороб рослин. Виокремлюються інноваційні тенденції та проблеми у даному напрямку, що потребують подальшого дослідження та уваги від наукової спільноти.
dc.description.abstractThe article explores the use of convolutional neural networks (CNNs) in the diagnosis and identification of plant diseases and pests. Various methods of plant disease diagnosis, features of datasets, and challenges in this research direction are considered. The article discusses a five-step methodology for determining plant diseases, including data collection, preprocessing, segmentation, feature extraction, and classification. Different deep learning architectures enabling fast and efficient plant disease diagnosis are investigated. Innovative trends and issues in this field requiring further research and attention from the scientific community are highlighted.
dc.format.extent37-48
dc.format.pages12
dc.identifier.citationГуменюк Р. Дослідження методів діагностики захворювань рослин за допомогою глибокого навчання / Роман Гуменюк, Іван Попович // Комп’ютерні системи проектування. Теорія і практика. — Львів : Видавництво Львівської політехніки, 2024. — Том 6. — № 1. — С. 37–48.
dc.identifier.citationenHumeniuk R. Research of plant disease diagnostic methods using deep learning / Roman Humeniuk, Ivan Popovych // Computer Systems of Design. Theory and Practice. — Lviv : Lviv Politechnic Publishing House, 2024. — Vol 6. — No 1. — P. 37–48.
dc.identifier.doidoi.org/10.23939/cds2024.01.037
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/64119
dc.language.isouk
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofКомп’ютерні системи проектування. Теорія і практика, 1 (6), 2024
dc.relation.ispartofComputer Systems of Design. Theory and Practice, 1 (6), 2024
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dc.relation.referencesen[1] F. Fina, P. Birch, R. Young, J. Obu, B. Faithpraise, and C. Chatwin, "Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters," International Journal of Advanced Biotechnology and Research, vol. 4, pp. 189-199, 2013.
dc.relation.referencesen[2] M. H. Saleem, S. Khanchi, P. Potgieter, and K. M. Arif, "Image-based plant disease identification by deep learning meta-architectures," Plants, vol. 9, no. 11, pp. 1451, 2020. https://doi.org/10.3390/plants9111451
dc.relation.referencesen[3] J. Amara, B. Bouaziz, and A. Algergawy, "A Deep Learning-based Approach for Banana Leaf Diseases Classification," 2017. Available at: https://dl.gi.de/handle/20.500.12116/944
dc.relation.referencesen[4] K. P. Panigrahi, H. Das, A. K. Sahoo, and S. C. Moharana, "Maize leaf disease detection and classification using machine learning algorithms," in Advances in Intelligent Systems and Computing, Springer, Germany, 2020, pp. 659-669. https://doi.org/10.1007/978-981-15-2414-1_66
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dc.relation.referencesen[6] S. B. Kotsiantis, D. Kanellopoulos, and P. E. Pintelas, "Data preprocessing for supervised learning," International Journal of Computer Science, vol. 1, pp. 111-117, 2006.
