From neural networks to deep learning: Tracing the AI evolution in plant ecology
DOI:
https://doi.org/10.12775/EQ.2026.022Keywords
bibliometrics, science mapping, species distribution modeling, random forests, precision agricultureAbstract
The integration of artificial intelligence (AI) with plant ecology provides a powerful supplementary toolkit for analyzing complex environmental data, yet the structure of this rapidly growing field remains unmapped. This study employs a comprehensive bibliometric analysis of 1055 Scopus documents (1968-2025) to chart its conceptual, intellectual, and social landscape. Our analysis reveals a distinct three-phase evolution from foundational Artificial Neural Networks (ANNs) to a broad machine learning expansion, and a current surge in deep learning for applications like precision agriculture. The field’s conceptual core is built on predictive modeling, particularly for species distribution, and advanced image analytics. We highlight that this evolution has been largely driven by the increasing availability of open-source programming libraries (e.g., in R and Python) and accessible software platforms, rather than just the theoretical advancement of AI itself. The global collaboration network is multi-polar, with the USA and China acting as prominent hubs. This research maps the trajectory of AI applications in ecology and emphasizes the emerging frontier of Explainable AI (XAI), which is essential for moving beyond "black-box" predictions toward interpretable ecological insights and fundamental scientific discovery
References
Adams, J. (2013). The fourth age of research. Nature, 497(7451), 557–560. https://doi.org/10.1038/497557a
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Austin, M. P. (2007). Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling, 200(1-2), 1–19. https://doi.org/10.1016/j.ecolmodel.2006.07.005
Bartold, M., & Kluczek, M. (2023). A Machine Learning Approach for Mapping Chlorophyll Fluorescence at Inland Wetlands. Remote Sensing, 15(9), 2392. https://doi.org/10.3390/rs15092392
Bartold, M., Wróblewski, K., Kluczek, M., & Dąbrowska-Zielińska, K. (2024). Mapping management intensity types in grasslands with synergistic use of Sentinel-1 and Sentinel-2 satellite images. Scientific Reports, 14, 32066. https://doi.org/10.1038/s41598-024-83699-4
Bodin, Ö., Crona, B., & Ernstson, H. (2006). Social networks in natural resource management: What is there to learn from a structural perspective?. Ecology and Society, 11(2), r2. https://doi.org/10.5751/ES-01808-1102r02
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Burt, R. S. (2004). Structural holes and good ideas. American Journal of Sociology, 110(2), 349–399. https://doi.org/10.1086/421787
Christin, S., Hervet, E., & Lecomte, N. (2019). Applications for deep learning in ecology. Methods in Ecology and Evolution, 10(10), 1632–1644. https://doi.org/10.1111/2041-210X.13256
Crane, D. (1972). Invisible colleges: Diffusion of knowledge in scientific communities. The University of Chicago Press.
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695. https://igraph.org
Cutler, D. R., Edwards, T. C. Jr., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random forests for classification in ecology. Ecology, 88(11), 2783–2792. https://doi.org/10.1890/07-0539.1
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Elith, J., Graham, C. H., Anderson, R. P., Dudík, M., Ferrier, S., Guisan, A., Hijmans, R. J., Huettmann, F., Leathwick, J. R., Lehmann, A., Li, J., Lohmann, L. G., Loiselle, B. A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J. M., Peterson, A. T., Phillips, S. J., Richardson, K., Scachetti-Pereira, R., Schapire, R. E., Soberón, J., Williams, S., Wisz, M. S., & Zimmermann, N. E. (2006). Novel methods improve prediction of species' distributions from occurrence data. Ecography, 29(2), 129–151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
Elith, J., & Leathwick, J. R. (2009). Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40, 677–697. https://doi.org/10.1146/annurev.ecolsys.110308.120159
Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x
Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24(1), 38–49. https://doi.org/10.1017/S0376892997000088
Franklin, J. (2010). Mapping species distributions: spatial inference and prediction. Cambridge University Press.
Grace, J. B., Anderson, T. M., Olff, H., & Scheiner, S. M. (2010). On the specification of structural equation models for ecological systems. Ecological Monographs, 80(1), 67–87. https://doi.org/10.1890/09-0464.1
Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135(2-3), 147–186. https://doi.org/10.1016/S0304-3800(00)00354-9
Heikkinen, R. K., Luoto, M., Araújo, M. B., Virkkala, R., Thuiller, W., & Sykes, M. T. (2006). Methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography, 30(6), 751–777. https://doi.org/10.1177/0309133306071957
Hey, T., Tansley, S., & Tolle, K. (Eds.). (2009). The fourth paradigm: Data-intensive scientific discovery. Microsoft Research. https://www.microsoft.com/en-us/research/wp-content/uploads/2009/10/Fourth_Paradigm.pdf
Hoekman, J., Frenken, K., & van Oort, F. (2009). The geography of collaborative knowledge production in Europe. Annals of Regional Science, 43(3), 721–738. https://doi.org/10.1007/s00168-008-0252-9
IPBES (2019). Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPBES secretariat. https://doi.org/10.5281/zenodo.3831673
Jones, M. B., Schildhauer, M. P., Reichman, O. J., & Bowers, S. (2006). The new bioinformatics: integrating ecological data from the gene to the biosphere. Annual Review of Ecology, Evolution, and Systematics, 37, 519–544. https://doi.org/10.1146/annurev.ecolsys.37.091305.110031
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and challenges. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016
Kattenborn, T., Leitloff, F., Schiefer, F., & Hinz, S. (2021). Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 24–49. https://doi.org/10.1016/j.isprsjprs.2020.12.010
King, D. A. (2004). The scientific impact of nations. Nature, 430(6997), 311–316. https://doi.org/10.1038/430311a
Lamba, A., Cassey, P., Segaran, R. R., & Koh, L. P. (2019). Deep learning for environmental conservation. Current Biology, 29(19), R977–R982. https://doi.org/10.1016/j.cub.2019.08.016
Langley, P. (1996). Elements of machine learning. Morgan Kaufmann Publishers.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lek, S., Delacoste, M., Baran, P., Dimopoulos, I., Lauga, J., & Aulagnier, S. (1996). Application of neural networks to modelling nonlinear relationships in ecology. Ecological Modelling, 90(1), 39–52. https://doi.org/10.1016/0304-3800(95)00142-5
Levin, S. A. (1992). The problem of pattern and scale in ecology: The Robert H. MacArthur award lecture. Ecology, 73(6), 1943–1967. https://doi.org/10.2307/1941447
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22.
