A computerized method for predicting the risk of powdery mildew in wheat based on software analysis of soil and climatic monitoring data

Today, smart agriculture is one of the core technologies for sustainable development and increasing the efficiency of open-field crop production enterprises of various sizes and forms of ownership in the face of changing climate conditions. The development and implementation of computerized methods and intelligent software and hardware solutions for transforming large volumes of agroclimatic data distributed in time and space is a relevant and important field for improving the efficiency of information technologies for agrotechnical applications. In this article, the scientific and applied problem of creating and validating a computerized method for predicting the probability of occurrence of crop diseases at the pre-symptomatic stage, which forms the basis of software and hardware components for processing data from agromonitoring systems based on fog architecture, has been solved. The main results of the research are: reduction of the number of informative features to five based on the Harris Hawk Optimizer algorithm, proving the effectiveness of Bagged Trees and Medium Neural Network algorithms in the classification of Powdery Mildew in Wheat, synthesis and testing of a computer model in Simulink that implements a full cycle of transformation of agroclimatic monitoring data in predicting the Risk of Powdery Mildew in Wheat. In addition, prospective directions for further research to improve the efficiency of information technologies for predicting the probability of crop diseases are substantiated in the article.