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RAQAMLI QISHLOQ XO‘JALIGI UCHUN SUN’IY INTELLEKT TEXNOLOGIYALARI: SAMARADORLIK VA BARQARORLIK

Fan yo'nalishi:Sun'iy intellektHisoblash nazariyasi va matematikaKompyuter grafikasi va kompyuter yordamida dizaynKompyuter tarmoqlari va kommunikatsiyalariAxborot tizimlariSignalni qayta ishlashDasturiy ta'minot
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“Raqamli iqtisodiyot” ilmiy-elektron jurnali

“Raqamli iqtisodiyot” ilmiy-elektron jurnali

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MAQOLA ANNOTATSIYASI

quote
Sun’iy intellekt (SI) zamonaviy qishloq xo‘jaligida muhim omilga aylanib, mahsuldorlikni oshirish, resurslardan samarali foydalanish va barqarorlikni ta’minlashga xizmat qilmoqda. SI ilovalari ekinlarni monitoring qilish, sug‘orishni boshqarish, kasalliklarni aniqlash va hosildorlikni bashorat qilishda keng qo‘llanilib, aniq va samarali dehqonchilikka imkon yaratmoqda. Shu bilan birga, SI texnologiyalarini joriy etishda infratuzilma cheklanganligi, yuqori xarajatlar va fermerlar orasida texnik bilim yetishmasligi kabi muammolar mavjud. Ushbu maqolada ilmiy manbalar asosida SI ilovalari funksiyalari bo‘yicha tasniflanib, ularning samaradorligi baholangan hamda tatbiq etishdagi to‘siqlar muhokama qilingan. Tadqiqot SI ni keng ko‘lamda joriy etishda fanlararo hamkorlik va qo‘llab quvvatlovchi siyosiy choralar zarurligini ta’kidlaydi.

MUALLIFLAR

X.Abasxanova

"MUHAMMAD AL-XORAZMIY NOMIDAGI TOSHKENT AXBOROT TEXNOLOGIYALARI UNIVERSITETI" DAVLAT MUASSASASI

Teglar

# monitoring# intellekt# bashorat# aniq# dehqonchilik# boshqarish# qishloq# sun’iy# o‘rganish# qilish# xo‘jaligi# mashinaviy# ekinlarni# sug‘orishni# hosildorlikni

O'XSHASH MAQOLALAR

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Maqola idintifikatorlari

ROI:

https://eroi.uz/11.142/A026-332612

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Mavjud emas

Foydalanilgan adabiyotlar

1. Bannerjee, G., Sarkar, U., Das, S., & Indrajit, P. (2018). Artificial intelligence in agriculture: A literature survey. International Journal of Scientific Research in Computer Science Applications and Management Studies, 7(3), 1–6. 2. Kumar, A., & Singh, R. (2019). Artificial intelligence in irrigation management: A sustainable approach. Journal of Agricultural Engineering, 56(2), 45 54. 3. Elavarasan, D., & Vincent, P. M. D. R. (2020). Crop yield prediction using deep learning techniques—A review. Computers and Electronics in Agriculture, 178, 105–124. [https://doi.org/10.1016/j.compag.2020.105124](https://doi.org/10.1016/j.compag.20 20.105124) 4. Patil, V. C., & Kale, S. M. (2016). Precision farming: The future of Indian agriculture. Indian Journal of Agronomy, 61(3), 243–252. 5. Singh, V., & Misra, A. K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4(1), 41–49. [https://doi.org/10.1016/j.inpa.2016.10.005](https://doi.org/10.1016/j.inpa.2016.10.00 5)

6. Xalima, A. (2023). EFFICIENCY OF APPLYING INNOVATIVE TECHNOLOGIES TO THE PLANT DEVELOPMENT. Universum: технические науки, (7-4 (112)), 33-35. 7. Xalima, A. (2024). THE EFFICIENCY OF ARTIFICIAL INTELLIGENCE IN AGRICULTURAL DEVELOPMENT. Universum: технические науки, 10(11 (128)), 40-42. 8. Xalima, A., Soxibjamol, R., E’Zoza, M., & Sabrina, S. (2024). THE ROLE OF DIGITAL TECHNOLOGIES IN INCREASING THE EFFICIENCY OF AGRICULTURAL ENTERPRISES. Universum: технические науки, 9(4 (121)), 11 13. 9. Yunusovna, A. X. (2024). ENVIRONMENTAL COMPLEXITY AND GREEN INNOVATION: THE SIGNIFICANCE OF OPEN INNOVATION. SCIENTIFIC APPROACH TO THE MODERN EDUCATION SYSTEM, 3(26), 332 338. 10. Yunusovna, A. X., & Anvarjon o‘g‘li, M. S. (2025). METHODS OF USING THE VERILOG PROGRAMMING LANGUAGE FOR LOGIC DEVICE PROGRAMMING. Prospects for innovative technologies in science and education, 2(2), 136-139.