MULTI-AGENT ALGORITHM FOR COLLECTING DATA FROM WEATHER STATION FOR FORECASTING PRODUCTIVITY AND CROPS CONDITION

Abstract

The weather affects the productivity and condition of crops, the requirements for the quantity and quality of fertilizers, as well as preventive measures to prevent diseases. Bad weather can affect the quality of products during transportation and storage, and hence the germination of seeds and planting material. Various intelligent monitoring systems are now widely used in agriculture, which include satellite monitoring and weather stations. In this case, the choice of a method for analyzing the received data and intelligent systems for their processing for predictive forecasting plays a fundamental role. The purpose of this study is to develop an intellectual system for predicting the state of the crop based on data from a weather station. A multi-agent algorithm for predicting the state of crops according to data from a weather station based on the selforganization of neurocognitive architecture was developed in this study. The description of the block diagram of the weather station and its sensors is given. A program algorithm has been developed for collecting and processing data from weather station sensors. As a result of processing, data on air and soil temperature, air and soil humidity, wind speed and direction, precipitation amount and the sum of active temperatures are sent to the intelligent decision-making system. A system for constructing cause-and-effect relationships is described. This system can make recommendations or forecasts on the condition of the crop and on the likelihood of diseases and pests in controlled crops.