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Cited by 61

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

EfficientNetB3-Adaptive Augmented Deep Learning (AADL) for Multi-Class Plant Disease Classification

Adnan, Faiqa, Awan, Mazhar Javed, Mahmoud, Amena, Nobanee, Haitham, Yasin, Awais, Zain, Azlan Mohd·2023·IEEE Access
DOI 10.1109/access.2023.3303131
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  1. 10.3390/rs13132486 · DOI · Paper page
  2. 10.1109/CVPR.2018.00474 · DOI · Paper page
  3. 10.1007/s11831-019-09324-0 · doi:10.1007/s11831-019-09324-0
  4. tan, 2019, EfficientNet: Rethinking model scaling for convolutional neural networks, Proc ICML, 6105
  5. 10.3390/s20051452 · DOI
  6. 10.1109/ICoDT252288.2021.9441512 · doi:10.1109/icodt252288.2021.9441512
  7. 10.1007/978-981-33-6424-0_7 · DOI
  8. 10.1016/j.ecoinf.2020.101182 · DOI · Paper page
  9. 10.1016/j.knosys.2020.105596 · DOI · Paper page
  10. 10.1109/TCAD.2021.3120076 · DOI
  11. 10.1109/ACCESS.2021.3069646 · DOI · Paper page
  12. 10.1007/s11042-019-08453-9 · DOI · Paper page
  13. 10.1016/j.aiia.2020.10.002 · DOI
  14. 10.3390/diagnostics11010105 · DOI
  15. 10.1186/s12859-020-03615-1 · DOI
  16. 10.1007/s11042-020-09261-2 · DOI
  17. 10.1016/j.jksuci.2018.09.002 · DOI · Paper page
  18. 10.1007/978-3-030-32606-7_3 · DOI
  19. 10.1007/s11042-021-10544-5 · doi:10.1007/s11042-021-10544-5
  20. 10.1007/s11042-021-11869-x · DOI
  21. 10.1109/DINWC.2018.8356995 · doi:10.1109/dinwc.2018.8356995
  22. 10.1109/TASLP.2020.3038524 · DOI · Paper page
  23. 10.1109/ASPCON49795.2020.9276722 · doi:10.1109/aspcon49795.2020.9276722
  24. 10.1016/j.compag.2018.08.048 · DOI · Paper page
  25. enkvetchakul, 2022, Effective data augmentation and training techniques for improving deep learning in plant leaf disease recognition, Appl Sci Eng Prog, 15, 3810
  26. 10.1109/ICECCT.2019.8869090 · doi:10.1109/icecct.2019.8869090
  27. awan, 2021, The customized convolutional neural network of face emotion expression classification, Annals of the Romanian Society for Cell Biology, 25, 5296
  28. 10.32607/actanaturae.11026 · DOI · Paper page
  29. 10.1007/s00344-021-10406-2 · DOI
  30. liliane, 2020, Factors affecting yield of crops, Agronomy—Climate Change and Food Security, 9
  31. 10.1016/j.compag.2019.105174 · DOI · Paper page
  32. 10.3390/su12135488 · DOI · Paper page
  33. 10.3390/v13030412 · DOI · Paper page
  34. 10.3390/s21062129 · DOI · Paper page
  35. 10.1016/j.compag.2020.105652 · DOI
  36. 10.1007/978-3-030-71051-4_8 · doi:10.1007/978-3-030-71051-4_8
  37. 10.3390/electronics12040827 · DOI
  38. 10.1007/s11554-020-00987-8 · DOI · Paper page
  39. 10.1007/s11042-022-12147-0 · doi:10.1007/s11042-022-12147-0
  40. 10.1007/s00500-021-06176-4 · DOI
  41. 10.1007/s10462-020-09825-6 · DOI · Paper page
  42. simonyan, 2014, Very deep convolutional networks for large-scale image recognition, arXiv 1409 1556
  43. xu, 2020, Recognition of corn leaf spot and rust based on transfer learning with convolutional neural network, Transactions of the Chinese Society for Agricultural Machinery, 51, 230
  44. rahamathunnisa, 2022, A survey on agriculture monitoring with satellite and its benefits, ICAC Proc, 1, 1854
  45. 10.1155/2016/3289801 · DOI · Paper page
  46. 10.3390/sym11070939 · DOI · Paper page
  47. 10.3390/app12188972 · DOI · Paper page
  48. 10.1609/aaai.v31i1.11231 · DOI · Paper page
  49. 10.1007/s11042-020-09669-w · DOI
  50. 10.1016/j.compag.2020.105393 · DOI · Paper page
  51. 10.1186/s13007-021-00722-9 · DOI · Paper page
  52. 10.1007/s41348-020-00403-0 · DOI · Paper page
  53. 10.1109/CVPR.2016.308 · DOI · Paper page
  54. 10.1109/CVPR.2017.195 · DOI · Paper page
  55. 10.3390/agriculture11080707 · DOI · Paper page
  56. 10.1016/j.procs.2020.03.225 · DOI · Paper page
  57. akshai, 2021, Plant disease classification using deep learning, Proc 3rd Int Conf Signal Process Commun (ICPSC), 407
  58. vujovi?, 2021, Classification model evaluation metrics, Int J Adv Comput Sci Appl, 12, 599
  59. 10.1109/IWQoS.2018.8624183 · DOI · Paper page

Metadata

Authors
Adnan, F., Awan, M. J., Mahmoud, A., Nobanee, H., Yasin, A., & Zain, A. M.
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2023
Volume
11
Pages
85426-85440
ISSN
2169-3536
DOI
10.1109/access.2023.3303131
Cited by
61
References
4
Licence
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)

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CoChat. (2026). Citation check: EfficientNetB3-Adaptive Augmented Deep Learning (AADL) for Multi-Class Plant Disease Classification. https://app.cochat.ai/papers/10.1109/access.2023.3303131
The paper
Adnan, F., Awan, M. J., Mahmoud, A., Nobanee, H., Yasin, A., & Zain, A. M. (2023). EfficientNetB3-Adaptive Augmented Deep Learning (AADL) for Multi-Class Plant Disease Classification. IEEE Access. https://doi.org/10.1109/access.2023.3303131

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.