How Artificial Intelligence Can Support Modern Horticulture?

Modern horticulture faces increasing challenges related to the rising demand for high-quality crops, limited environmental resources, and the need for improved production efficiency. Artificial intelligence (AI), encompassing machine learning, deep learning, and advanced analysis of sensor and image data, offers significant potential to support decision-making and automation in plant cultivation systems. The aim of this manuscript is to examine current and emerging applications of AI in horticulture, with particular emphasis on disease and stress detection, microclimate control in controlled-environment agriculture, robotic harvesting, and the enhancement of breeding programs. A review of scientific literature and industrial deployment cases demonstrates that AI can contribute to improved precision of cultivation practices, reduction of production losses, and more sustainable resource management. However, several challenges remain, including data standardization, model interpretability, and the economic feasibility of implementation. The findings highlight that further development of AI applications in horticulture requires close collaboration between researchers, practitioners, and technology providers, as well as progress in digital infrastructure and agronomic competence-building.