Agricultura de precisión en la producción de banano. Revisión sistemática
DOI:
https://doi.org/10.35381/i.p.v7i12.4450Palabras clave:
Agricultura de precisión, banano, inteligencia artificial, tecnología de la información, (Thesaurus AGROVOC).Resumen
Este trabajo consistió en una revisión sistemática la literatura sobre el uso de tecnologías de agricultura de precisión aplicadas en la producción de banano. Se aplicó la metodología PRISMA; sus fases son: identificación, selección e inclusión. Se consideraron criterios de inclusión y exclusión rigurosos para la búsqueda en bases de datos académicas y selección de artículos científicos de los últimos cinco años. El análisis permitió identificar las principales tecnologías empleadas en las distintas fases del cultivo, así como sus beneficios en la optimización del proceso productivo y en la mejora de la toma de decisiones basada en datos. Como resultado, se diseñó una arquitectura organizada en capas: percepción, red y aplicación, que integra herramientas, tecnologías (Internet de las Cosas e Inteligencia Artificial) y aplicaciones para cultivos de banano. En conclusión, esta arquitectura es una guía de referencia para los agricultores que buscan tecnificar sus procesos productivos
Descargas
Citas
Aeberli, A., Johansen, K., Robson, A., Lamb, D. W., & Phinn, S. (2021). Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery. Remote Sensing, 13(11). https://doi.org/10.3390/rs13112123
Aeberli, A., Phinn, S., Johansen, K., Robson, A., & Lamb, D. W. (2023). Characterisation of Banana Plant Growth Using High-Spatiotemporal-Resolution Multispectral UAV Imagery. Remote Sensing, 15(3), 679. https://doi.org/10.3390/rs15030679
Aeberli, A., Robson, A., Phinn, S., Lamb, D. W., & Johansen, K. (2022). A Comparison of Analytical Approaches for the Spectral Discrimination and Characterisation of Mite Infestations on Banana Plants. Remote Sensing, 14(21), 5467. https://doi.org/10.3390/rs14215467
Aherwadi, N., Mittal, U., Singla, J., Jhanjhi, N. Z., Yassine, A., & Hossain, M. S. (2022). Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms. Electronics, 11(24), 4100. https://doi.org/10.3390/electronics11244100
Ahmadi, A., Emami, M., Daccache, A., & He, L. (2021). Soil Properties Prediction for Precision Agriculture Using Visible and Near-Infrared Spectroscopy: A Systematic Review and Meta-Analysis. Agronomy, 11(3), 433. https://doi.org/10.3390/agronomy11030433
Akkem, Y., Biswas, S. K., & Varanasi, A. (2023). Smart farming using artificial intelligence: A review. Engineering Applications of Artificial Intelligence, 120, 105899. https://doi.org/10.1016/j.engappai.2023.105899
Alexopoulos, A., Koutras, K., Ali, S. B., Puccio, S., Carella, A., Ottaviano, R., & Kalogeras, A. (2023). Complementary Use of Ground-Based Proximal Sensing and Airborne/Spaceborne Remote Sensing Techniques in Precision Agriculture: A Systematic Review. Agronomy, 13(7), 1942. https://doi.org/10.3390/agronomy13071942
Almeyda, E., & Ipanaqué, W. (2022). Recent developments of artificial intelligence for banana: application areas, learning algorithms, and future challenges. Engenharia Agrícola, 42, e20210144. https://doi.org/10.1590/1809-4430-Eng.Agric.v42nepe20210144/2022
Aslan, M. F., Durdu, A., Sabanci, K., Ropelewska, E., & Gültekin, S. S. (2022). A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses. Applied Sciences, 12(3), 1047. https://doi.org/10.3390/app12031047
Ayesha Barvin, P., & Sampradeepraj, T. (2023). Crop Recommendation Systems Based on Soil and Environmental Factors Using Graph Convolution Neural Network: A Systematic Literature Review. Engineering Proceedings, 58(1), 97. https://doi.org/10.3390/ecsa-10-16010
