Systematic review and bibliometric analysis of trends in programming languages and environments for Agriculture 4.0

Agriculture 4.0 represents a process of digital transformation driven by the integration of technologies such as the Internet of Things, artificial intelligence, remote sensing, and cloud computing. In this context, programming languages and development environments constitute the technical foundation enabling the design, implementation, and scalability of intelligent agricultural solutions. This study aims to systematically analyze scientific trends related to programming languages and environments applied to Agriculture 4.0 through a bibliometric and meta-analytical approach. A corpus of documents indexed in Scopus for the period 2014 - 2025 was constructed and processed in RStudio using the bibliometrix package and the Biblioshiny interface. The results show sustained growth in scientific production, with a conceptual structure in which remote sensing, crop analysis, and unmanned aerial vehicles emerge as motor themes within the field. The predominance of general-purpose programming languages, particularly Python, is observed in artificial intelligence and machine learning applications, while low-level languages remain relevant for embedded systems and agricultural robotics. The study also identifies gaps related to standardization, interoperability, and the development of domain-specific languages tailored to agroproductive contexts. It is concluded that the consolidation of Agriculture 4.0 depends on the integration of flexible, efficient, and context-oriented programming environments capable of addressing real agricultural conditions.