Enhancing Deep Learning-Based Semantic Segmentation Approaches for Smart Agriculture
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50300-9_21
Reference18 articles.
1. Alrowais, F., Asiri, M. M., Alabdan, R., Marzouk, R., Hilal, A. M., Alkhayyat, A., & Gupta, D. (2022). Hybrid leader based optimization with deep learning driven weed detection on internet of things enabled smart agriculture environment. Computers and Electrical Engineering, 104, 108411. https://doi.org/10.1016/j.compeleceng.2022.108411.
2. Amrani, A., Sohel, F., Diepeveen, D., Murray, D., & Jones, M. G. K. (2023). Deep learning-based detection of aphid colonies on plants from a reconstructed brassica image dataset. Computers and Electronics in Agriculture, 205, 107587. https://doi.org/10.1016/j.compag.2022.107587.
3. Chen, W., Xu, Y., Zhang, Z., Yang, L., Pan, X., & Jia, Z. (2021). Mapping agricultural plastic greenhouses using google earth images and deep learning. Computers and Electronics in Agriculture, 191, 106552. https://doi.org/10.1016/j.compag.2021.106552.
4. Coulibaly, S., Kamsu-Foguem, B., Kamissoko, D., & Traore, D. (2022). Deep learning for precision agriculture: A bibliometric analysis. Intelligent Systems with Applications, 16, 200102. https://doi.org/10.1016/j.iswa.2022.200102.
5. Padmapriya, J., & Sasilatha, T. (2023). Deep learning based multi-labelled soil classification and empirical estimation toward sustainable agriculture. Engineering Applications of Artificial Intelligence, 119, 105690. https://doi.org/10.1016/j.engappai.2022.105690.
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