Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

DOI: http://dx.doi.org/10.15439/2024F8703

Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 625–630 (2024)

Full text

Abstract. Citizen science has emerged as a valuable resource for scientific research, providing large volumes of data for training deep learning models. However, the quality and accuracy of crowd-sourced data pose significant challenges for supervised learning tasks such as plant trait detection. This study investigates the application of AI techniques to address these issues within natural science. We explore the potential of multi-modal data analysis and ensemble methods to improve the accuracy of plant trait classification using citizen science data. Additionally, we examine the effectiveness of transfer learning from authoritative datasets like PlantVillage to enhance model performance on open- access platforms such as iNaturalist. By analysing the strengths and limitations of AI-driven approaches in this context, we aim to contribute to developing robust and reliable methods for utilising citizen science data in natural science.

References

1. J. Silvertown, “A new dawn for citizen science,” Trends in ecology & evolution, 2009. http://dx.doi.org/10.1016/j.tree.2009.03.017
2. R. Bonney, C. B. Cooper, J. Dickinson, S. Kelling, T. Phillips, K. V. Rosenberg, and J. Shirk, “Citizen science: a developing tool for expanding science knowledge and scientific literacy,” BioScience, 2009. http://dx.doi.org/10.1525/bio.2009.59.11.9
3. C. L. of Ornithology, “eBird,” last retrieved July 24, 2024. [Online]. Available: https://science.ebird.org/en
4. K. O’Donnell, “iNaturalist,” last retrieved July 24, 2024. [Online]. Available: https://www.inaturalist.org/
5. K. S. Chris Lintott, “Zooniverse,” last retrieved July 24, 2024. [Online]. Available: https://www.zooniverse.org/
6. C. Schiller, S. Schmidtlein, C. Boonman, A. Moreno-Martínez, and T. Kattenborn, “Deep learning and citizen science enable automated plant trait predictions from photographs,” Scientific Reports, 2021. http://dx.doi.org/10.1038/s41598-021-95616-0
7. M. J. Feldman, L. Imbeau, P. Marchand, M. J. Mazerolle, M. Darveau, and N. J. Fenton, “Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review,” PloS one, 2021. http://dx.doi.org/10.1371/journal.pone.0234587
8. S. Wolf, M. D. Mahecha, F. M. Sabatini, C. Wirth, H. Bruelheide, J. Kattge, Á. Moreno Martínez, K. Mora, and T. Kattenborn, “Citizen science plant observations encode global trait patterns,” Nature Ecology & Evolution, 2022. http://dx.doi.org/10.1038/s41559-022-01904-x
9. O. Atkin, J. Kattge, S. Diaz, S. Lavorel, I. C. Prentice, P. Leadley, G. Bonisch, E. Garnier, M. Westoby, P. B. Reich et al., “Try-a global database of plant traits,” Global Change Biology, 2011. http://dx.doi.org/10.1111/j.1365-2486.2011.02451.x
10. WorldClim, “WorldClim,” last retrieved July 24, 2024. [Online]. Available: https://www.worldclim.org/
11. T. Kattenborn, “Planttraits2023,” 2023, competition. [Online]. Available: https://kaggle.com/competitions/planttraits2023
12. A. Awsaf, H.-J. Sharma, M. Görner, and T. Kattenborn, “Planttraits2024 - fgvc11,” 2024, competition. [Online]. Available: https://kaggle.com/competitions/planttraits2024
13. A. Bruno, D. Moroni, and M. Martinelli, “Efficient deep learning approach for olive disease classification,” in 2023 18th Conference on Computer Science and Intelligence Systems (FedCSIS), 2023. http://dx.doi.org/10.15439/2023F4794
14. S. Woo, S. Debnath, R. Hu, X. Chen, Z. Liu, I. S. Kweon, and S. Xie, “Convnext v2: Co-designing and scaling convnets with masked autoencoders,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. http://dx.doi.org/10.1109/CVPR52729.2023.01548
15. C. Schiller, “CNN Models, metadata and global trait distribution maps,” dataset Repository. [Online]. Available: https://figshare.com/articles/dataset/CNN_Models_metadata_and_global_trait_distribution_maps/13312040
16. T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. http://dx.doi.org/10.1145/2939672.2939785
17. L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, “Catboost: unbiased boosting with categorical features,” Advances in neural information processing systems, 2018. http://dx.doi.org/https://doi.org/10.48550/arXiv.1706.09516
18. S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo, “Cutmix: Regularization strategy to train strong classifiers with localizable features,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019. http://dx.doi.org/10.1109/ICCV.2019.00612
19. A. Bruno, D. Moroni, R. Dainelli, L. Rocchi, S. Morelli, E. Ferrari, P. Toscano, and M. Martinelli, “Improving plant disease classification by adaptive minimal ensembling,” Frontiers in Artificial Intelligence, 2022. http://dx.doi.org/10.3389/frai.2022.868926
20. S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in plant science, 2016. http://dx.doi.org/10.3389/fpls.2016.01419
21. Y. Gorishniy, I. Rubachev, V. Khrulkov, and A. Babenko, “Revisiting deep learning models for tabular data,” Advances in Neural Information Processing Systems, 2021. http://dx.doi.org/10.48550/arXiv.2106.11959
22. P. Soroye, T. Newbold, and J. Kerr, “Climate change contributes to widespread declines among bumble bees across continents,” Science, 2020. http://dx.doi.org/10.1126/science.aax8591
23. F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, and Q. He, “A comprehensive survey on transfer learning,” Proceedings of the IEEE, 2019. http://dx.doi.org/10.48550/arXiv.1911.02685
24. Z.-A. Huang, Y. Hu, R. Liu, X. Xue, Z. Zhu, L. Song, and K. C. Tan, “Federated multi-task learning for joint diagnosis of multiple mental disorders on mri scans,” IEEE Transactions on Biomedical Engineering, 2022. http://dx.doi.org/10.1109/TBME.2022.3210940