Artificial Intelligence in the Agro-Industrial Complex: Issues of Legal Regulation and Prospects for Use

Agriculture is the foundation of the sustainability of any economy. It plays a key role in long-term economic growth and structural transformations. In the process of achieving the goal of maximizing yield, the agro-industrial complex faces numerous challenges, including inappropriate soil treatment, disease and pest infestations, low productivity, and gaps in knowledge between farmers and technologies. The main concept of applying AI in agriculture is based on its flexibility, high productivity, accuracy, and economic efficiency. Currently, there are several legal regulatory issues regarding AI systems in agriculture that require clarification from lawmakers, as existing legal norms, both at the international and national levels, are not sufficiently detailed and do not cover all aspects of AI implementation in agriculture. Today, the AI market in agriculture is most developed in three promising areas: machine learning, computer vision, and predictive analytics. Various countries are developing strategies for agricultural practices using artificial intelligence, which should mark the beginning of the development of legal regulation in this field at the international level. Both state and international levels should actively support innovations in AI by creating favorable conditions for the development and implementation of new technologies, as well as ensuring their safety and transparency. Artificial intelligence has high potential to transform the agro-industrial complex, allowing for optimized production, increased yields, reduced resource costs, and minimized environmental damage. However, to realize this potential, several technical, legal, and ethical issues that exist in the regulation of information technologies and artificial intelligence in particular must be addressed.

agro-industrial complex, artificial intelligence, ELR, agriculture, international standards, national legislation, machine learning, agro-industrial crops, weeds, agricultural data