This research explores the prospective implementations of artificial intelligence (AI) algorithms within the agrifood sector, focusing on the Spanish context. AI methodologies, encompassing machine learning, deep learning, and neural networks, are increasingly integrated into various agrifood sectors, including precision farming, crop yield forecasting, disease diagnosis, and resource management. Utilizing a comprehensive bibliometric analysis of scientific literature from 2020 to 2024, this research outlines the increasing incorporation of AI in Spain and identifies the prevailing trends and obstacles associated with it in the agrifood industry. The findings underscore the extensive application of AI in remote sensing, water management, and environmental sustainability. These areas are particularly pertinent to Spain’s diverse agricultural landscapes. Additionally, the study conducts a comparative analysis between Spain and global research outputs, highlighting its distinctive contributions and the unique challenges encountered within its agricultural sector. Despite the considerable opportunities presented by these technologies, the research identifies key limitations, including the need for enhanced digital infrastructure, improved data integration, and increased accessibility for smaller agricultural enterprises. The paper also outlines future research pathways aimed at facilitating the integration of AI in Spain’s agriculture. It addresses cost-effective solutions, data-sharing frameworks, and the ethical and societal implications inherent to AI deployment.

Stone pitching and other finishing works on steep infrastructure slopes (45–60°) are still commonly executed by workers standing on the inclined surface. This paper assesses a modular system of aluminium stringers resting on the slope and horizontal platforms connected by pins and hooks, derived from a Spanish utility model. The original shell finite element analysis (FEA) was re-examined with an independent shell FEA built from the original geometry and verified against benchmarks and closed-form solutions, complemented with a mesh-sensitivity study, a sub-model of the pin holes, frictional contact with the slope and code-based checks to EN 12811-1 and Eurocodes 3 and 9. Deflections under imposed load agreed within 4% between models and changed by less than 1% with mesh refinement, whereas peak stresses at the idealised connections increased by up to 3.5 times; connections were therefore verified with code resistance models. For two workers per platform, the selected configuration (60 × 40 × 3 mm platform tubes and 250 × 40 × 3 mm stringers in EN AW-6063 T6) reached a maximum utilisation of 0.87, and the end-of-span load case did not govern. A single 6 kN arrest force at an integrated anchor point exceeded the elastic limit of the platform by 1.4–2.1 times and reached 86–115% of its plastic collapse load, leaving no margin for dynamic amplification or for the 12 kN static strength of EN 795. Fall arrest therefore governs any integrated anchorage, which should be replaced by an independent anchor device; driven-bar ground anchors were also found to be insufficient in granular soils.