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Bidirectional Encoder representation from Image Transformers for recognizing sunflower diseases from photographs
 V.A. Baboshina 1, P.A. Lyakhov 1,2, U.A. Lyakhova 2, V.A. Pismennyy 2
 1 North-Caucasus Center for Mathematical Research, North-Caucasus Federal University,
     Pushkin Str. 1, 355017, Stavropol, Russia;
     2 Department of Mathematical Modeling, North-Caucasus Federal University,
     Pushkin Str. 1, 355017, Stavropol, Russia
 
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DOI: 10.18287/2412-6179-CO-1514
Страницы: 435-442.
Язык статьи: English.
 
Аннотация:
This  paper proposes a modern system for recognizing sunflower diseases based on  Bidirectional Encoder representation from Image Transformers (BEIT). The  proposed system is capable of recognizing various sunflower diseases with high  accuracy. The presented research results demonstrate the advantages of the  proposed system compared to known methods and contemporary neural networks. The  proposed visual diagnostic system for sunflower diseases achieved 99.57 %  accuracy on the sunflower disease dataset, which is higher than that of known  methods. The approach described in the work can serve as an auxiliary tool for  farmers, assisting them in promptly identifying diseases and pests and taking  timely measures to treat plants. This, in turn, helps in preserving and  enhancing the yield. This work can have a significant impact on the development  of agriculture and the fight against the global food shortage problem.
Ключевые слова:
image  transformer, neural network recognition, image processing, sunflower diseases,  bidirectional encoder.
Благодарности
The authors express  their gratitude to NCFU for the support of small scientific groups and  individual scientists.  Research was conducted with the support of the Russian  Science Foundation (project No. 23-71-10013).
Citation:
Baboshina VA, Lyakhov PA, Lyakhova UA, Pismennyy VA. Bidirectional Encoder representation from Image Transformers for recognizing sunflower diseases from photographs. Computer Optics 2025; 49(3): 435-442. DOI: 10.18287/2412-6179-CO-1514.
References:
  - Population, United  Nations. 2024. Source: <https://www.un.org/en/global-issues/population>.
 
- The state of food  security and nutrition in the world. Food and Agriculture Organization of the  United Nations. 2023. Source: <https://www.fao.org/publications/home/fao-flagship-publications/the-state-of-food-security-and-nutrition-in-the-world/en>.
 
- The  plants that feed the world: Baseline data and metrics to inform strategies for  the conservation and use of plant genetic resources for food and agriculture.  Ninth Session of the Governing Body, New    Delhi, India.  2022. Source: <https://openknowledge.fao.org/server/api/core/bitstreams/3f79e42f-0fd2-45cb-98a9-099b3748547c/content>.
 
- Adeleke  BS, Babalola OO. Oilseed crop sunflower (Helianthus annuus) as a source of  food: Nutritional and health benefits. Food Sci Nutr 2020; 8: 4666-4684. DOI:  https://doi.org/10.1002/fsn3.1783
 
- Kottapalli  B, Nguyen SPV, Dawson  K, Casulli K, Knockenhauer C, Schaffner DW. Evaluating the risk of salmonellosis  from dry roasted sunflower seeds. J Food Prot 2020; 83(1): 17-27. DOI:  10.4315/0362-028x.jfp-19-171.
 
- da  Rocha-Filho PA, Maruno M, Ferrari M, Topan JF. Liquid crystal formation from  sunflower oil: Long term stability studies. Molecules 2016; 21(6): 680. DOI: 10.3390/molecules21060680.
 
- Savary  S. Plant health and food security. J Plant Pathol 2020; 102: 605-607. DOI:  10.1007/s42161-020-00611-5.
 
- Agrawal M, Agrawal S. Rice plant diseases  detection using convolutional neural networks. Int J Eng Syst Model Simul 2023;  14(1): 30-42. DOI: 10.1504/IJESMS.2023.127396.
 
- Umapathi  R, Ghoreishian SM, Sonwal S, Rani GM, Huh YS. Portable electrochemical sensing  methodologies for on-site detection of pesticide residues in fruits and vegetables.  Coord Chem Rev 2022; 453: 214305. DOI: 10.1016/j.ccr.2021.214305.
 
- Sara  U, Rajbongshi A, Shakil R, Akter B, Sazzad S, Uddin MS. An extensive sunflower  dataset representation for successful identification and classification of  sunflower diseases. Data Brief 2022; 42: 108043. DOI: 10.1016/j.dib.2022.108043.
 
- Gulzar  Y, Ünal Z, Aktaş H, Mir MS. Harnessing the power of transfer learning in sunflower  disease detection: A comparative study. Agriculture 2023; 13(8): 1479. DOI:  10.3390/agriculture13081479.
 
