CV
§ Bibliography

Selected references for this portfolio.

Key references from my graduation thesis: the methodology, models, and statistical framework.

  1. Abraham, N., & Khan, N. M. (2019). A novel focal Tversky loss function with improved attention U-Net for lesion segmentation. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 683–687). IEEE. https://doi.org/10.1109/ISBI.2019.8759329
  2. Avelino, J., Cabut, S., Barboza, B., Barquero, M., Alfaro, R., Esquivel, C., Durand, J.-F., & Cilas, C. (2007). Topography and crop management are key factors for the development of American leaf spot epidemics on coffee in Costa Rica. Phytopathology, 97(12), 1532–1542. https://doi.org/10.1094/PHYTO-97-12-1532
  3. 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
  4. Bland, J. M., & Altman, D. G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet, 327(8476), 307–310. https://doi.org/10.1016/S0140-6736(86)90837-8
  5. Bock, C. H., Chiang, K.-S., & Del Ponte, E. M. (2022). Plant disease severity estimated visually: A century of research, best practices, and opportunities for improving methods and practices to maximize accuracy. Tropical Plant Pathology, 47(1), 25–42. https://doi.org/10.1007/s40858-021-00439-z
  6. Capucho, A. S., Zambolim, L., Duarte, H. S. S., & Vaz, G. R. O. (2011). Development and validation of a standard area diagram set to estimate severity of leaf rust in Coffea arabica and C. canephora. Plant Pathology, 60(6), 1144–1150. https://doi.org/10.1111/j.1365-3059.2011.02472.x
  7. Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer vision – ECCV 2018 (pp. 833–851). Springer. https://doi.org/10.1007/978-3-030-01234-2_49
  8. Cohen, J. (1968). Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4), 213–220. https://doi.org/10.1037/h0026256
  9. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248–255). IEEE. https://doi.org/10.1109/CVPR.2009.5206848
  10. Efron, B. (1979). Bootstrap methods: Another look at the jackknife. The Annals of Statistics, 7(1), 1–26. https://doi.org/10.1214/aos/1176344552
  11. 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, Article 105162. https://doi.org/10.1016/j.compag.2019.105162
  12. Gonçalves, J. P., Pinto, F. A. C., Queiroz, D. M., Villar, F. M. M., Barbedo, J. G. A., & Del Ponte, E. M. (2021). Deep learning architectures for semantic segmentation and automatic estimation of severity of foliar symptoms caused by diseases or pests. Biosystems Engineering, 210, 129–142. https://doi.org/10.1016/j.biosystemseng.2021.08.011
  13. Healey, G. E., & Kondepudy, R. (1994). Radiometric CCD camera calibration and noise estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(3), 267–276. https://doi.org/10.1109/34.276126
  14. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.
  15. Loshchilov, I., & Hutter, F. (2019). Decoupled weight decay regularization (arXiv:1711.05101). arXiv. https://doi.org/10.48550/arXiv.1711.05101
  16. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220–229). Association for Computing Machinery. https://doi.org/10.1145/3287560.3287596
  17. Ravi, N., Gabeur, V., Hu, Y.-T., Hu, R., Ryali, C., Ma, T., Khedr, H., Rädle, R., Rolland, C., Gustafson, L., Mintun, E., Pan, J., Alwala, K. V., Carion, N., Wu, C.-Y., Girshick, R., Dollár, P., & Feichtenhofer, C. (2024). SAM 2: Segment anything in images and videos (arXiv:2408.00714). arXiv. https://doi.org/10.48550/arXiv.2408.00714
  18. Romero-Benavides, M. P., Moura, W. de M., & Del Ponte, E. M. (2026). Foundation model-assisted segmentation enables robust field-based severity estimation: A case-study of coffee leaf rust. Plant Pathology, 75(3), Article e70191. https://doi.org/10.1111/ppa.70191
  19. Salehi, S. S. M., Erdogmus, D., & Gholipour, A. (2017). Tversky loss function for image segmentation using 3D fully convolutional deep networks. In Q. Wang, Y. Shi, H.-I. Suk, & K. Suzuki (Eds.), Machine learning in medical imaging (MLMI 2017) (pp. 379–387). Springer. https://doi.org/10.1007/978-3-319-67389-9_44
  20. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4510–4520). IEEE. https://doi.org/10.1109/CVPR.2018.00474
  21. Tassis, L. M., Tozzi de Souza, J. E., & Krohling, R. A. (2021). A deep learning approach combining instance and semantic segmentation to identify diseases and pests of coffee leaves from in-field images. Computers and Electronics in Agriculture, 186, Article 106191. https://doi.org/10.1016/j.compag.2021.106191
  22. Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83. https://doi.org/10.2307/3001968
  23. Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021). SegFormer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34, 12077–12090. https://proceedings.neurips.cc/paper/2021/hash/64f1f27bf1b4ec22924fd0acb550c235-Abstract.html
  24. Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018). UNet++: A nested U-Net architecture for medical image segmentation. In Deep learning in medical image analysis and multimodal learning for clinical decision support (pp. 3–11). Springer. https://doi.org/10.1007/978-3-030-00889-5_1
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