Tendencias en agricultura y silvicultura de precisión: un análisis bibliométrico de la integración de datos, el apoyo a la toma de decisiones y la sostenibilidad

Autores/as

DOI:

https://doi.org/10.69639/arandu.v13i3.2452

Palabras clave:

agricultura de precisión, silvicultura de precisión, análisis bibliométrico, mapa temático, sostenibilidad

Resumen

Este estudio analiza las tendencias y la estructura conceptual de la investigación sobre agricultura y silvicultura de precisión, con énfasis en la integración de datos, los sistemas de apoyo a la toma de decisiones y la sostenibilidad. Se desarrolló un análisis bibliométrico de documentos indexados en Web of Science y Scopus, recuperados mediante una estrategia de búsqueda orientada a identificar estudios relacionados con tecnologías digitales, inteligencia artificial, teledetección, IoT, modelización predictiva y gestión sostenible. El proceso de selección documental siguió criterios de depuración basados en PRISMA, clasificación por cuartiles y revisión de metadatos, obteniéndose un corpus final de 411 documentos publicados entre 2003 y 2026. Los resultados evidencian un crecimiento acelerado de la producción científica, con una alta concentración de literatura reciente y una estructura temática organizada en torno a temas motores, básicos, de nicho y emergentes. Los clústeres de precision agriculture y smart agriculture se identificaron como núcleos articuladores del campo, mientras que inteligencia artificial, IoT y seguridad alimentaria funcionaron como bases transversales. Asimismo, la agricultura digital, la gobernanza tecnológica, el aprendizaje por refuerzo y las redes neuronales convolucionales aparecen como líneas especializadas o en desarrollo. Se concluye que el mapa temático permite reconocer patrones de centralidad y densidad que diferencian tendencias consolidadas, especializadas y emergentes, aportando una visión ordenada para orientar futuras investigaciones sobre sistemas agroforestales inteligentes, adaptativos y sostenibles.

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Citas

Adamo, T., Caivano, D., Colizzi, L., Dimauro, G., & Guerriero, E. (2025). Optimization of irrigation and fertigation in smart agriculture: An IoT-based micro-services framework. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2025.100885

Afzaal, H., Rude, D., Farooque, A. A., Randhawa, G. S., Schumann, A. W., & Krouglicof, N. (2025). Improved crop row detection by employing attention-based vision transformers and convolutional neural networks with integrated depth modeling for precise spatial accuracy. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2025.100934

Ali, B. (2022). The Role of FAIR Data towards Sustainable Agricultural Performance: A Systematic Literature Review. AGRICULTURE-BASEL. https://doi.org/10.3390/agriculture12020309

Altherwy, Y. N., Roman, A., Naqvi, S. R., Alsuhaibani, A., & Akram, T. (2024). Remote Sensing Insights: Leveraging Advanced Machine Learning Models and Optimization for Enhanced Accuracy in Precision Agriculture. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3455169

Azlan, Z. H. Z., Junaini, S. N., & Bolhassan, N. A. (2024). Evidence of the potential benefits of digital technology integration in Asian agronomy and forestry: A systematic review. Agricultural Systems. https://doi.org/10.1016/j.agsy.2024.103947

Barbosa, J. M. R., Moreira, B. R. D. A., Carreira, V. D. S., Brito, F. A. L. D., Trentin, C., Souza, F. L. P. D., Tedesco, D., Setiyono, T., Flores, J. P., Ampatzidis, Y., Silva, R. P. D., & Shiratsuchi, L. S. (2024). Precision agriculture in the United States: A comprehensive meta-review inspiring further research innovation and adoption. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2024.108993

Bhat, S. A. (2021). Big Data and AI Revolution in Precision Agriculture: Survey and Challenges. IEEE ACCESS. https://doi.org/10.1109/ACCESS.2021.3102227

Bykzkan, G. (2024). Integrated design framework for smart agriculture: Bridging the gap between digitalization and sustainability. JOURNAL OF CLEANER PRODUCTION. https://doi.org/10.1016/j.jclepro.2024.141572

Colussi, J. (2024). A Comparative Study of the Influence of Communication on the Adoption of Digital Agriculture in the United States and Brazil. AGRICULTURE-BASEL. https://doi.org/10.3390/agriculture14071027

Damaeviius, R., & Maskelinas, R. (2025). Enhancing Smart Forestry Through AI-Driven Knowledge Management: A Synergistic Approach to Efficiency and Sustainability. Journal of Sustainable Forestry. https://doi.org/10.1080/10549811.2025.2513220

Dara, R., Hazrati, F. S. M., & Kaur, J. (2022). Recommendations for ethical and responsible use of artificial intelligence in digital agriculture. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2022.884192

Demissie, W. A. (2026). Integration of artificial intelligence and remote sensing for crop yield prediction and crop growth parameter estimation in Mediterranean agroecosystems: Methodologies emerging technologies research gaps and future directions. EUROPEAN JOURNAL OF AGRONOMY. https://doi.org/10.1016/j.eja.2025.127894

Eriyadi, M., Supangkat, S. H., & Hidayat, F. (2025). Sensing as a Service Reinvented: A Unified and Sustainable Sensing Framework for Smart X Development. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3558736

