logo
O‘zbekcha

SMART SEEDLING MACHINE – INNOVATION FOR A GREEN FUTURE

Authors

DOI:

https://doi.org/10.56292/SJFSU/vol31_iss6/a266

Keywords:

SmartPlant, intelligent seedling planting machine, Aral Sea region, ecology, desertification, artificial intelligence.

Abstract

The ecological challenges in the Aral Sea region, including desertification, soil degradation, and the loss of biodiversity, pose a serious threat to sustainable development. Traditional manual tree-planting methods require significant time and resources, while many seedlings fail to survive in the harsh climatic conditions. To address these challenges, the “SmartPlant – Intelligent Seedling Planting Machine” project has been developed. This device is based on artificial intelligence and automated systems, enabling analysis of soil moisture, chemical composition, and climate conditions to ensure optimal planting sites for seedlings. Moreover, the machine provides continuous monitoring of the planted seedlings, tracking their root development and growth to enhance efficiency. This innovative approach contributes to environmental restoration, reduces desertification, and expands green areas. Ultimately, the project plays an important role not only in the Aral Sea region but also in ensuring ecological sustainability across Central Asia.

Author Biographies

  • Dadajonov Baxtiyorjon Akramovich, Farg‘ona davlat texnik universiteti

    Farg‘ona davlat texnik universiteti Mexanika-mashinasozlik fakulteti “Transport vositalari muhandisligi kafedrasi” Katta o‘qituvchisi

  • Abdubannopov Abdulatif Abdulxaq o‘g‘li, Farg‘ona davlat texnik universiteti

    Farg‘ona davlat texnik universiteti Mexanika-mashinasozlik fakulteti “Transport vositalari muhandisligi kafedrasi” assistant

References

1.FAO. (2024). The State of Food and Agriculture 2024: Innovation in agrifood systems. https://www.fao.org

2.Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Elec-tronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016

3.Zhang, M., et al. (2022). Machine vision-based weed detection and classification. Computers and Electron-ics in Agriculture, 196, 106885.

4.Alahi, M. E. E., & Mukhopadhyay, S. C. (2018). Plant disease detection using smart sensors. Springer.

5.Liu, B., et al. (2020). Real-time plant disease detection using CNN. IEEE Access, 8, 135558–135569.

6.Jayasundara, D. M., et al. (2023). AgroAI: AI model for plant and soil health. Agricultural Systems, 210, 103599.

7.Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease de-tection. Frontiers in Plant Science, 7, 1419.

8.Sladojevic, S., et al. (2016). Deep neural networks for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 124, 93–99.

9.Koirala, A., et al. (2019). Deep learning for fruit detection and counting: A review. Computers and Electronics in Agriculture, 162, 219–234.

10.Ferentinos, K. P. (2018). Deep learning models for plant disease detection. Computers and Electronics in Agriculture, 145, 311–318.

11.Deng, L., et al. (2021). Weed recognition using CNN. Sensors, 21(4), 1293.

12.Zhang, Y., et al. (2020). Automatic weed detection using UAV imagery and deep learning. Remote Sens-ing, 12(1), 25.

13.Islam, M., et al. (2021). IoT-based smart agriculture. Sensors, 21(19), 6758.

14.Verma, R., et al. (2020). Agriculture 4.0: AI-based solutions. International Journal of Scientific & Technology Research, 9(2), 3473–3476.

15.Davis, S. C., et al. (2019). Soil fertility monitoring using sensors. Journal of Precision Agriculture, 20, 223–235.

16.Khodjaev, B. (2021). O‘zbekiston sharoitida o‘simlik kasalliklarini aniqlash usullari. Agrar fanlar jurnali, 3(9), 15–21.

17.Shavkatov, A., & Karimov, I. (2021). IoT texnologiyalari va kichik dehqonchilik. O‘zbekiston agrar fanlari jurnali, 4(12), 35–41.

18.Yang, G., et al. (2020). Crop monitoring using drones and AI. Computers and Electronics in Agriculture, 174, 105499.

19.Kussul, N., et al. (2017). Deep learning for plant stress detection. IEEE International Geoscience and Re-mote Sensing Symposium (IGARSS), 5469–5472.

20.Bekchanova, M. T. (2022). Raqamli texnologiyalar va fermer xo‘jaliklari. Innovatsion qishloq xo‘jaligi, 3(7), 17–23.

21.Al-Kodmany, K. (2018). Smart farming: Global trends. International Journal of Agricultural Research, 13(2), 76–92.

22.Singh, A., et al. (2019). Edge computing in agriculture: A review. IEEE Access, 7, 156999–157014.

23.Whelan, B., & McBratney, A. B. (2003). Precision agriculture: Rationale and developments. International Journal of Applied Earth Observation and Geoinformation, 4(1), 1–9.

24.FAO & ITU. (2022). Digital Agriculture: Technology Briefs for Sustainable Rural Development.

Downloads

Published

2026-02-03

How to Cite

SMART SEEDLING MACHINE – INNOVATION FOR A GREEN FUTURE. (2026). Scientific Journal of the Fergana State University, 31(6), 266. https://doi.org/10.56292/SJFSU/vol31_iss6/a266