Babalola, E.-O., Asad, M. H., & Bais, A. (2023). Soil Surface Texture Classification Using RGB Images Acquired Under Uncontrolled Field Conditions. IEEE Access, 11, 67140-67155. https://doi.org/10.1109/ACCESS.2023.3290907
Balafas, V., Karantoumanis, E., Louta, M., & Ploskas, N. (2023). Machine Learning and Deep Learning for Plant Disease Classification and Detection. IEEE Access, 11, 114352-114377. https://doi.org/10.1109/ACCESS.2023.3324722
Bouguettaya, A., Zarzour, H., Kechida, A., & Taberkit, A. M. (2023). A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images. Cluster Computing, 26(2), 1297-1317. https://doi.org/10.1007/s10586-022-03627-x
Calvo-Valverde, L. A., Rojas-Salazar, K., Hidalgo-Rodríguez, J. F., Mora, V., Sandoval, J. A., Bolaños-Céspedes, E., y Quirós, C. (2023). Un estudio de tecnologías sobre agentes conversacionales para la asistencia de agricultores del plátano. Revista Tecnología En Marcha, 36(4), 3-18. https://doi.org/10.18845/tm.v36i4.6242
Chen, T., Zhang, S., Chen, J., Fu, G., Chen, Y., & Zhu, L. (2024). Development, Integration, and Field Experiment Optimization of an Autonomous Banana-Picking Robot. Agriculture, 14(8), 1389. https://doi.org/10.3390/agriculture14081389
FAO. (2024a). Banano. Análisis del Mercado 2023. https://n9.cl/paecd6
FAO. (2024b, marzo 12). La FAO pide más cooperación en el sector bananero, de importancia para algunos de los países menos adelantados y los países de bajos ingresos y con déficit de alimentos, así como para los pequeños agricultores. Newsroom. https://n9.cl/e15i1
FAO. (2024c, agosto 26). Bananas | Markets and Trade | Food and Agriculture Organization of the United Nations. MarketsAndTrade. https://n9.cl/rb9cl
Faqir, Y., Qayoom, A., Erasmus, E., Schutte-Smith, M., & Visser, H. G. (2024). A review on the application of advanced soil and plant sensors in the agriculture sector. Computers and Electronics in Agriculture, 226, 109385. https://doi.org/10.1016/j.compag.2024.109385
Fu, L., Duan, J., Zou, X., Lin, J., Zhao, L., Li, J., & Yang, Z. (2020). Fast and Accurate Detection of Banana Fruits in Complex Background Orchards. IEEE Access, 8, 196835-196846. https://doi.org/10.1109/ACCESS.2020.3029215
Fu, L., Yang, Z., Wu, F., Zou, X., Lin, J., Cao, Y., & Duan, J. (2022). YOLO-Banana: A Lightweight Neural Network for Rapid Detection of Banana Bunches and Stalks in the Natural Environment. Agronomy, 12(2), 391. https://doi.org/10.3390/agronomy12020391
Gao, D., Sun, Q., Hu, B., & Zhang, S. (2020). A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles. Sensors, 20(5), 1487. https://doi.org/10.3390/s20051487
Ghazal, S., Munir, A., & Qureshi, W. S. (2024). Computer vision in smart agriculture and precision farming: Techniques and applications. Artificial Intelligence in Agriculture, 13, 64-83. https://doi.org/10.1016/j.aiia.2024.06.004
Khan, T., Sherazi, H. H. R., Ali, M., Letchmunan, S., & Butt, U. M. (2021). Deep Learning-Based Growth Prediction System: A Use Case of China Agriculture. Agronomy, 11(8), 1551. https://doi.org/10.3390/agronomy11081551
Khoroshevsky, F., Khoroshevsky, S., & Bar-Hillel, A. (2021). Parts-per-Object Count in Agricultural Images: Solving Phenotyping Problems via a Single Deep Neural Network. Remote Sensing, 13(13), 2496. https://doi.org/10.3390/rs13132496
Kirimi, F. K., Onyari, C. N., Njeru, L. K., & Mogaka, H. R. (2021). Effect of on-farm testing on adoption of banana production technologies among smallholder farmers in Meru region, Kenya. Journal of Agribusiness in Developing and Emerging Economies, 13(1), 90-105. https://doi.org/10.1108/JADEE-04-2021-0100
Krishnan, S., Karuppasamypandiyan, M., Chandran, R. R., & Devaraj, D. (2024). Revolutionizing agriculture: A comprehensive review of agribots, machine learning, and deep learning in meeting global food demands. Engineering Research Express, 6(3), 032201. https://doi.org/10.1088/2631-8695/ad59f6
