Deep Learning in Strawberry Growth Monitoring Research: A Review
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-53404-1_7
Reference16 articles.
1. Dong, C., Zhang, Z., Yue, J., Zhou, L.: Classification of strawberry diseases and pests by improved alexnet deep learning networks. In: 2021 13th International Conference on Advanced Computational Intelligence (ICACI), pp. 359–364. IEEE (2021)
2. Santiago, A., Solaque, L., Velasco, A.: Strawberry disease detection in precision agriculture. In: Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics - ICINCO, pp. 537–544. INSTICC, SciTePress (2021)
3. Abbas, I., Liu, J., Amin, M., Tariq, A., Tunio, M.H.: Strawberry fungal leaf scorch disease identification in real-time strawberry field using deep learning architectures. Plants 10(12), 2643 (2021)
4. Shin, J., Chang, Y.K., Heung, B., Nguyen-Quang, T., Price, G.W., Al-Mallahi, A.: A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves. Comput. Electron. Agric. 183, 106042 (2021)
5. Hariri, M., Avşar, E.: Tipburn disorder detection in strawberry leaves using convolutional neural networks and particle swarm optimization. Multimedia Tools Appl. 81(8), 11795–11822 (2022)
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