The importance and necessity of protecting beneficial rhizobacteria using artificial intelligence

Document Type : Scientific Letters

Author

Assistant Professor, Agricultural Research, Education and Extension Organization, Tehran, Iran.

10.22092/irn.2026.371786.1716

Abstract

This study explores the role of artificial intelligence (AI) in the protection and management of Iran’s natural resources, with a focus on beneficial microorganisms. The results indicate that AI technologies, including machine learning and neural networks, can effectively contribute to forest monitoring, drought prediction, endangered plant conservation, and bacterial identification. A case study on the bacterial isolate Pseudomonas canadensis demonstrated that integrating biological data with AI analysis provides an efficient tool for sustainable natural resource management. This approach introduces an innovative pathway toward the advancement of smart environmental and agricultural technologies in Iran.

Keywords


Ali, H., Mohammadi, J. and Shataee Jouibary, S., 2024. Deep and machine learning prediction of forest above-ground biomass using multi-source remote sensing data in coniferous planted forests in Iran. European Journal of Forest Research, 143(1): 1731–1745. https://doi.org/10.1007/s10342-024-01721-w
Asadi, H. and Gorji, M., 2022. Challenges and limitations of soil and land resources in Iran. Land Management Journal, 10(1): 111-134. https://doi.org/10.22092/lmj.2022.358760.309
Azizianpour, S., Mirzaei, J., Omidipour, R. and Jafarian, N., 2025. Machine learning based forest fire susceptibility prediction in semi-arid Oak forests of western Iran. Ecopersia, 13(1): 1–19. https://ecopersia.modares.ac.ir/article-24-78131-en.pdf
Barhate, D., Pathak, S., Singh, B.K., Jain, A. and Dubey, A.K., 2024. A systematic review of machine learning and deep learning approaches in plant species detection. Smart Agricultural Technology, 9, Article 100605. https://doi.org/10.1016/j.atech.2024.100605
Causevic, A., Causevic, S., Fielding, M. Barrott, J., 2024. Artificial intelligence for sustainability: Opportunities and risks of utilizing Earth observation technologies to protect forests. Discover Conservation, 1, Article 1. https://doi.org/10.1007/s44353-024-00002-2
Food and Agriculture Organization of the United Nations (FAO), (2020. Global forest resources assessment 2020: Main Report. Rome: FAO. https://doi.org/10.4060/ca9825en
Hou, B., Liang, C., Sheng, X., Liu, Y.G., Ren, J., Ma, Q., Wang, T. and Zhang, L., 2025. Artificial intelligence in medicinal herb breeding. Engineering. https://doi.org/10.1016/j.eng.2025.08.021
Onyebuchi, N., Biu, P., Umoh, A., Obaedo, B., Adegbite, A. and Abatan, A., 2024. Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet. World Journal of Advanced Research and Reviews, 21(1): 161–171. https://doi.org/10.30574/wjarr.2024.21.1.2720
Pace, R., Schiano Di Cola, V. and Monti, M.M., 2025. Artificial intelligence in soil microbiome analysis: A potential application in predicting and enhancing soil health—a review. Discover Applied Science, 7: 85. https://doi.org/10.1007/s42452-024-06381-4
Reckling, W., Mitasova, H., Wegmann, K., Kauffman, G. and Reid, R., 2021. Efficient drone-based rare plant monitoring using a species distribution model and AI-based object detection. Drones, 5(4): 110. https://doi.org/10.3390/drones5040110
Rezaei Danesh, Y., 2025. Harnessing beneficial microbes and sensor technologies for sustainable smart agriculture. Preprints.org. https://doi.org/10.20944/preprints202509.1849.v1
Rolnick, D., Donti, P.L., Kaack, L.H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A.S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A.S., Maharaj, T., Sherwin, E.D., Mukkavilli, S.K., Kording, K.P., Gomes, C.P., Ng, A.Y., Hassabis, D., Platt, J.C., Creutzig, F., Chayes, J. and Bengio, Y., 2022. Tackling climate change with machine learning. ACM Computing Surveys, 55(2): Article 42, 1–96. https://doi.org/10.1145/3485128 
Sarker, I.H., Hossain, M.J. and Mahi, N.J., 2020. Water resource management using artificial intelligence techniques: A review and perspective. Journal of Hydrology, 587: 124983. https://doi.org/10.1016/j.jhydrol.2020.124983