A deep feature fusion network using residual channel shuffled attention for cassava leaf disease detection
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-08943-w.pdf
Reference38 articles.
1. Abayomi-Alli OO, Damaševičius R, Misra S, Maskeliūnas R (2021) Cassava disease recognition from low-quality images using enhanced data augmentation model and deep learning. Expert Syst. https://doi.org/10.1111/exsy.12746
2. Agricultural Research Council (2014) https://www.arc.agric.za/arc-iic/Pages/Cassava.aspx
3. Ayu HR, Surtono A, Apriyanto DK (2021) Deep learning for detection cassava leaf disease. J Phys Conf Ser 1751(1):012072. https://doi.org/10.1088/1742-6596/1751/1/012072
4. Buslaev A, Iglovikov VI, Khvedchenya E, Parinov A, Druzhinin M, Kalinin AA (2020) Albumentations: fast and flexible image augmentations. Information 11(2):125. https://doi.org/10.3390/info11020125
5. Chen Y, Xu K, Zhou P, Ban X, He D (2022) Improved cross entropy loss for noisy labels in vision leaf disease classification. IET Image Process. 16(6):1511–1519. https://doi.org/10.1049/ipr2.12402
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