Plant Disease Detection Algorithm Based on Efficient Swin Transformer
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Abstract
Plant diseases present a significant threat to global agricultural productivity, endangering both crop yields and quality. Traditional detection methods largely rely on manual inspection, a process that is not only labor-inte... | Find, read and cite all the research you need on Tech Science Press
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OpenAlex topics: Wireless Sensor Networks and IoT
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References (42)
Show source-provided references
1. The global burden of pathogens and pests on major food crops10.1038/s41559-018-0793-y ↗
2. Pathogens, beneficial microbes, and beyond: A toolbox for plant health improvement
3. Plant disease detection by imaging sensors—Parallels and specific demands for precision agriculture and plant phenotyping10.1094/PDIS-03-15-0340-FE ↗
4. Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging10.1080/07352681003617285 ↗
5. Advances in plant disease detection
6. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” presented at the Int. Conf. Learn. Representations, San Diego, CA, USA, May 7–9, 2015.
7. C. Szegedy et al., “Going deeper with convolutions,” presented at the IEEE Conf. Comput. Vis. Pattern Recognit., Boston, MA, USA, Jun. 7–12, 2015.
8. G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” presented at the IEEE Conf. Comput. Vis. Pattern Recognit., Honolulu, HI, USA, Jul. 21–26, 2017.10.1109/CVPR.2017.243 ↗
9. A. Dosovitskiy et al., “An image is worth 16 × 16 words: Transformers for image recognition at scale,” presented at the Int. Conf. Learn. Representations, Vienna, Austria, May 3–7, 2021.
10. K. Han, A. Xiao, E. Wu, J. Guo, and C. Xu, “Swin transformer: Hierarchical vision transformer using shifted windows,” presented at the IEEE/CVF Int. Conf. Comput. Vis., Montreal, QC, Canada, Oct. 11–17, 2021.
11. W. Wang, X. Xie, and Y. Li, “Pyramid vision transformer: A versatile backbone for dense prediction without convolutions,” presented at the IEEE/CVF Int. Conf. Comput. Vis., Montreal, QC, Canada, Oct. 11–17, 2021.10.1109/ICCV48922.2021.00061 ↗
12. Szeliski · Computer Vision: Algorithms and Applications · 2010
13. Distinctive image features from scale-invariant keypoints10.1023/B:VISI.0000029664.99615.94 ↗
14. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” presented at the Adv. Neural Inf. Process. Syst., Lake Tahoe, NV, USA, Dec. 3–6, 2012.
15. K. He, X. Zhang, and J. Sun, “Deep residual learning for image recognition,” presented at the IEEE Conf. Comput. Vis. Pattern Recognit., Las Vegas, NV, USA, Jun. 27–30, 2016.
16. M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” presented at the Int. Conf. Mach. Learn., Long Beach, CA, USA, Jun. 9–15, 2019.
17. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” presented at the IEEE Conf. Comput. Vis. Pattern Recognit., Las Vegas, NV, USA, Jun. 27–30, 2016.10.1109/CVPR.2016.91 ↗
18. Plant leaf disease detection using improved convolutional neural network
19. Deep learning models for plant disease detection and diagnosis10.1016/j.compag.2018.01.009 ↗
20. Deep learning for tomato diseases: Classification and symptoms visualization
21. Combining deep learning and machine learning for plant disease detection
22. Enhancing plant disease detection using CNN and data augmentation techniques
23. Improved deep learning approach for plant disease recognition on PlantVillage dataset
24. DenseNet-based framework for plant disease recognition
25. Bayesian convolutional neural networks for efficient plant disease detection
26. Adaptive CNN for complex plant disease recognition
27. Crop pest image recognition based on the improved ViT method
28. H. Li, S. Li, J. Yu, Y. Han, and A. Dong, “Plant disease and insect pest identification based on vision transformer,” presented at the Int. Conf. Image Signal Process. Agricul., Beijing, China, SPIE, Apr. 2022.10.1117/12.2628467 ↗
29. ViT-SmartAgri: Vision transformer and smartphone-based plant disease detection for smart agriculture10.3390/agronomy14020327 ↗
30. A deep learning approach for crop disease and pest classification using Swin transformer and dual-attention multi-scale fusion network10.1109/ACCESS.2024.3481675 ↗
31. A dual track deep fusion network for citrus disease classification using group shuffle depthwise feature pyramid and Swin transformer
32. R. Reedha, E. Dericquebourg, R. Canals, and A. Hafiane, “Application of convolution-free vision transformers in small dataset scenarios,” presented at the IEEE Int. Conf. Comput. Vis. Appl., Singapore, 2022.
33. Y. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” presented at the IEEE Conf. Comput. Vis. Pattern Recognit., Las Vegas, NV, USA, Jun. 27–30, 2016.
34. X. Li, W. Wang, X. Hu, and J. Yang, “Selective kernel networks,” presented at the IEEE Conf. Comput. Vis. Pattern Recognit., Long Beach, CA, USA, Jun. 16–20, 2019.
35. A. G. Howard et al., “Searching for MobileNetV3,” presented at the IEEE Int. Conf. Comput. Vis., Seoul, Republic of Korea, Oct. 27–Nov. 2, 2019.
36. S. Han, J. Pool, J. Tran, and W. J. Dally, “Learning both weights and connections for efficient neural networks,” presented at the Adv. Neural Inf. Process. Syst., Montreal, QC, Canada, Dec. 7–12, 2015.
37. PlantDoc Dataset, “A dataset for plant disease detection and classification,” 2019, Accessed: Apr. 16, 2024. [Online]. Available: https://github.com/pratikkayal/PlantDoc-Dataset
38. A. G. Howard et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications,” 2017, arXiv:1704.04861.
39. N. Ma, X. Zhang, H. T. Zheng, and J. Sun, “ShuffleNet V2: Practical guidelines for efficient CNN architecture design,” presented at the Eur. Conf. Comput. Vis., Munich, Germany, Sep. 8–14, 2018.10.1007/978-3-030-01264-9_8 ↗
40. S. Mehta and M. Rastegari, “MobileViT: Light-weight, general-purpose, and mobile-friendly vision transformer,” 2021, arXiv:2110.02178.
41. T-CNN: Trilinear convolutional neural networks model for visual detection of plant diseases10.1016/j.compag.2021.106468 ↗
42. Inception convolutional vision transformers for plant disease identification10.1016/j.iot.2022.100650 ↗