- Liu J, Lv F, Penghui D. Identification of  sunflower leaf diseases based on random forest algorithm. 2019 Int Conf on  Intelligent Computing, Automation and Systems (ICICAS) 2019: 459-463. DOI:  10.1109/ICICAS48597.2019.00102.
 
- Sathi  TA, Hasan MA, Alam MJ. SunNet: A deep learning approach to detect sunflower  disease. 7th Int Conf on Trends in Electronics and Informatics (ICOEI) 2023:  1210-1216. DOI: 10.1109/ICOEI56765.2023.10125676.
 
- Zhong  Y, Tong MJ. TeenyNet: A novel lightweight attention model for sunflower disease  detection. Meas Sci Technol 2023; 35(3): 035701. DOI: 10.1088/1361-6501/ad1152.
 
- Thilagavathi  T, Arockiam L. Segmentation of sunflower leaf disease using improved YOLO  network with IDMO model. Int J Intell Syst Appl Eng 2024; 12(125): 600-611.
 
- Dai G, Tian Z, Fan J, Sunil CK, Dewi C.  DFN-PSAN: Multi-level deep information feature fusion extraction network for  interpretable plant disease classification. Comput Electron Agric 2024; 216:  108481. DOI: 10.1016/j.compag.2023.108481.
 
- Sun  C, Zhou X, Zhang M, Qin A. SE-VisionTransformer: Hybrid network for diagnosing  sugarcane leaf diseases based on attention mechanism. Sensors 2023; 23(20):  8529. DOI: 10.3390/s23208529.
 
- Sodikov B, Rakhmonov U, Khamiraev U,  Akbarov M. Fungal diseases of sunflower and measures against them. PalArch’s  Journal of Archaeology of Egypt/Egyptology 2020; 17(6): 3268-3279.
 
- Devlin  J, Chang M-W, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional  transformers for language understanding. arXiv Preprint. 2019. Source: <https://arxiv.org/abs/1810.04805>.  DOI: 10.48550/arXiv.1810.04805.
 
- Ramesh  A, Pavlov M, Goh G, Gray S, Voss C, Radford A, Chen M, Sutskever I. Zero-shot  text-to-image generation. arXiv Preprint. 2021. Source: <https://arxiv.org/abs/2102.12092>.  DOI: 10.48550/arXiv.2102.12092.
 
- Vaswani  A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I.  Attention is all you need. 31st Conf on Neural Information Processing Systems  (NIPS 2017) 2017: 6000-6010.
 
- Bao  H, Dong L, Piao S, Wei F. BEiT: BERT pre-training of image transformers. The  Tenth Int Conf on Learning Representations (ICLR) 2022: 1-18.
 
- Zhu H, Chen B,  Yang C. Understanding why ViT trains badly on small datasets: An intuitive  perspective. arXiv Preprint. 2023. Source:  <https://arxiv.org/abs/2302.03751>. DOI: 10.48550/arXiv.2302.03751.
 
- Loshchilov I, Hutter F. Decoupled weight  decay regularization. 7th Int Conf on Learning Representations (ICLR) 2019:  1-8. Source: <https://openreview.net/pdf?id=Bkg6RiCqY7>.
 
- Kim  Y, Lee Y, Jeon M. Imbalanced image classification with complement cross entropy.  Pattern Recogn Lett 2022; 151: 33-40. DOI: 10.1016/j.patrec.2021.07.017.       
 
- Ho  Y, Wookey S. The real-world-weight cross-entropy loss function: Modeling the  costs of mislabeling. IEEE Access 2020; 8: 4806-4813. DOI:  10.1109/ACCESS.2019.2962617.
 
- Huynh  T, Nibali A, He Z. Semi-supervised learning for medical image classification  using imbalanced training data. Comput Methods Programs Biomed 2022; 216:  106628. DOI: 10.1016/j.cmpb.2022.106628.
 
- Vo NH, Won Y.  Classification of unbalanced medical data with weighted regularized least  squares. 2007 Frontiers in the Convergence of Bioscience and Information  Technologies 2007: 347-352. DOI: 10.1109/FBIT.2007.20.
 
- Aurelio  YS, de Almeida GM, de Castro CL. Learning from imbalanced data sets with  weighted cross-entropy function. Neural Process Lett 2019; 50(2): 1937-1949.  DOI: 10.1007/s11063-018-09977-1.
 
- Dong Y, Shen X,  Jiang Z, Wang H. Recognition of imbalanced underwater acoustic datasets with  exponentially weighted cross-entropy loss. Appl Acoust 2021; 174: 107740. DOI:  10.1016/j.apacoust.2020.107740. 
- Ghosh P, Mondal AK,  Chatterjee S, Masud M, Meshref H, Bairagi AK. Recognition of sunflower diseases  using hybrid deep learning and its explainability with AI. Mathematics 2023;  11(10): 2241. DOI: 10.3390/math11102241.
  
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