dc.relation.referencesen[7] U. Shruthi, V. Nagaveni, and B. K. Raghavendra, "A Review on Machine Learning Classification Techniques for Plant Disease Detection," in 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019, Institute of Electrical and Electronics Engineers Inc., United States, 2019, pp. 281-284. https://doi.org/10.1109/ICACCS.2019.8728415
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dc.relation.referencesen[9] Nagasubramanian, K., Jones, S., Singh, A.K. et al. Plant disease identification using explainable 3D deep learning on hyperspectral images. Plant Methods 15, 98 (2019). https://doi.org/10.1186/s13007-019-0479-8
dc.relation.referencesen[10] Y. Lu, S. Yi, N. Zeng, Y. Liu, and Y. Zhang, "Identification of rice diseases using deep convolutional neural networks," Neurocomputing, vol. 267, pp. 378-384, 2017. https://doi.org/10.1016/j.neucom.2017.06.023
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dc.relation.referencesen[12] J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkarana, "Using deep transfer learning for image-based plant disease identification," Computers and Electronics in Agriculture, vol. 173, p. 105393, 2020. https://doi.org/10.1016/j.compag.2020.105393
dc.relation.referencesen[13] J. G. Arnal Barbedo, "Plant disease identification from individual lesions and spots using deep learning," Biosystems Engineering, vol. 180, pp. 96-107, 2019. https://doi.org/10.1016/j.biosystemseng.2019.02.002
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dc.relation.referencesen[21] H. Durmus, E. O. Gunes, and M. Kirci, "Disease Detection on the Leaves of the Tomato Plants by Using Deep Learning," in 2017 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2017, Institute of Electrical and Electronics Engineers Inc., United States, 2017. https://doi.org/10.1109/Agro-Geoinformatics.2017.8047016
dc.relation.referencesen[22] Z. Ibrahim, N. Sabri, and D. Isa, "Multi-maxpooling Convolutional Neural Network for Medicinal Herb Leaf Recognition," in Proceedings of the 6th IIAE International Conference on Intelligent Systems and Image Processing 2018, The Institute of Industrial Applications Engineers, Japan, 2018. https://doi.org/10.12792/icisip2018.060
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dc.relation.referencesen[24] N. Fatihah Sahidan, A. K. Juha, N. Mohammad, and Z. Ibrahim, "Flower and leaf recognition for plant identification using convolutional neural network," Indonesian Journal of Electrical Engineering and Computer Science, vol. 16, no. 2, pp. 737-743, 2019. https://doi.org/10.11591/ijeecs.v16.i2.pp737-743
dc.relation.referencesen[25] M. M. Saufi, M. A. Zamanhuri, N. Mohammad, and Z. Ibrahim, "Deep learning for roman handwritten character recognition," Indonesian Journal of Electrical Engineering and Computer Science, vol. 12, no. 2, pp. 455-460, 2018. https://doi.org/10.11591/ijeecs.v12.i2.pp455-460
dc.relation.referencesen[26] D. Jiang, G. Li, Y. Sun, J. Hu, J. Yun, and Y. Liu, "Manipulator grabbing position detection with information fusion of color image and depth image using deep learning," Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 12, pp. 10809-10822, 2021. https://doi.org/10.1007/s12652-020-02843-w
dc.relation.referencesen[27] B. Liu, Y. Zhang, D. He, and Y. Li, "Identification of apple leaf diseases based on deep convolutional neural networks," Symmetry, vol. 10, p. 11, 2017. https://doi.org/10.3390/sym10010011
dc.relation.referencesen[28] S. M. Omer, K. Z. Ghafoor, and S. K. Askar, "An intelligent system for cucumber leaf disease diagnosis based on the tuned convolutional neural network algorithm," Mobile Information Systems, vol. 2022, p. 8909121, 2022. https://doi.org/10.1155/2022/8909121
dc.relation.referencesen[29] S. Hernández and J. L. López, "Uncertainty quantification for plant disease detection using Bayesian deep learning," Applied Soft Computing Journal, vol. 96, p. 106597, 2020. https://doi.org/10.1016/j.asoc.2020.106597
dc.relation.referencesen[30] K. P. Ferentinos, "Deep learning models for plant disease detection and diagnosis," Computers and Electronics in Agriculture, vol. 145, pp. 311-318, 2018. https://doi.org/10.1016/j.compag.2018.01.009
dc.relation.referencesen[31] A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, and D. P. Hughes, "Deep learning for image-based cassava disease detection," Frontiers in Plant Science, vol. 8, p. 1852, 2017. https://doi.org/10.3389/fpls.2017.01852
dc.relation.referencesen[32] A. Fuentes, S. Yoon, S. C. Kim, and D. S. Park, "A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition," Sensors (Switzerland), vol. 17, no. 9, p. 2022, 2017. https://doi.org/10.3390/s17092022
dc.relation.referencesen[33] F. Hutter, J. Lücke, and L. Schmidt-Thieme, "Beyond manual tuning of hyperparameters," KI-Kunstliche Intelligenz, vol. 29, no. 4, pp. 329-337, 2015. https://doi.org/10.1007/s13218-015-0381-0
dc.relation.referencesen[34] A. H. Victoria and G. Maragatham, "Automatic tuning of hyperparameters using Bayesian optimization," Evolving Systems, vol. 12, no. 1, pp. 217-223, 2021. https://doi.org/10.1007/s12530-020-09345-2
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dc.relation.referencesen[37] M. Arsenovic, M. Karanovic, S. Sladojevic, A. Anderla, and D. Stefanovic, "Solving current limitations of deep learning based approaches for plant disease detection," Symmetry, vol. 11, no. 7, p. 939, 2019.
