Kalit so‘zlar
rang sensori
Arduino mikrokontrolleri
avtomatlashtirilgan tizim
RGB rang komponentlari
mexanik saralash
sun’iy intellekt
real vaqt rejimi
Iqtibos keltirish tartibi
Annotatsiya
Ushbu maqolada Arduino mikrokontrolleri va TCS230 rang sensori asosida meva mahsulotlarini rang xususiyatlariga ko‘ra avtomatik saralash tizimini ishlab chiqish masalalari yoritilgan. Taklif etilgan tizim mevalarning tashqi rang ko‘rsatkichlarini real vaqt rejimida aniqlash, RGB rang komponentlarini qayta ishlash va ularni oldindan belgilangan mezonlar asosida tasniflash imkonini beradi. Tadqiqot qishloq xo‘jaligi va oziq-ovqat sanoatida mahsulot sifatini baholash jarayonlarini avtomatlashtirishga qaratilgan.
Tizimning ishlash algoritmi rang sensoridan olingan ma’lumotlarni qayta ishlash, infraqizil datchiklar yordamida obyektni aniqlash va mexanik saralash mexanizmini boshqarish bosqichlarini o‘z ichiga oladi. Rang sensorini kalibrlash orqali tashqi yoritish sharoitlarining ta’siri kamaytirilib, rangni aniqlash aniqligi oshirildi. Tajriba natijalari tizimning barqaror ishlashi va mevalarni rangiga ko‘ra ishonchli saralash imkoniyatini ko‘rsatdi.
Shuningdek, maqolada sun’iy intellekt elementlari, xususan neyron tarmoqlaridan foydalanish istiqbollari tahlil qilinadi. Rangga asoslangan an’anaviy chegaraviy usullar bilan solishtirganda, intellektual yondashuvlar rang farqlarini yanada nozik aniqlash imkonini beradi. Tadqiqot natijalari avtomatik meva saralash tizimlarini rivojlantirish va ishlab chiqarish samaradorligini oshirish uchun amaliy ahamiyatga ega.
Adabiyotlar ro‘yxati
[1] Blasco J., Cubero S., Moltó E. Vision-based quality inspection of fruits and vegetables. Biosystems Engineering, 2018, 176, pp. 97–108.
[2] Uljaev E., Abdixalilov U., Suleymanova S., Tadjitdinov G.
Analysis of Methods and Means of Fruit Sorting, Selection of a Rational Method of Fruit Sorting.
International Journal of Mechatronics and Applied Mechanics, 2025, Issue 22, Vol. II, pp. 233–241.
[3] Patel K.K., Kar A., Jha S.N., Khan M.A. Machine vision system: a tool for quality inspection of food and agricultural products. Journal of Food Science and Technology, 2018, 55(6), pp. 1974–1989.
[4] Uljaev E., Abdixalilov O‘.U. Sun’iy intellekt asosida meva saralash tizimlarini takomillashtirish: mexanik va optik usullar tahlili. The Role and Prospects of Modeling in the Processes of Digital Transformation: Xalqaro ilmiy va ilmiy-texnik konferensiya materiallari, 1-qism, Namangan: Namangan davlat texnika universiteti, 2025, 4–5-dekabr.
[5] Kumar S., Singh R. Arduino-based low-cost color sensing system for agricultural applications. IEEE Sensors Journal, 2020, 20(12), pp. 6789–6796.
[6] Perez-Borrero I., et al. Fruit quality assessment using color and vision-based systems. Computers and Electronics in Agriculture, 2020, 175, 105–118.
[7] Li J., Rao X., Ying Y. Calibration methods for color sensors in agricultural product grading. Sensors, 2021, 21(8), 2674.
[8] Kamilaris A., Prenafeta-Boldú F.X. Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 2018, 147, pp. 70–90.
[9] Mohanty S.P., Hughes D.P., Salathé M. Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 2019, 10, 272.
[10] G‘ayimnazarov I. X. UDC 532.543: 627.157: Calculation of the parameters of the base rows in a non-stationary flow //Innovatsion texnologiyalar. – 2025. – Т. 59. – №. 3. – С. 62-66.
[11] Lu Y., Yi S., Zeng N. Identification of fruit ripeness based on color features and machine learning. Measurement, 2022, 187, 110–118.
[12] Gonzales R. C., Woods R. E. Digital Image Processing. 4th ed., Pearson Education, New York, 2018.
[13] Jayaraman S., Esakkirajan S., Veerakumar T. Digital Image Processing. Tata McGraw-Hill Education, New Delhi, 2011.
[14] Haykin S. Neural Networks and Learning Machines. 3rd ed., Pearson Education, New York, 2009.
[15] Goodfellow I., Bengio Y., Courville A. Deep Learning. MIT Press, Cambridge, 2016.
Ushbu asar Creative Commons Attribution 4.0 Xalqaro Litsenziyasi asosida litsenziyalangan.
Mualliflik huquqi (c) 2026 Uljaev E., Abdixalilov O‘.U. (Muallif)