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首页 All issues Volume 16 (2025) Int. J. Simul. Multidisci. Des. Optim., 16 (2025) 7 参考文献
Open Access
期号
Int. J. Simul. Multidisci. Des. Optim.
卷号 16, 2025
文献编号 7
页数 16
DOI https://doi.org/10.1051/smdo/2025006
网上发表时间 2025年4月29日
  1. D. O'Rourke, Economic importance of the world apple industry, The apple genome 1–18 (2021). https://doi.org/10.1007/978-3-030-74682-7-1 [Google Scholar]
  2. E. Arrigoni, D. Albanese, C.M.O. Longa, D. Angeli, C. Donati, C. Ioriatti, I. Pertot, M. Perazzolli, Tissue age, orchard location and disease management influence the composition of fungal and bacterial communities present on the bark of apple trees, Environ. Microbiol. 22, 2080–2093 (2020) [Google Scholar]
  3. X. Liang, R. Zhang, M.L. Gleason, G. Sun, Sustainable apple disease management in China: Challenges and future directions for a transforming industry, Plant Dis. 106, 786–799 (2022) [CrossRef] [Google Scholar]
  4. P. Bansal, R. Kumar, S. Kumar, Disease detection in apple leaves using deep convolutional neural network, Agriculture 11, 617 (2021) [CrossRef] [Google Scholar]
  5. H. Azgomi, F.R. Haredasht, M.R.S. Motlagh, Diagnosis of some apple fruit diseases by using image processing and artificial neural network, Food Control 145, 109484 (2023) [Google Scholar]
  6. M. Jan H. Ahmad, Image features based intelligent apple disease prediction system: Machine learning based apple disease prediction system, Int. J. Agric. Environ. Inf. Syst. 11, 31–47 (2020) [Google Scholar]
  7. A.J. Moshayedi, A.S. Khan, M. Davari, T. Mokhtari, M.E. Andani, Micro robot as the feature of robotic in healthcare approach from design to application: The state of art and challenges, EAI Endorsed Trans. AI Robot. 3 (Apr. 2024). https://doi.org/10.4108/airo.5602 [Google Scholar]
  8. A.A. Bracino, R.S. Concepcion, R.A.R. Bedruz, E.P. Dadios, and R.R.P. Vicerra, Development of a hybrid machine learning model for apple (Malus domestica) health detection and disease classification, in: 2020 IEEE 12th Int. Conf. Humanoid, Nanotechnol., Inf. Technol., Commun. Control, Environ. Manage. (HNICEM) (IEEE, 2020), pp. 1–6. https://doi.org/10.1109/HNICEM51456.2020.9400139 [Google Scholar]
  9. V.D. Jose and K. Santhi, Early detection and classification of apple leaf diseases by utilizing IFPA genetic algorithm with MC-SVM, SVI and deep learning methods, Indian J. Sci. Technol. 15, 1440–1450 (2022). https://doi.org/10.17485/IJST/v15i29.1235 [Google Scholar]
  10. M.R. Kale and M.S. Shitole, Analysis of crop disease detection with SVM, KNN and random forest classification, Inf. Technol. Ind. 9, 364–372 (2021). [Google Scholar]
  11. S. Singh, S. Gupta, A. Tanta, and R. Gupta, Extraction of multiple diseases in apple leaf using machine learning, Int. J. Image Graph. 22, 2140009 (2022). https://doi.org/10.1142/S021946782140009X [Google Scholar]
  12. A.J. Moshayedi, A.S. Khan, J. Hu, A. Nawaz, and J. Zhu, E-nose-driven advancements in ammonia gas detection: A comprehensive review from traditional to cutting-edge systems in indoor to outdoor agriculture, Sustainability 15 (15), 11601 (2023). https://doi.org/10.3390/su151511601 [CrossRef] [Google Scholar]
  13. M. Sardoğan, Y. Özen, and A. Tuncer, Detection of apple leaf diseases using Faster R-CNN, Düzce Univ. Sci. Technol. J. 8, 1110–1117 (2020). https://doi.org/10.29130/dubited.648387 [Google Scholar]
  14. Z. Chen, R. Su, Y. Wang, G. Chen, Z. Wang, P. Yin, and J. Wang, Automatic estimation of apple orchard blooming levels using the improved YOLOv5, Agronomy 12 (10), 2483 (2022). https://doi.org/10.3390/agronomy12102483 [CrossRef] [Google Scholar]
  15. H. Sun, H. Xu, B. Liu, D. He, J. He, H. Zhang, and N. Geng, MEAN-SSD: A novel real-time detector for apple leaf diseases using improved lightweight convolutional neural networks, Comput. Electron. Agric. 189, 106379 (2021). https://doi.org/10.1016/j.compag.2021.106379 [Google Scholar]
  16. W. Luo, L. Cai, and Y. Yang, Apple leaf disease recognition in natural scenes based on re-parameterised SSD algorithm, in: Int. Conf. Comput. Graph., Artif. Intell., Data Process. (ICCAID 2022) (SPIE, 2023), 12604, 994–1006. https://doi.org/10.1117/12.2674686. [Google Scholar]
  17. F.O. Babalola, N.I. Kpai, and Ö. Toygar, Deep learning-based classification of apple leaf diseases using AlexNet, Comput. Sci. (IDAP-2023), 67–74 (2023). https://doi.org/10.53070/bbd.1349566 [Google Scholar]
  18. W. Bao, T. Fan, G. Hu, D. Liang, and H. Li, Detection and identification of tea leaf diseases based on AX-RetinaNet, Sci. Rep. 12, 2183 (2022). https://doi.org/10.1038/s41598-022-06181-z [CrossRef] [Google Scholar]
