Systematic review and bibliometric analysis of trends in programming languages and environments for Agriculture 4.0
DOI:
https://doi.org/10.64041/7betdd49Keywords:
Bibliometric analysis, Systematic review, Digital agriculture, Internet of Things, Precision agricultureAbstract
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.
Downloads
References
Aguirre-Munizaga, M. (2025). Integración de sistemas de información con tecnologías abiertas para el monitoreo en estaciones meteorológicas universitarias (1.a ed.). Atena Editora. https://doi.org/10.22533/at.ed.355250809
Aguirre-Munizaga, M., Chang-Zorilla, S., Rivera, D. V., & Vera-Lucio, N. (2025). Implementation of a Web Application for Estimating Cocoa Productivity Using Machine Learning. Information Technology and Systems, Lecture Notes in Networks and Systems, 1447, 382-390. https://doi.org/10.1007/978-3-031-93109-3_34
Albiero, D., Paulo, R. L. D., Félix Junior, J. C., Santos, J. D. S. G., & Melo, R. P. (2020). Agriculture 4.0: A terminological introduction. REVISTA CIÊNCIA AGRONÔMICA, 51(5). https://doi.org/10.5935/1806-6690.20200083
Carrasco Rivera, E. (2025). Ciberseguridad en los sistemas de información agropecuarios como desafío estratégico para la protección de datos en la agricultura digital. ASCE MAGAZINE, 4(4), 1608-1633. https://doi.org/10.70577/asce.v4i4.504
Castillo Gómez, J. (2023). Ingeniería de Software para la Transformación Digital: Retos, Tendencias y Oportunidades Profesionales en el Ecuador. Revista internacional de Investigación y Desarrollo Global, 2(2), 66-80. https://doi.org/10.64041/riidg.v2i2.40
De Luca, G., Li, Z., Mian, S., & Chen, Y. (2018). Visual programming language environment for different IoT and robotics platforms in computer science education. CAAI Transactions on Intelligence Technology, 3(2), 119-130. https://doi.org/10.1049/trit.2018.0016
Du, L., Hu, C., Yang, C., Yang, L., Du, H., Li, Q., Yu, C., Xie, L., & Jiang, X. (2020). Investigation of a preliminary ventilation energy-recovery system for poultry houses. Computers and Electronics in Agriculture, 175, 105521. https://doi.org/10.1016/j.compag.2020.105521
Facuy, J., Aguirre-Munizaga, M., Pasini, A., Estévez, E., & Moran-Castro, C. (2026). Validation of an Ambient Intelligence System Applied to the Prediction of Electronic Waste in Smart Cities. En R. Valencia-Garcia, P. Alvarez-Muñoz, J. Tarquino Calderon, V. Vergara-Lozano, L. Ortega-Ponce, A. L. Pico-Aguilar, & B. M. Vásconez-García (Eds.), Technologies and Innovation (Vol. 2776, pp. 259-272). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-11494-5_17
Facuy Toledo, D. P. (2024). Aplicación de sensores IoT e inteligencia artificial para la optimización del riego en cultivos agroecológicos. Revista internacional de Investigación y Desarrollo Global, 3(2), 36-52. https://doi.org/10.64041/riidg.v3i2.23
Lagogiannis, G., & Chatzopoulos, A. (2025). A No-Code Educational Platform for Introducing Internet of Things and Its Application to Agricultural Education. IoT, 6(3), 42. https://doi.org/10.3390/iot6030042
Meza Nieto, A. K. (2023). Estudio de caso sobre los efectos económicos del uso de tecnologías digitales en la comercialización de productos agrícolas. Revista internacional de Investigación y Desarrollo Global, 2(3), 1-16. https://doi.org/10.64041/riidg.v2i3.12
Raj, M., Gupta, S., Chamola, V., Elhence, A., Garg, T., Atiquzzaman, M., & Niyato, D. (2021). A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0. Journal of Network and Computer Applications, 187, 103107. https://doi.org/10.1016/j.jnca.2021.103107
Ravina, Gill, P. S., Kumar, A., Narang, J., Prasad, M., & Mohan, H. (2022). Molecular detection of H1N1 virus by conventional reverse transcription PCR coupled with nested PCR. Sensors International, 3, 100178. https://doi.org/10.1016/j.sintl.2022.100178
Urjilez, H., Valdez, D., García, Y., Aguirre, M., & Mancero, D. (2025). Advances in Predictive Modeling in Fruit Crops: Mobile Applications. En A. Rocha, C. Ferrás, & H. Calvo (Eds.), Information Technology and Systems (Vol. 1447, pp. 354-363). Springer Nature Switzerland. https://doi.org/https://doi.org/10.1007/978-3-031-93109-3_31
Zohuri, B., Mossavar-Rahmani, F., & Behgounia, F. (2022). Python programming–driven artificial intelligence. En Knowledge is Power in Four Dimensions: Models to Forecast Future Paradigm (pp. 827-836). Elsevier. https://doi.org/10.1016/B978-0-323-95112-8.00026-X
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Eddie Carrasco-Rivera
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Los contenidos de esta revista se publican bajo la licencia Creative Commons Atribución–NoComercial–SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0).
Usted es libre de compartir el material en cualquier medio o formato, siempre que:
-
Reconozca la autoría original.
-
No utilice la obra con fines comerciales.
-
No realice obras derivadas, adaptaciones o modificaciones.
Enlace oficial: https://creativecommons.org/licenses/by-nc-nd/4.0/
