Analysis of directions for application of artificial intelligence systems in soil condition analysis

Abstract

The most significant characteristics of the soil are water-physical, biological and other properties, by influencing which it is possible to increase the productivity of agricultural crops. Analyzing various soil characteristics is a very labor-intensive and expensive process. The authors reviewed research on the use of artificial intelligence systems in the field of soil analysis. The purpose of the study is to analyze research on the use of artificial intelligence systems in the field of soil analysis. The article presents an analysis of artificial intelligence systems based on artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), multiple regression (MR) methods, particle swarm algorithm (PSO) and also their ensembles (various combinations). The use of these systems (their models) makes it possible to predict various types of soil erosion, predict soil temperature and moisture depending on various conditions, and also predict such complex indicators as the cation exchange capacity of the soil. The scope of application of artificial intelligence systems is to increase the awareness of managers and specialists of agricultural organizations and industry management bodies about various soil characteristics. This, in turn, allows you to make optimal decisions on various agricultural activities. Artificial intelligence systems can process significant amounts of data with high accuracy and speed compared to humans, which makes it possible to more effectively analyze many factors affecting the condition of the soil. The use of artificial intelligence systems can significantly reduce the cost of analyzing various soil characteristics and is a necessary condition for the use of precision farming systems.