PRECISION AGRICULTURE BASED ON MACHINE LEARNING AND REMOTE SENSING TECHNIQUES

被引：0
|
作者：
Alshaya, Shaya A. [1 ]
机构：
[1] Majmaah Univ, Coll Sci, Comp Sci Dept, Al Zulfi 11932, Saudi Arabia
来源：
COMPTES RENDUS DE L ACADEMIE BULGARE DES SCIENCES | 2025年 / 78卷 / 01期
关键词：
remote sensing; precision agriculture; artificial intelligence; soil moisture; vegetation indices;
D O I：
10.7546/CRABS.2025.01.12
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
In today's agricultural landscape, precision is crucial, utilizing advanced technologies like IoT, AI, aircraft, and satellite systems. Smart agriculture aims to revolutionize production by monitoring soil quality and employing data anUAVs, remote sensing provides crucial data and imagery for olive cultivation. Cloud platforms with satellite data offer invaluable information, aiding public and private decision-making. Proposing an AI-powered application combined with remote sensing, this paper develops predictive models for soil moisture and with decision tree and extra trees regressor models, highlighting their effectiveness. This research transforms agricultural productivity through advanced technology and data-driven methodologies.
引用
收藏
页码：101 / 108
页数：8
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