Open AccessUsing artificial intelligence to analyse businesses in agriculture industry
Author(s) -
Jakub Horák
Publication year - 2019
Publication title -
shs web of conferences
Language(s) - English
Resource type - Journals
eISSN - 2416-5182
pISSN - 2261-2424
DOI - 10.1051/shsconf/20196101005
Subject(s) - self organizing map , artificial neural network , artificial intelligence , computer science , agriculture , state (computer science) , marketing and artificial intelligence , machine learning , intelligent decision support system , geography , archaeology , algorithm
Artificial intelligence is largely used in many technical applications and allows you to provide various solutions in problem estimation, regression, or optimization. Artificial intelligence, specifically artificial neural networks, extend to the area of economics and finance. They are used primarily for operations that can´t be identified analytically. Neural networks are suitable for modelling very complex strategic decisions, for large sets of data, and so on. The main advantage is the ability to learn and then to capture hidden and strongly non-linear dependencies. In this paper they are used for the analysis of agricultural businesses. The aim is to analyse the state of the agricultural sector through the use of Kohonen networks and then to assess its future development. On the basis of the analysis, significant and large clusters of businesses are depicted, and the most significant clusters are analysed. It is possible to estimate the number of businesses that will be successful, those that will stagnate and those that will fail in the following period. Application of Kohonen networks is rather complex, but they have great potential and the results are very interesting.