The problem of overcoming hunger worldwide is extremely important. Solving it requires coordination and communication between different countries. The paper deals with the problem of using information technologies for translation of materials in the agricultural sector to ensure interaction of Ukrainian producers with partners from other countries in the international food market. The purpose of this article is to consider the possibility of using cloud-based machine translation systems based on artificial intelligence (AI) for this task. We used methods of comparison, analysis, synthesis and description. We compared the functional indicators of three leading systems: Google Translate, DeepL, and SYSTRAN. We have conducted an experimental study to compare the adequacy of translation of texts from different fields of the agricultural sector by these systems. The evaluation criteria were various aspects of equivalence, namely: semantic, informational, denotational, terminological, syntactic, pragmatic, and dynamic. Each of them was measured by three levels of equivalence: low, medium, high. The branches chosen for the experiment were engineering, veterinary medicine, animal husbandry, fish farming, forestry, horticulture, crop production, biotechnology, and plant protection. Our findings demonstrate that the levels of translation adequacy for different branches of the agricultural sector in each of these systems vary, but are generally satisfactory after post-editing.