Artificial Intelligence in Precision Agriculture: trends and future directions
DOI:
https://doi.org/10.35588/mwfneb13Keywords:
Precision agriculture, artificial intelligence, agricultural sustainability, technology adoptionAbstract
The demand for global agricultural production has led to the search for different tools that contribute to the optimal management of resources. This research presents a bibliometric mapping on the use of artificial intelligence in precision agriculture through the analysis of 479 articles published between 2019 and 2024 in Scopus and Web of Science databases, allowing to identify a higher scientific production in countries with strong technological investment. Artificial intelligence has revolutionized precision agriculture through the use of sensors, machine learning, robotics and predictive analytics, allowing to improve productivity, reduce costs and mitigate environmental impacts; which has allowed the development of research lines that include crop monitoring, early disease detection, smart irrigation and sustainable agriculture. However, adoption faces barriers such as the digital divide, high initial costs and limited technical training in rural areas; therefore, if a more efficient, resilient and inclusive agriculture is envisioned, a strategic, gradual and context-adapted implementation must be carried out.
Downloads
References
Adereti, D., McMaine, J., y Malik, A. (2024). Understanding barriers to smart farming in small-scale systems. Journal of Rural Studies, 101, 342–354.
Aria, M., y Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Banco Mundial. (2023). Digital Agriculture: Farmers in the Driver’s Seat.
Bock, C. H., Barbedo, J. G. A., Del Ponte, E. M., y Mahlein, A.-K. (2020). From visual estimates to fully automated sensor-based assessments of plant diseases. Phytopathology Research, 2, 1–9. https://doi.org/10.1186/s42483-020-00049-8
Bostanova, P. I., Palisat, I., y Koicheva, V. (2024). The economic impact of precision farming on sustainable agricultural development. Ekonomika i Upravlenie: Problemy, Resheniya.
Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. https://doi.org/10.1002/asi.20317
Chen, Z., Liu, X., y Zhang, Y. (2024). Blockchain in agriculture: Applications and challenges. Computers and Electronics in Agriculture, 210, 107745.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., y Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
FAO y Banco Mundial. (2021). Joint report on digital agriculture and climate resilience.
FAO. (2023). Innovation in agriculture: A key driver for food security and sustainability.
Ferreira, P., Loures, A., Castanho, R., Chamizo, A., Loures, L., y Panagopoulos, T. (2020). Assessing the effectiveness of precision agriculture management systems in Mediterranean small farms. Sustainability.
Fountas, S., Mylonas, N., y Gemtos, T. A. (2020). Smart farming and sustainability. Agronomy, 10(4), 637.
Goel, R., Yadav, C., Vishnoi, S., y Rastogi, R. (2021). Smart agriculture: Urgent need of the day in developing countries. Sustainable Computing: Informatics and Systems, 30, 100512. https://doi.org/10.1016/j.suscom.2021.100512
Gyarmati, G., y Mizik, T. (2020). The present and future of the precision agriculture. IEEE 15th International Conference of System of Systems Engineering (SoSE).
Hernández S., R., & Mendoza, C. (2020). Metodología de la investigación: las rutas cuantitativa, cualitativa y mixta.
Huo, D., Malik, A., Ravana, S., Rahman, A., y Ahmedy, I. (2024). Mapping smart farming: Addressing agricultural sustainability through big data and AI. Renewable y Sustainable Energy Reviews, 186, 113858. https://doi.org/10.1016/j.rser.2023.113858
Keswani, B., Mohapatra, A., Mohanty, A., Khanna, A., y Rodrigues, J. J. P. C. (2019). Adapting weather conditions-based IoT-enabled smart irrigation in agriculture. Neural Computing and Applications, 31, 1401–1417. https://doi.org/10.1007/s00521-018-3737-1
Kim, M.-Y., y Lee, K. H. (2022). Electrochemical sensors for sustainable precision agriculture—A review. Frontiers in Chemistry.
Lowenberg-DeBoer, J., y Erickson, B. (2019). Precision agriculture technology adoption in the US. Journal of Agricultural and Applied Economics, 51(1), 1–16.
