Aplicaciones de la Inteligencia Artificial en la Educación Agrícola Superior.

Palabras clave: agricultura, docencia, innovación educativa, aprendizaje automático

Resumen

Este artículo investiga las aplicaciones de la Inteligencia Artificial (IA) en la educación superior en general y en la educación agrícola en particular mediante revisiones de literatura. Lo anterior se hace con el propósito de sentar las bases para la mejora del proceso educativo con la inclusión curricular de la IA para preparar a los estudiantes ante los desafíos del sector agrícola moderno, concluyendo con la importancia del uso de herramientas para una educación agrícola de excelencia.

Citas

Addas, A., Tahir, M., & Ismat, N. (2023). Enhancing Precision of Crop Farming towards Smart Cities: An Application of Artificial Intelligence. Sustainability, 16(1), 355. https://doi.org/10.3390/su16010355

Araújo, S. O., Peres, R. S., Ramalho, J. C., Lidon, F., & Barata, J. (2023). Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives. Agronomy, 13(12), 2976. https://doi.org/10.3390/agronomy13122976

Baceta, M. Á., Thelwall, M., & Kousha, K. (2019). Web of Science and Scopus language coverage. Scientometrics, 121(3), 1803-1813. https://doi.org/10.1007/s11192-019-03264-z

Bampasidou, M., Goldgaber, D., Gentimis, T., & Mandalika, A. (2024). Overcoming ‘Digital Divides’: Leveraging higher education to develop next generation digital agriculture professionals. Computers and Electronics in Agriculture, 224, 1-8. https://doi.org/10.1016/j.compag.2024.109181

Bellman, R. (1978). An introduction to artificial intelligence: can computers think? Boyd & Fraser Pub. Co. https://books.google.com.mx/books/about/An_Introduction_to_Artificial_Intelligen.html?id=84xQAAAAMAAJ&redir_esc=y

Bolaño-García, M., & Duarte-Acosta, N. (2024). Una revisión sistemática del uso de la inteligencia artificial en la educación. Revista Colombiana de Cirugía, 39(1), 51-63. https://doi.org/10.30944/20117582.2365

Calderón, J. A., Apolo, N. P., Escandón, J. D., & Luján, M. G. (2024). Integración de la Inteligencia Artificial en la educación superior: un análisis bibliométrico de la literatura reciente. Runas, 5(10), 176. https://doi.org/10.46652/runas.v5i10.176

Chen, C.-H., Kung, H.-Y., & Hwang, F.-J. (2019). Deep Learning Techniques for Agronomy Applications. Agronomy, 9(3), 142. https://doi.org/10.3390/agronomy9030142

Cravero, A., & Sepúlveda, S. (2021). Use and Adaptations of Machine Learning in Big Data— Applications in Real Cases in Agriculture. Electronics, 10(5), 552. https://doi.org/10.3390/electronics10050552

Escamilla-García, A., Soto-Zarazúa, G. M., Toledano-Ayala, M., Rivas-Araiza, E., & Gastélum-Barrios, A. (2020). Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development. Applied Sciences, 10(11), 3835. https://doi.org/10.3390/app10113835

Farjon, G., Huijun, L., & Edan, Y. (2023). Deep-learning-based counting methods, datasets, and applications in agriculture: a review. Precision Agriculture, 24(5), 1683–1711. https://doi.org/10.1007/s11119-023-10034-8

Gallent-Torres, C., González, A. Z., & Ortego-Hernando, J. L. (2023). El impacto de la inteligencia artificial generativa en educación superior: una mirada desde la ética y la integridad académica. Relieve. Revista Electrónica de Investigación y Evaluación Educativa, 29(3). https://doi.org/10.30827/relieve.v29i2.29134

García Peñalvo, F. J., Llorens-Largo, F., & Vidal, J. (2024). The new reality of education in the face of advances in generative artificial intelligence. RIED-Revista Iberoamericana de Educación a Distancia, 27(1), 9-39 https://doi.org/10.5944/ried.27.1.37716

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/

Haykin, S. (1998). Neural Networks: A Comprehensive Foundation. Prentice Hall. https://books.google.com.mx/books/about/Neural_Networks.html?hl=es&id=bX4pAQAAMAAJ&redir_esc=y

Herring, M. C.; Koehler, Matthew J.; Mishra, Punya; Rosenberg, Joshua M.; Teske, J. (2016) Introduction to the Second Edition of the TPACK Handbook. En: Punya Mishra, Matthew J. Koehler (2016) Handbook of technological pedagogical content knowledge (TPACK) for educators (2nd ed., pp. 1-8). Taylor & Francis. https://doi.org/10.4324/9781315771328

