§ Bibliography
Selected references for this portfolio.
Citations underpinning the methodology, models, and statistical framework of the graduation project.
- Avelino, J., Cristancho, M., Georgiou, S., Imbach, P., Aguilar, L., Bornemann, G., Läderach, P., Anzueto, F., Hruska, A. J., & Morales, C. (2015). The coffee rust crises in Colombia and Central America (2008–2013): Impacts, plausible causes and proposed solutions. Food Security, 7(2), 303–321. https://doi.org/10.1007/s12571-015-0446-9
- Capucho, A. S., Zambolim, L., Lopes, U. N., & Milagres, N. S. (2011). Chemical control of coffee leaf rust in Coffea canephora cv. conilon. Plant Pathology, 60(6), 1144–1150. https://doi.org/10.1111/j.1365-3059.2011.02472.x
- Esgario, J. G. M., Krohling, R. A., & Ventura, J. A. (2020). Deep learning for classification and severity estimation of coffee leaf biotic stress. Computers and Electronics in Agriculture, 169, 105162. https://doi.org/10.1016/j.compag.2019.105162
- Eskes, A. B. (1983). Incomplete resistance to coffee leaf rust (Hemileia vastatrix) [Doctoral dissertation, Wageningen Agricultural University].
- Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. In D. Precup & Y. W. Teh (Eds.), Proceedings of the 34th International Conference on Machine Learning (Vol. 70, pp. 1321–1330). PMLR.
- Kruschke, J. K. (2013). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142(2), 573–603. https://doi.org/10.1037/a0029146
- Lakens, D. (2017). Equivalence tests: A practical primer for t tests, correlations, and meta-analyses. Social Psychological and Personality Science, 8(4), 355–362. https://doi.org/10.1177/1948550617697177
- Li, J., Liang, S., Wang, R., Liu, T., Sun, X., Lu, J., Yang, J., Zhang, Y., Liu, T., & Zhao, B. (2025). EMSAM: Enhanced segmentation with the Segment Anything Model for plant pathology imagery. Frontiers in Plant Science, 16, 1564079. https://doi.org/10.3389/fpls.2025.1564079
- Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.-Y., Li, S.-W., Misra, I., Rabbat, M., Sharma, V., … Bojanowski, P. (2024). DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research. https://openreview.net/forum?id=a68SUt6zFt
- Schuirmann, D. J. (1987). A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. Journal of Pharmacokinetics and Biopharmaceutics, 15(6), 657–680. https://doi.org/10.1007/BF01068419
- Shi, X., Cao, W., & Raschka, S. (2023). Deep neural networks for rank-consistent ordinal regression based on conditional probabilities. Pattern Analysis and Applications, 26(3), 941–955. https://doi.org/10.1007/s10044-023-01155-x
- Vanbelle, S., & Albert, A. (2008). A bootstrap method for comparing correlated kappa coefficients. Journal of Statistical Computation and Simulation, 78(11), 1009–1015. https://doi.org/10.1080/00949650701754972
- Vasu, P. K. A., Gabriel, J., Zhu, J., Tuzel, O., & Ranjan, A. (2023). FastViT: A fast hybrid vision transformer using structural reparameterization. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 5785–5795). IEEE. https://doi.org/10.1109/ICCV51070.2023.00532
- Warfield, S. K., Zou, K. H., & Wells, W. M. (2004). Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation. IEEE Transactions on Medical Imaging, 23(7), 903–921. https://doi.org/10.1109/TMI.2004.828354
- Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021). SegFormer: Simple and efficient design for semantic segmentation with transformers. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. W. Vaughan (Eds.), Advances in Neural Information Processing Systems (Vol. 34, pp. 12077–12090). Curran Associates.