Majumder, K., Phadikar, S., y Bhakta, I. (2019). State-of-the-art technologies in precision agriculture: A systematic review. Journal of the Science of Food and Agriculture, 99(11), 4878–4888. https://doi.org/10.1002/jsfa.9740
Malik, A., y Wang, T. (2024). Smart farming adoption: Barriers and enablers in emerging economies. Journal of Cleaner Production, 402, 136978.
Neményi, M., y Nyéki, A. (2022). Crop yield prediction in precision agriculture. Agronomy, 12(10), 1–15.
Padhiary, M., y Hoque, A. (2024). Automation and AI in precision agriculture: Innovations for enhanced crop management and sustainability. Asian Journal of Research in Computer Science, 20(10), 1–20.
Patel, K. K., Kumar, R., y Singh, D. (2024). A review on precision agriculture: An evolution and prospect for the future. International Journal of Plant y Soil Science.
Prakash, A., Bhambota, S., Kumar, S., y Sharma, A. (2024). Investigations of precision agriculture technologies with application to developing countries. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-024-04000-2
Rodrigues, J. J. P. C., y Mohapatra, A. (2022). Wireless sensor networks for agriculture: A comprehensive review. Sensors, 22(4), 1432.
Rozenstein, O., Cohen, Y., Alchanatis, V., y Behrendt, K. (2024). Data-driven agriculture and sustainable farming using machine learning and remote sensing. Precision Agriculture, 25, 233–249. https://doi.org/10.1007/s11119-023-10061-5
Rutter, S. M., y Rozenstein, O. (2024). Livestock monitoring with AI and sensors: State of the art. Animals, 14(2), 230.
Sadowski, A., Wiśniewski, J., y Marciniak, M. (2024). The role of precision agriculture technologies in enhancing sustainable agriculture. Sustainability.
Stock, R., Ogunyiola, A., y Gardezi, M. (2024). Precision agriculture and the future of agrarian labor in the US food system. Agriculture and Human Values, 41(3), 1–15.
Stumpe, C., Leukel, J., y Zimpel, T. (2024). Prediction of pasture yield using machine learning and remote sensing technologies. Precision Agriculture, 25, 150–169. https://doi.org/10.1007/s11119-023-10079-9
Ubina, N. A., y Cheng, S. (2022). A review of unmanned system technologies with applications in precision agriculture. Drones, 6(1), 12. https://doi.org/10.3390/drones6010012
Van Eck, N. J., y Waltman, L. (2014). Visualizing bibliometric networks. En Y. Ding, R. Rousseau, y D. Wolfram (Eds.), Measuring scholarly impact (pp. 285–320). Springer. https://doi.org/10.1007/978-3-319-10377-8_13
Vasconez, J. P., Delpiano, J., Vougioukas, S., y Auat Cheein, F. A. (2020). Comparison of convolutional neural networks in agricultural classification tasks. Computers and Electronics in Agriculture, 175, 105348. https://doi.org/10.1016/j.compag.2020.105348
Verrelst, J., Rivera-Caicedo, J. P., y Moreno, J. (2021). Remote sensing of vegetation traits using machine learning. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 56–68.
Yadav, C., Goel, R., y Rastogi, R. (2022). Edge computing in agriculture: Opportunities and challenges. Future Generation Computer Systems, 136, 252–264.
Yang, Y., y Wang, H. (2023). Sustainable agriculture through artificial intelligence: A bibliometric analysis. Sustainability, 15(3), 1056.
Yost, M., Kitchen, N. R., Sudduth, K. A., y Sadler, E. J. (2017). Long-term impact of a precision agriculture system on grain crop production. Precision Agriculture, 18(6), 840–856.
Zhang, Y., Liu, X., y Chen, Z. (2023). Big data applications in agriculture: A review. Computers and Electronics in Agriculture
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Dr. Oscar Fabian Patiño Perdomo, Dr. Víctor Julio Balanta Martínez, Dr. Wilmer Arley Patiño Perdomo, Paula Andrea Sánchez Orozco, Dr. Jesús Emilio Pinto Lopera
This work is licensed under a Creative Commons Attribution 4.0 International License.