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K., & Thuiller, W. (2009). Evaluation of consensus methods in predictive species distribution modelling. Diversity and Distributions, 15(1), 59–69. https://doi.org/10.1111/j.1472-4642.2008.00491.x
Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (1983). Machine learning: An artificial intelligence approach. Tioga Publishing Company. https://doi.org/10.1007/978-3-662-12405-5
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419
Molnar, C. (2020). Interpretable machine learning: A guide for making black box models explainable. https://christophm.github.io/interpretable-ml-book/
Narin, F., Stevens, K. S., & Whitlow, E. S. (1991). Scientific co-operation in Europe and the citation of multinationally authored papers. Scientometrics, 21(3), 313–323. https://doi.org/10.1007/BF02093973
Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(Suppl 1), 5200–5205. https://doi.org/10.1073/pnas.0307545100
Norberg, A., Abrego, N., Blanchet, F. G., Adler, F. R., Anderson, B. J., Anttila, J., Araújo, M. B., Dallas, T., Dunson, D., Elith, J., Foster, S. D., Fox, R., Franklin, J., Godsoe, W., Guisan, A., O'Hara, B., Hill, N. A., Holt, R. D., Hui, F. K. C., Husby, M., Kålås, J. A., Lehikoinen, A., Luoto, M., Mod, H. K., Newell, G., Renner, I., Roslin, T., Soininen, J., Thuiller, W., Vanhatalo, J., Warton, D., White, M., Zimmermann, N. E., Gravel, D., & Ovaskainen, O. (2019). A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecological Monographs, 89(3), e01370. https://doi.org/10.1002/ecm.1370
Olden, J. D., & Jackson, D. A. (2002). Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154(1-2), 135–150. https://doi.org/10.1016/S0304-3800(02)00064-9
Olden, J. D., Lawler, J. J., & Poff, N. L. (2008). Machine learning methods without tears: a primer for ecologists. The Quarterly Review of Biology, 83(2), 171–193. https://doi.org/10.1086/587826
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Prasad, A. M., Iverson, L. R., & Liaw, A. (2006). Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems, 9, 181–199. https://doi.org/10.1007/s10021-005-0054-1
R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
Recknagel, F. (2001). Applications of machine learning to ecological modelling. Ecological Modelling, 146(1-3), 303–310. https://doi.org/10.1016/S0304-3800(01)00316-7
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195–204. https://doi.org/10.1038/s41586-019-0912-1
Roscher, R., Bohn, B., Duarte, M. F., & Garcke, J. (2020). Explainable Machine Learning for Scientific Insights and Discoveries. IEEE Access, 8, 44963–44985. https://doi.org/10.1109/ACCESS.2020.2976199
Royal Society (2011). Knowledge, networks and nations: Global scientific collaboration in the 21st century. The Royal Society.
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
Singh, A., Ganapathysubramanian, B., Singh, A. K., & Sarkar, S. (2016). Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science, 21(2), 110–124. https://doi.org/10.1016/j.tplants.2015.10.015
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016, 3289801. https://doi.org/10.1155/2016/3289801
Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinformatics, 8(1), 25. https://doi.org/10.1186/1471-2105-8-25
Thessen, A. E. (2016). Adoption of machine learning techniques in ecology and earth science. One Ecosystem, 1, e8622. https://doi.org/10.3897/oneeco.1.e8621
Uzzi, B. (1997). Social structure and competition in interfirm networks: The paradox of embeddedness. Administrative Science Quarterly, 42(1), 35–67. https://doi.org/10.2307/2393808
Valavi, R., Elith, J., Lahoz-Monfort, J. J., & Guillera-Arroita, G. (2021). Modelling species presence-only data with random forests. Ecography, 44(12), 1731–1742. https://doi.org/10.1111/ecog.05615
Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442. https://doi.org/10.1038/30918
Weinstein, B. G., Marconi, S., Bohlman, S., Zare, A., & White, E. (2019). Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks. Remote Sensing, 11(11), 1309. https://doi.org/10.3390/rs11111309
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org
Wulder, M. A., Masek, J. G., Cohen, W. B., Loveland, T. R., & Woodcock, C. E. (2012). Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment, 122, 2–10. https://doi.org/10.1016/j.rse.2012.01.010
Zhou, P., & Leydesdorff, L. (2006). The emergence of China as a leading nation in science. Research Policy, 35(1), 83–104. https://doi.org/10.1016/j.respol.2005.08.006
Zhu, X. X., Tuia, D., Mou, L., Xia, G. S., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4), 8–36. https://doi.org/10.1109/MGRS.2017.2762307
Zuccala, A. (2006). Modeling the invisible college. Journal of the American Society for Information Science and Technology, 57(2), 152–168. https://doi.org/10.1002/asi.20256
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
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