Latorre Biel, Juan Ignacio
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Latorre Biel
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Juan Ignacio
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ISC. Institute of Smart Cities
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Publication Open Access AI guidelines for sustainable rural development and climate resilience in resource-constrained regions(Wiley, 2025-08-14) Arévalo Royo, Javier; Flor Montalvo, Francisco Javier; Latorre Biel, Juan Ignacio; Jiménez Macías, Emilio; Martínez Cámara, Eduardo; Blanco Fernández, Julio; Institute of Smart Cities - ISCArtificial intelligence (AI) has begun to permeate the strategies adopted for rural development and climate adaptation, especially in those regions where economic and infrastructural constraints are most acute. Rather than offering mere technological panaceas, the current landscape reveals a mosaic of partial successes and persistent obstacles, frequently shaped by the unique realities of small producers and community organizations. This study offers a critical examination of the concrete difficulties faced when deploying AI-driven solutions in domains such as agriculture, water governance, and renewable energy management. Particular attention is paid to the sometimes underappreciated influence of regulatory environments and the subtle interplay of local incentives. Throughout the analysis, it becomes apparent that the true value of AI lies not only in algorithmic sophistication but in the ability to design energy- and computation-conscious systems that remain sensitive to the diversity of local contexts. The findings suggest that progress in resource-limited settings depends equally on participatory approaches, digital literacy, and the adaptation of methodologies—ranging from climate prediction to financial risk assessment—to specific community needs. As a result, the work proposes a set of actionable principles, in harmony with the Sustainable Development Goals, that may serve as a practical guide for those seeking to translate technological promise into tangible benefits at the local scale.Publication Open Access AI algorithms in the agrifood industry: application potential in the Spanish agrifood context(MDPI, 2025-02-17) Arévalo Royo, Javier; Flor Montalvo, Francisco Javier; Latorre Biel, Juan Ignacio; Tino Ramos, Rubén; Martínez Cámara, Eduardo; Blanco Fernández, Julio; Ingeniería; Ingeniaritza; Institute of Smart Cities - ISCThis 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.