ARCHITECTURAL MODELS AND OPERATIONAL ALGORITHMS OF AN INTELLIGENT MONITORING SYSTEM FOR WATER AND SOIL CONDITIONS IN AGRICULTURAL LANDS

Abstract

The article presents the architectural models and operational algorithms of an intelligent monitoring system for assessing the condition of water and soil in agricultural lands. The goal of the research is to develop an adaptive system that provides accurate and timely monitoring of soil parameters, such as moisture, salinity, and temperature, by employing big data analytics and machine learning methods. The primary focus is on leveraging a sensor infrastructure for data collection, preprocessing, and integration with server-side analysis modules. The study also explores approaches to forecasting potential changes in soil conditions using neural networks, which enhances the accuracy of agricultural decision-making and minimizes risks associated with unfavorable factors. The proposed system architecture is characterized by high scalability, flexibility, and the capability to integrate with existing information and management systems in agriculture. The results aim to improve resource efficiency and the resilience of agroecosystems in a changing climate.