Fleming, A., Jakku, E., Fielke, S., Taylor, B. M., Lacey, J., Terhorst, A., & Stitzlein, C. (2021). Foresighting Australian digital agricultural futures: Applying responsible innovation thinking to anticipate research and development impact under different scenarios. Agricultural Systems. https://doi.org/10.1016/j.agsy.2021.103120

Gardezi, M., Adereti, D. T., Stock, R., & Ogunyiola, A. (2022). In pursuit of responsible innovation for precision agriculture technologies. Journal of Responsible Innovation. https://doi.org/10.1080/23299460.2022.2071668

Gebresenbet, G., Bosona, T., Patterson, D., Persson, H., Fischer, B., Mandaluniz, N., Chirici, G., Zacepins, A., Komasilovs, V., Pitulac, T., & Nasirahmadi, A. (2023). A concept for application of integrated digital technologies to enhance future smart agricultural systems. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2023.100255

Herrera-Granda, I. D. (2026). A Systematic Review of Recent Models for Agri-Food Supply Chain Management With Emphasis on the Application of Artificial Intelligence and Sustainability. IEEE ACCESS. https://doi.org/10.1109/ACCESS.2026.3657974

Hundal, G. S., Laux, C. M., Buckmaster, D., Sutton, M. J., & Langemeier, M. (2023). Exploring Barriers to the Adoption of Internet of Things-Based Precision Agriculture Practices. Agriculture (Switzerland). https://doi.org/10.3390/agriculture13010163

Kalyanaraman, A., Burnett, M., Fern, A., Khot, L., & Viers, J. (2022). Special report: The AgAID AI institute for transforming workforce and decision support in agriculture. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2022.106944

Latterini, F. (2025). Remote sensing for planning harvesting operations and monitoring their effects on the forest ecosystem: State of the art and future perspectives. FOREST ECOLOGY AND MANAGEMENT. https://doi.org/10.1016/j.foreco.2025.123175

Nugroho, H., Chew, J. X., Eswaran, S., & Tay, F. S. (2024). Resource-optimized cnns for real-time rice disease detection with ARM cortex-M microprocessors. Plant Methods. https://doi.org/10.1186/s13007-024-01280-6

Pereira, S. S. D., Richter, V., Junior, N. B., Ferreira, R. A., Ferreira, G. V., Carneiro, A. T. J., Pott, L. P., & Amaral, L. D. P. (2026). Remote detection of root malformation disorder in Eucalyptus saligna using UAV multispectral imagery and U-Net++. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2026.111522

Rackovi, T., Ratkovi, K., Simeunovi, M., Kova, N., Menz, C., Fraga, H., Malheiro, A. C., Fernandes, A., & Santos, J. A. (2026). From Concept to Practice: Implementing a Knowledge-Driven Decision Support Platform for Sustainable Viticulture in Montenegro. Sensors. https://doi.org/10.3390/s26092843

Richards, C. (2025). Digital technology and on-farm responses to climate shocks: exploring the relations between producer agency and the security of food production. AGRICULTURE AND HUMAN VALUES. https://doi.org/10.1007/s10460-024-10624-w

Simeunovi, M., Ratkovi, K., Kova, N., Rackovi, T., & Fernandes, A. (2025). A Knowledge-Driven Framework for a Decision Support Platform in Sustainable Viticulture: Integrating Climate Data and Supporting Stakeholder Collaboration. Sustainability (Switzerland). https://doi.org/10.3390/su17041387

Ullah, A. (2026). How misinformation and information asymmetry distort climate adaptation among smallholder farmers. Agricultural Systems. https://doi.org/10.1016/j.agsy.2026.104660

Varzaru, A. A. (2025). Digital Revolution in Agriculture: Using Predictive Models to Enhance Agricultural Performance Through Digital Technology. AGRICULTURE-BASEL. https://doi.org/10.3390/agriculture15030258

Veerachamy, R., & Ramar, R. (2022). Agricultural Irrigation Recommendation and Alert (AIRA) system using optimization and machine learning in Hadoop for sustainable agriculture. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-021-13248-3

Wang, Z., Jang, W., Ruan, B., Wang, J., & Xiao, S. (2026). Developing and integrating trust modeling into multi-objective reinforcement learning for intelligent agricultural management. Smart Agricultural Technology. https://doi.org/10.1016/j.atech.2026.102145

Wang, Z., Xiao, S., Wang, J., Parab, A., & Patel, S. (2025). Reinforcement Learning-Based Agricultural Fertilization and Irrigation Considering N2O Emissions and Uncertain Climate Variability. AgriEngineering. https://doi.org/10.3390/agriengineering7080252

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Publicado

2026-08-29

Cómo citar

Carvajal Chavez , C. A. (2026). Tendencias en agricultura y silvicultura de precisión: un análisis bibliométrico de la integración de datos, el apoyo a la toma de decisiones y la sostenibilidad. Arandu UTIC, 13(3), 1068–1085. https://doi.org/10.69639/arandu.v13i3.2452

Número

Sección

Ciencias y Tecnologías