dc.relation.referencesen[38] J. Brownlee, "Better Deep Learning: Train Faster, Reduce Overfitting, and Make Better Predictions," 2018. https://doi.org/10.3390/sym11070939
dc.relation.urihttps://doi.org/10.3390/plants9111451
dc.relation.urihttps://dl.gi.de/handle/20.500.12116/944
dc.relation.urihttps://doi.org/10.1007/978-981-15-2414-1_66
dc.relation.urihttps://doi.org/10.4028/www.scientific.net/JERA.24.124
dc.relation.urihttps://doi.org/10.1109/ICACCS.2019.8728415
dc.relation.urihttps://doi.org/10.1155/2016/3289801
dc.relation.urihttps://doi.org/10.1186/s13007-019-0479-8
dc.relation.urihttps://doi.org/10.1016/j.neucom.2017.06.023
dc.relation.urihttps://doi.org/10.1016/j.compag.2020.105393
dc.relation.urihttps://doi.org/10.1016/j.biosystemseng.2019.02.002
dc.relation.urihttps://doi.org/10.1016/j.biosystemseng.2018.05.013
dc.relation.urihttps://doi.org/10.3389/fpls.2016.01419
dc.relation.urihttps://doi.org/10.1063/5.0028564
dc.relation.urihttps://doi.org/10.1007/s10462-020-09825-6
dc.relation.urihttps://hal.science/hal-02172017
dc.relation.urihttps://doi.org/10.14569/IJACSA.2019.0100634
dc.relation.urihttps://doi.org/10.1162/neco.1989.1.4.541
dc.relation.urihttps://doi.org/10.1109/Agro-Geoinformatics.2017.8047016
dc.relation.urihttps://doi.org/10.12792/icisip2018.060
dc.relation.urihttp://arxiv.org/abs/1710.05941
dc.relation.urihttps://doi.org/10.11591/ijeecs.v16.i2.pp737-743
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dc.relation.urihttps://doi.org/10.3390/sym10010011
dc.relation.urihttps://doi.org/10.1155/2022/8909121
dc.relation.urihttps://doi.org/10.1016/j.asoc.2020.106597
dc.relation.urihttps://doi.org/10.1016/j.compag.2018.01.009
dc.relation.urihttps://doi.org/10.3389/fpls.2017.01852
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dc.relation.urihttps://doi.org/10.1007/s13218-015-0381-0
dc.relation.urihttps://doi.org/10.1007/s12530-020-09345-2
dc.relation.urihttps://eprints.lancs.ac.uk/id/eprint/134273
dc.relation.urihttps://doi.org/10.3390/sym11070939
dc.rights.holder© Національний університет “Львівська політехніка”, 2024
dc.rights.holder© Гуменюк Р., Попович І., 2024
dc.subjectглибоке навчання
dc.subjectідентифікація рослин
dc.subjectдіагностика хвороб рослин
dc.subjectрозпізнавання хвороб рослин
dc.subjectзгорткові нейронні мережі
dc.subjectCNN
dc.subjectкласифікація ознак хвороб рослин
dc.subjectdeep learning
dc.subjectplant identification
dc.subjectplant disease diagnosis
dc.subjectplant disease recognition
dc.subjectconvolutional neural networks
dc.subjectCNN
dc.subjectplant disease symptom classification
dc.titleДослідження методів діагностики захворювань рослин за допомогою глибокого навчання
dc.title.alternativeResearch of plant disease diagnostic methods using deep learning
dc.typeArticle

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Комп'ютерні системи проектування теорія і практика. – 2024. – Том 6, № 1