Application of artificial intelligence to improve the economic efficiency of land use management in the agricultural sector
The purpose of the study was to assess the cost-effectiveness of using artificial intelligence (AI) to predict environmental changes in land use and optimise agricultural production. It was determined that the introduction of machine learning and big data analysis algorithms can significantly improve the accuracy of forecasts of agricultural land conditions, optimise the use of resources, including fertilisers and water, and reduce costs. The study analysed how AI can contribute to a more rational choice of crops and planning of sowing and harvesting, which has a positive impact on yields. In addition, the study addressed the environmental aspect: the use of AI can reduce the negative impact on the environment through precise resource management and reduced soil and water pollution. The work included modelling yields for different climate scenarios, which allows forecasting possible outcomes and developing adaptive strategies for the sustainable development of Ukraine’s agricultural sector. The study demonstrated that the introduction of AI in the Ukrainian agricultural sector contributes to the improvement of land use efficiency, allowing farmers to respond more quickly to changing climate conditions. Machine learning algorithms, including those based on the analysis of data collected from satellite images and sensors, can help determine the timely need for fertiliser and water, which ensures the rational use of resources and reduces production costs. The forecasting models used in the article reflect possible yield scenarios for the period 2025-2028 for different climatic conditions, which allows enterprises to better plan agrotechnical measures and minimise risks. Thus, the study results emphasised the importance of AI as a tool for long-term strategic planning in the agricultural sector
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