  19. X. Gao, Z. Tang, Y. Deng, S. Hu, H. Zhao, and G. Zhou, HSSNet: An end-to-end network for detecting tiny targets of apple leaf diseases in complex backgrounds, Plants 12 (15), 2806 (2023). https://doi.org/10.3390/plants12152806 [CrossRef] [Google Scholar]
  20. Y. Wang, P. Zhang, and S. Tian, Tomato leaf disease detection based on attention mechanism and multi-scale feature fusion, Front. Plant Sci. 15, 1382802 (2024). https://doi.org/10.3389/fpls.2024.1382802 [Google Scholar]
  21. L. Gao, X. Zhao, X. Yue, Y. Yue, X. Wang, H. Wu, and X. Zhang, A lightweight YOLOv8 model for apple leaf disease detection, Appl. Sci. 14 (15), 6710 (2024). https://doi.org/10.3390/app14156710 [CrossRef] [Google Scholar]
  22. M. Hussain, YOLOv1 to v8: Unveiling each variant − a comprehensive review of YOLO, IEEE Access 12, 42816–42833 (2024). https://doi.org/10.1109/ACCESS.2024.3378568 [Google Scholar]
  23. A.J. Moshayedi, A.S. Khan, Y. Yang, J. Hu, and A. Kolahdooz, Robots in agriculture: Revolutionizing farming practices, EAI Endorsed Trans. AI Robot. 3 (Jun. 2024). https://doi.org/10.4108/airo.5855 [Google Scholar]
  24. S. Liu, Y. Qiao, J. Li, H. Zhang, M. Zhang, and M. Wang, An improved lightweight network for real-time detection of apple leaf diseases in natural scenes, Agronomy 12 (10), 2363 (2022). https://doi.org/10.3390/agronomy12102363 [CrossRef] [Google Scholar]
  25. L. Fu, S. Li, Y. Sun, Y. Mu, T. Hu, and H. Gong, Lightweight convolutional neural network for apple leaf disease identification, Front. Plant Sci. 13, 831219 (2022). https://doi.org/10.3389/fpls.2022.831219 [Google Scholar]
  26. X. Chao, G. Sun, H. Zhao, M. Li, and D. He, Identification of apple tree leaf diseases based on deep learning models, Symmetry (Basel) 12, 1065 (2020). https://doi.org/10.3390/sym12071065 [CrossRef] [Google Scholar]
  27. Y. Wang, Y. Wang, and J. Zhao, MGA-YOLO: A lightweight one-stage network for apple leaf disease detection, Front. Plant Sci. 13, 927424 (2022). https://doi.org/10.3389/fpls.2022.927424 [Google Scholar]
  28. Q. Yang, S. Duan, and L. Wang, Efficient identification of apple leaf diseases in the wild using convolutional neural networks, Agronomy 12 (11), 2784 (2022). https://doi.org/10.3390/agronomy12112784 [CrossRef] [Google Scholar]
  29. I. Ahmed and P.K. Yadav, Predicting apple plant diseases in orchards using machine learning and deep learning algorithms, SN Comput. Sci. 5, 700 (2024). https://doi.org/10.1007/s42979-024-02959-2 [CrossRef] [Google Scholar]
  30. M.M.U. Nobi, M. Rifat, M.F. Mridha, S. Alfarhood, M. Safran, and D. Che, GLD-Det: Guava leaf disease detection in real-time using lightweight deep learning approach based on MobileNet, Agronomy 13, 2240 (2023). https://doi.org/10.3390/agronomy13092240 [CrossRef] [Google Scholar]
  31. S. Parez, N. Dilshad, T.M. Alanazi, and J.-W. Lee, Towards sustainable agricultural systems: A lightweight deep learning model for plant disease detection, Comput. Syst. Sci. Eng. 47, 515–536 (2023). https://doi.org/10.32604/csse.2023.037992 [Google Scholar]
  32. J. Ni, Smart agriculture: An intelligent approach for apple leaf disease identification based on convolutional neural network, J. Phytopathol. 172, e13374 (2024). https://doi.org/10.1111/jph.13374 [CrossRef] [Google Scholar]
  33. M. Maaz, A. Shaker, H. Cholakkal, S. Khan, S.W. Zamir, R.M. Anwer, and F.S. Khan, EdgeNeXt: Efficiently amalgamated CNN-Transformer architecture for mobile vision applications, in: Eur. Conf. Comput. Vis. (Springer, 2022), pp. 3–20. https://doi.org/10.1007/978-3-031-25082-8_1 [Google Scholar]
  34. J. Li, Y. Wen, and L. He, SCConv: Spatial and channel reconstruction convolution for feature redundancy, in: Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (2023) pp. 6153–6162. [Google Scholar]
  35. W. Liu, H. Lu, H. Fu, and Z. Cao, Learning to upsample by learning to sample, in: Proc. IEEE/CVF Int. Conf. Comput. Vis. (2023) pp. 6027–6037. [Google Scholar]
  36. Y.-F. Zhang, W. Ren, Z. Zhang, Z. Jia, L. Wang, and T. Tan, Focal and efficient IoU loss for accurate bounding box regression, Neurocomputing 506, 146–157 (2022). https://doi.org/10.1016/j.neucom.2022.07.042 [CrossRef] [Google Scholar]
  37. P. Pan, M. Shao, P. He, L. Hu, S. Zhao, L. Huang, G. Zhou, and J. Zhang, Lightweight cotton diseases real-time detection model for resource-constrained devices in natural environments, Front. Plant Sci. 15, 1383863 (2024). https://doi.org/10.3389/fpls.2024.1383863 [Google Scholar]

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