Kaur, G., Adhikari, N., Krishnapriya, S., Wawale, S. G., Malik, R. Q., Zamani, A. S., Perez-Falcon, J., & Osei-Owusu, J. (2023). Recent Advancements in Deep Learning Frameworks for Precision Fish Farming Opportunities, Challenges, and Applications. Journal of Food Quality, 2023, 1–11. https://doi.org/10.1155/2023/4399512

Kneissl, S. M., Tichy, A., & Mitlacher, S. F. (2023). Flipped Classroom to Facilitate Deeper Learning in Veterinary Undergraduate Students: An Educational Change Pilot Study Limited to the Imaging Module Bones. Animals, 13(1540), 2-12. https://doi.org/10.3390/ani13091540

Li, G., Huang, Y., Chen, Z., Chesser, G. D., Purswell, J. L., Linhoss, J., & Zhao, Y. (2021). Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review. Sensors, 21(4), 1492. https://doi.org/10.3390/s21041492

Loukatos, D., Kondoyanni, M., Kyrtopoulos, I.-V., & Arvanitis, K. (2022). Enhanced Robots as Tools for Assisting Agricultural Engineering Students’ Development. Electronics, 11(755), 1-27. https://doi.org/10.3390/electronics11050755

Macías, M. R., Sánchez, M. L., & Marrero, D. G. (2016). La cultura agropecuaria y el uso de las TIC en la formación pedagógica. Atenas, 4(36). https://www.redalyc.org/journal/4780/478055146019/478055146019.pdf

Mahmud, M. S., Zahid, A., Das, A. K., Muzammil, M., & Khan, M. U. (2021). A systematic literature review on deep learning applications for precision cattle farming. Computers and Electronics in Agriculture, 187, 106313. https://doi.org/10.1016/j.compag.2021.106313

Malanski, P. D., Dedieu, B., & Schiavi, S. (2021). Mapping the research domains on work in agriculture: A bibliometric review from Scopus database. Journal of Rural Studies, 81, 305–314. https://doi.org/10.1016/j.jrurstud.2020.10.050

Mariñelarena-Dondena, Luciana Errecalde, M. L., & Castro Solano, A. (2017). Extracción de conocimiento con técnicas de minería de textos aplicadas a la psicología. Revista Argentina de Ciencias Del Comportamiento, 9(2), 65–76. https://www.redalyc.org/pdf/3334/333452119006.pdf

Megeto, G. A. S., Silva, A. G. da, Bulgarelli, R. F., Bublitz, C. F., Valente, A. C., & Costa, D. A. G. da. (2020). Artificial intelligence applications in the agriculture 4.0. Revista Ciência Agronômica, 51(5). https://doi.org/10.5935/1806-6690.20200084

Mitchell, T. M. (1997). Machine Learning. McGraw-Hill Education. https://www.cs.cmu.edu/~tom/files/MachineLearningTomMitchell.pdf

Mohan, P., & Patil, K. (2018). Deep Learning Based Weighted SOM to Forecast Weather and Crop Prediction for Agriculture Application. International Journal of Intelligent Engineering and Systems, 11(4), 167–176. https://doi.org/10.22266/ijies2018.0831.17

Murugamani, C., Shitharth, S., Hemalatha, S., Kshirsagar, P. R., Riyazuddin, K., Naveed, Q. N., Islam, S., Mazher Ali, S. P., & Batu, A. (2022). Machine Learning Technique for Precision Agriculture Applications in 5G-Based Internet of Things. Wireless Communications and Mobile Computing, 2022, 1–11. https://doi.org/10.1155/2022/6534238

Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(6), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w

Nitin, & Bal Gupta, S. (2023). Artificial Intelligence in Smart Agriculture: Applications and Challenges. Current Applied Science and Technology, 24(2), e0254427. https://doi.org/10.55003/cast.2023.254427

Nturambirwe, J. F. I., & Opara, U. L. (2020). Machine learning applications to non-destructive defect detection in horticultural products. Biosystems Engineering, 189, 60–83. https://doi.org/10.1016/j.biosystemseng.2019.11.011

Oliveira, R. C., & Silva, R. D. (2023). Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends. Appl. Sci., 13(13), 7405. https://doi.org/10.3390/app13137405

Opara, I. K., Opara, U. L., Okolie, J. A., & Fawole, O. A. (2024). Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review. Plants, 13(9), 1200. https://doi.org/10.3390/plants13091200

Peng, W., & Karimi Sadaghiani, O. (2023). A review on the applications of machine learning and deep learning in agriculture section for the production of crop biomass raw materials. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 45(3), 9178–9201. https://doi.org/10.1080/15567036.2023.2232322

