Integrating Artificial Intelligence into an Automated Irrigation System.

Area of Science:

• Agricultural Science
• Environmental Science
• Computer Science

Background:

• Eastern Europe faces significant climate change impacts on agriculture, threatening food security.
• Existing agricultural practices often lack the precision needed to adapt to changing climatic conditions and optimize resource use.

Purpose of the Study:

• To present an AI-powered system for automated irrigation and climate monitoring in Eastern Europe.
• To optimize resource management (water, energy) and enhance sustainable agricultural productivity.
• To provide a framework for adapting agriculture to extreme climate phenomena.

Main Methods:

• Continuous data collection of temperature, humidity, and soil moisture via wireless transmission (WebSocket).
• AI (OpenAI models) for processing Internet of Things (IoT) data and generating climate reports.
• Development of a system for advanced analytics, predictive recommendations, and national-level climate trend assessment.

Main Results:

• The system enables continuous monitoring and data collection for optimized irrigation.
• AI-driven analytics provide predictive recommendations to improve plant health and resource efficiency.
• National-level climate assessments and trend identification aid in adapting to extreme weather events.

Conclusions:

• Integrating AI in agricultural monitoring and irrigation is crucial for developing sustainable agriculture.
• The proposed system offers a replicable framework for vulnerable regions to adapt to aggressive climate change.
• Automated systems enhance food security through optimized resource management and increased agricultural productivity.