1. Ali, M. M., Bachik, N. A., Muhadi, N. A., Yusof, T. N. T., & Gomes, C. (2019). Non-destructive techniques of detecting plant diseases: A review. Physiological and Molecular Plant Pathology, 108, 101426.
2. Arya, S., Sandhu, K. S., Singh, J., & Kumar, S. (2022). Deep learning: As the new frontier in high-throughput plant phenotyping. Euphytica, 218(4), 47.
3. Buxbaum, N., Lieth, J. H., & Earles, M. (2022). Non-destructive plant biomass monitoring with high spatio-temporal resolution via proximal RGB-d imagery and end-to-end deep learning. Frontiers in Plant Science, 13, 758818.
4. Chang, S., Lee, U., Hong, M. J., Jo, Y. D., & Kim, J. B. (2021). Time-series growth prediction model based on U-net and machine learning in Arabidopsis. Frontiers in Plant Science, 12, 721512.
5. Das Choudhury, S., Samal, A., & Awada, T. (2019). Leveraging image analysis for high-throughput plant phenotyping. Frontiers in Plant Science, 10, 508. https://doi.org/10.3389/fpls.2019.00508