Pokhariyal, S., Patel, N. R., & Govind, A. (2023). Machine Learning-Driven Remote Sensing Applications for Agriculture in India—A Systematic Review. Agronomy, 13(9), 2302. https://doi.org/10.3390/agronomy13092302

Sarkar, U., Banerjee, G., & Ghosh, I. (2022). Artificial intelligence in agriculture: Application trend analysis using a statistical approach. International Journal of Applied Science and Engineering, 20(1), 1–8. https://doi.org/10.6703/IJASE.202303_20(1).002

Senoo, E. E. K., Anggraini, L., Kumi, J. A., Karolina, L. B., Akansah, E., Sulyman, H. A., Mendonça, I., & Aritsugi, M. (2024). IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities. Electronics, 13(10), 1894. https://doi.org/10.3390/electronics13101894

Sharma, A., Jain, A., Gupta, P., & Chowdary, V. (2021). Machine Learning Applications for Precision Agriculture: A Comprehensive Review. IEEE Access, 9, 4843–4873. https://doi.org/10.1109/ACCESS.2020.3048415

Silge, J., & Robinson, David. (2017). Text Mining with R: A Tidy Approach (First edit). O’REILLY ®. USA. https://www.tidytextmining.com/

Thapa, B., Lovell, S., & Wilson, J. (2023). Remote sensing and machine learning applications for aboveground biomass estimation in agroforestry systems: a review. Agroforestry Systems, 97(6), 1097–1111. https://doi.org/10.1007/s10457-023-00850-2

Then, S.-M., Kokolski, M., Mbaki, Y., Merrick, D., & Anderson, S. (2022). Un enfoque colaborativo internacional para el aprendizaje de la histología utilizando un microscopio virtual. Revista de Medicina Veterinaria Serie C: Anatomía, Histología, Embriología, 1(52), 21-30. https://doi.org/10.1111/ahe.12888

Torres, Á. F., Maigua, S. I., Carcelén, Y. M., & Loján-Ramírez, E. (2024). Inteligencia Artificial en Educación Superior: Análisis Bibliométrico. Polo del conocimiento, 9(9), 477-497. https://polodelconocimiento.com/ojs/index.php/es/article/view/7946

Tripodi, P., Nicastro, N., & Pane, C. (2022). Digital applications and artificial intelligence in agriculture toward next-generation plant phenotyping. Crop & Pasture Science, 74(6), 597–614. https://doi.org/10.1071/CP21387

Wang, Y., Zhang, W., Gao, R., Jin, Z., & Wang, X. (2021). Recent advances in the application of deep learning methods to forestry. Wood Science and Technology, 55(5), 1171–1202. https://doi.org/10.1007/s00226-021-01309-2

Worthing, K. A., MadeleineRobert, & Šlapeta, J. (2024). Surveyed veterinary students in Australia find ChatGPT practical and relevant while expressing no concern about artificial intelligence replacing veterinarians. Veterinary Record Open, 11(1), e280. https://doi.org/10.1002/vro2.80

Yang, X., Zhang, S., Liu, J., Gao, Q., Dong, S., & Zhou, C. (2021). Deep learning for smart fish farming: Applications, opportunities and challenges. Reviews in Aquaculture, 13(1), 66–90. https://doi.org/10.1111/raq.12464

Yerlikaya, N., & Kúçúkaslan, Ö. (2024). Views of veterinary faculty students on the concept of Artificial Intelligence and its use in Veterinary Medicine practices: An example of Ankara University Faculty of Veterinary Medicine. Research Article, 71(3), 249-257. https://doi.org/10.33988/auvfd.1221352

Zheng, C., Abd-Elrahman, A., & Whitaker, V. (2021). Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming. Remote Sensing, 13(3), 531. https://doi.org/10.3390/rs13030531

Zhou, Z., Majeed, Y., Diverres Naranjo, G., & Gambacorta, E. M. T. (2021). Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications. Computers and Electronics in Agriculture, 182, 106019. https://doi.org/10.1016/j.compag.2021.106019

Zhu, J., & Weishu, L. (2020). A tale of two databases: the use of Web of Science and Scopus in academic papers. Scientometrics, 123(1), 321-335. https://doi.org/10.1007/s11192-020-03387-8

Zhu, N., Liu, X., Liu, Z., Hu, K., Wang, Y., Tan, J., Huang, M., Zhu, Q., Ji, X., Jiang, Y., & Guo, Y. (2018). Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. International Journal of Agricultural and Biological Engineering, 11(4), 32–44. https://doi.org/10.25165/j.ijabe.20181104.4475

Publicado
2026-01-01