BOG‘DORCHILIKDA ZAMONAVIY SUG‘ORISH TIZIMLARINI BOSHQARISHNING ALGORITMIK VA DASTURIY TA’MINOTINI TAKOMILLASHTIRISH
Keywords:
smart agriculture, IoT, evapotranspiration, machine learning, Random Forest, microcontrollers, water savingAbstract
This article explores the development of algorithmic and software support for smart irrigation systems aimed at efficient use of water resources in horticulture. A comparative analysis of traditional and modern irrigation methods is presented. The main focus is on calculating evapotranspiration using the Penman–Monteith method and predicting soil moisture through Machine Learning (ML) algorithms, particularly the Random Forest method. The article includes Python code fragments that illustrate the system’s operational logic.References
1. Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56. Rome.
2. Goap, A., Sharma, D., Shukla, A. K., & Krishna, C. R. (2018). An IoT based smart irrigation system using Machine Learning and Open Source technologies. Computers and Electronics in Agriculture, 155, 41-49.
3. Goldstein, A., Fink, L., Meiti, A., et al. (2017). Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge. Precision Agriculture, 19, 421–444.
4. Costa, J. M., et al. (2020). Modern viticulture in southern Europe: Vulnerabilities and strategies for adaptation to water scarcity. Agricultural Water Management, 240, 106306.
5. O‘zbekiston Respublikasi Qishloq xo‘jaligi vazirligi ma’lumotlari. (2023). Suv tejovchi texnologiyalarni joriy etish strategiyasi.
2. Goap, A., Sharma, D., Shukla, A. K., & Krishna, C. R. (2018). An IoT based smart irrigation system using Machine Learning and Open Source technologies. Computers and Electronics in Agriculture, 155, 41-49.
3. Goldstein, A., Fink, L., Meiti, A., et al. (2017). Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge. Precision Agriculture, 19, 421–444.
4. Costa, J. M., et al. (2020). Modern viticulture in southern Europe: Vulnerabilities and strategies for adaptation to water scarcity. Agricultural Water Management, 240, 106306.
5. O‘zbekiston Respublikasi Qishloq xo‘jaligi vazirligi ma’lumotlari. (2023). Suv tejovchi texnologiyalarni joriy etish strategiyasi.
![“AGRO ILM” jurnali Maxsus son 9 [120], 2025](https://test.agrokarantin.uz/public/journals/2/issue-87-cover.jpg)
Downloads
Published
2025-12-29
Issue
Section
Articles