The use of artificial intelligence in agriculture

Nowadays, it is difficult to imagine the modern world without the involvement of technologies such as artificial intelligence (AI) – both in economic and social spheres. The concept was first formulated in 1956 at a conference in Dartmouth by J. McCarthy, an American computer scientist who is sometimes referred to in academic circles as the “father of artificial intelligence”1. At that time, in Dartmouth, New Hampshire, he put forward the thesis that every precisely described aspect of learning or any other form of intelligence could be simulated by machines2. The origins of the development of artificial intelligence – defined by the PWN encyclopaedia as a branch of science concerned with studying the mechanisms of human intelligence and with modelling and constructing systems capable of supporting or replacing intelligent human actions3, therefore date back to the 1950s. This field of computer science, alongside the ongoing global technological advancement observed over recent decades, has aroused growing interest among scholars. The intensive development of artificial intelligence became particularly noticeable in the 21st century, when the use of AI software for professional purposes became widespread. This process significantly contributed to the optimisation of work tools, as well as the mechanisation and automation of many processes – not only in production. The development of machine learning and advanced technologies based on artificial intelligence has not only changed the nature of various industries but also influenced the transformation of the agricultural sector. Among the greatest advantages of this process are undoubtedly: effective resource management in agriculture, precise real-time monitoring of crops and livestock, and more efficient reduction of negative environmental impact. Through the computing power of artificial intelligence algorithms adapted for agricultural purposes, it is possible, among other things, to predict yields with high accuracy, optimise the use of water and fertilisers, and detect plant diseases and pests at an early stage. Despite the clear benefits that artificial intelligence can bring to agriculture, its large-scale implementation in this sector will also be associated with certain challenges. It appears that the most significant of these are the costs of implementation and maintenance, as well as the potential risk of software failures, which could lead to serious production delays and slowdowns. Nevertheless, the development of AI in agriculture is inevitable. The aim of the study is to present the prospects for the use of artificial intelligence in agricultural activity. Hypothesis: Artificial intelligence is being used more and more comprehensively and increasingly in agricultural, crop and livestock production. Keywords: KRUS, agriculture, crop production, animal production, artificial intelligence (AI).