Computer Model of an IoT Decision-Making Network for Detecting the Probability of Crop Diseases

Abstract

This article is devoted to the development and testing of a computer model of an IoT system that combines wireless network technologies for the online monitoring of climatic and soil conditions in agriculture. The system supports decision-making by predicting the probability of crop diseases. This study focuses on the processes of aggregation, wireless transmission, and processing of soil and climatic measurement data within infocommunication software and hardware solutions. This research makes both scientific and practical contributions. Specifically, it presents a computer model based on wireless sensor networks and edge-computing technologies. This model aggregates and intelligently processes agricultural monitoring data to predict crop diseases. The software component, developed using an adaptive neuro-fuzzy inference system (ANFIS), was integrated into the microcontroller unit of IoT systems for agricultural applications. This approach enabled the substantiation of an optimised algorithmic and structural organisation of the IoT system, enabling its use in designing reliable architectures for agricultural monitoring systems in open fields with decision-making support.