An Effective Framework for the Background Removal of Tomato Leaf Disease Using Residual Transformer Network
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61298-5_8
Reference19 articles.
1. Yang, G., Chen, G., He, Y., Yan, Z., Guo, Y., Ding, J.: Self-supervised collaborative multi-network for fine-grained visual categorization of tomato diseases. IEEE Access 8, 211912–211923 (2020)
2. Schor, N., Bechar, A., Ignat, T., Dombrovsky, A., Elad, Y., Berman, S.: Robotic disease detection in greenhouses: combined detection of powdery mildew and tomato spotted wilt virus. IEEE Robot. Autom. Lett. 1(1), 354–360 (2016)
3. Zhang, Y., Song, C., Zhang, D.: Deep learning-based object detection improvement for tomato disease. IEEE Access 8, 56607–56614 (2020)
4. Wu, Q., Chen, Y., Meng, J.: DCGAN-based data augmentation for tomato leaf disease identification. IEEE Access 8, 98716–98728 (2020)
5. Liu, J., Wang, X.: Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model. Plant Methods 16, 83 (2020). https://doi.org/10.1186/s13007-020-00624-2
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