logo
O‘zbekcha

AQLLI KO‘CHAT EKISH MASHINASI – YASHIL KELAJAK UCHUN INNOVATSIYA

Mualliflar

DOI:

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

Kalit so‘zlar:

SmartPlant, aqlli ko‘chat ekish mashinasi, Orolbo‘yi, ekologiya, cho‘llanish, sun’iy intellekt.

Annotatsiya

Orol dengizi mintaqasida yuzaga kelgan ekologik muammolar, jumladan cho‘llanish, tuproq unumdorligining pasayishi va bioxilma-xillikning qisqarishi hududning barqaror rivojlanishiga jiddiy tahdid solmoqda. An’anaviy qo‘lda amalga oshiriladigan ko‘chat ekish usullari katta vaqt va resurs talab qiladi, shu bilan birga qattiq iqlim sharoitida ekilgan ko‘chatlarning ko‘pchiligi ildiz otmaydi. Ushbu muammoni bartaraf etish maqsadida “SmartPlant – Aqlli ko‘chat ekish mashinasi” startap loyihasi ishlab chiqildi. Qurilma sun’iy intellekt va avtomatlashtirilgan tizimlarga asoslangan bo‘lib, tuproqning namligi, kimyoviy tarkibi va hududning iqlim sharoitlarini tahlil qilib, optimal joylarda ko‘chatlarni ekishni ta’minlaydi. Shuningdek, qurilma ekilgan ko‘chatlarning monitoringini olib boradi, ularning ildiz otishi va rivojlanishini kuzatib, samaradorlikni oshiradi. Mazkur innovatsion yondashuv ekologik muhitni tiklash, cho‘llanish jarayonini kamaytirish va yashil maydonlarni kengaytirishga xizmat qiladi. Natijada, loyiha nafaqat Orolbo‘yi, balki butun Markaziy Osiyo ekologik barqarorligini ta’minlashda muhim ahamiyat kasb etadi.

Mualliflar haqida

  • 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

Adabiyotlar

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.

Yuklab olishlar

Nashr etilgan

2026-02-03

Qanday iqtibos keltirish

AQLLI KO‘CHAT EKISH MASHINASI – YASHIL KELAJAK UCHUN INNOVATSIYA. (2026). Scientific Journal of the Fergana State University, 31(6), 266. https://doi.org/10.56292/SJFSU/vol31_iss6/a266

Xuddi shu muallif (lar) ning eng ko'p o'qilgan maqolalari