Lamour, J., Le Moguédec, G., Naud, O., Lechaudel, M., Taylor, J., & Tisseyre, B. (2021). Evaluating the drivers of banana flowering cycle duration using a stochastic model and on farm production data. Precision Agriculture, 22(3), 873-896. https://doi.org/10.1007/s11119-020-09762-y
Mazón Olivo, B. (2023). Una arquitectura para la recopilación, integración y análisis de información en el contexto de la Internet de las Cosas. Caso estudio: Aplicaciones en el sector agrícola. [Tesis Doctoral, Universidad de Coruña]. Repositorio Universidade Coruña https://ruc.udc.es/dspace/handle/2183/33729
Mazon Olivo, B., & Pan, A. (2022). Internet of Things: State-of-the-art, Computing Paradigms and Reference Architectures. IEEE Latin America Transactions, 20(1), 49-63. https://doi.org/10.1109/TLA.2022.9662173
Melesse, T. Y., Bollo, M., Pasquale, V. D., Centro, F., & Riemma, S. (2022). Machine Learning-Based Digital Twin for Monitoring Fruit Quality Evolution. Procedia Computer Science, 200, 13-20. https://doi.org/10.1016/j.procs.2022.01.200
Mkhatshwa, J., Kavu, T., & Daramola, O. (2024). Analysing the Performance and Interpretability of CNN-Based Architectures for Plant Nutrient Deficiency Identification. Computation, 12(6), 113. https://doi.org/10.3390/computation12060113
Molina, E., & Parraga-Alava, J. (2024). Artificial Neural Networks for Classification Tasks: A Systematic Literature Review. Enfoque UTE, 15(4), 1–10. https://doi.org/10.29019/enfoqueute.1058
Muthusamy, S., & Ramu, S. P. (2024). IncepV3Dense: Deep Ensemble Based Average Learning Strategy for Identification of Micro-Nutrient Deficiency in Banana Crop. IEEE Access, 12, 73779-73792. https://doi.org/10.1109/ACCESS.2024.3405027
Najdenko, E., Lorenz, F., Dittert, K., & Olfs, H. W. (2024). Rapid in-field soil analysis of plant-available nutrients and pH for precision agriculture—A review. Precision Agriculture, 25(6), 3189-3218. https://doi.org/10.1007/s11119-024-10181-6
Nasim, S., Rashid, M., Syed, S. A., & Brohi, I. (2023). Artificial intelligence techniques for the pest detection in banana field: a systematic review. Pakistan Journal of Biotechnology, 20(02), 209-223. https://doi.org/10.34016/pjbt.2023.20.02.746
Nowak, B. (2021). Precision Agriculture: Where do We Stand? A Review of the Adoption of Precision Agriculture Technologies on Field Crops Farms in Developed Countries. Agricultural Research, 10(4), 515-522. https://doi.org/10.1007/s40003-021-00539-x
OEC. (2024). Plátanos frescos o secos (HS:): Comercio, Exportadores e Importadores. Observatorio de Complejidad Económica. https://n9.cl/ya70gg
Patrick, S., Mirau, S., Mbalawata, I., & Leo, J. (2023). Time series and ensemble models to forecast banana crop yield in Tanzania, considering the effects of climate change. Resources, Environment and Sustainability, 14, 100138. https://doi.org/10.1016/j.resenv.2023.100138
Ramírez-Orellana, A., Ruiz-Palomo, D., Rojo-Ramírez, A., & Burgos-Burgos, J. E. (2021). The Ecuadorian Banana Farms Managers’ Perceptions: Innovation as a Driver of Environmental Sustainability Practices. Agriculture, 11(3), 213. https://doi.org/10.3390/agriculture11030213
Rosero, L. C. C., Hernandez, G. L., Almanza, K. M., & Cuautle, J. J. A. F. (2024). Sensores Electrónicos en la Agricultura de Precisión. Ideas en Ciencias de la Ingeniería, 2(1), 4-16. https://doi.org/10.36677/ideaseningenieria.v2i1.22176
Segall, R. S., Takahashi, S., & Rajbhandari, P. (2024). Big Data Visualization for Black Sigatoka Disease of Bananas and Pathogen–Host Interactions (PHI) of Other Plants. International Journal of Applied Research in Bioinformatics (IJARB), 13(1), 1-22. https://doi.org/10.4018/IJARB.361940
Singh, R. K., Berkvens, R., & Weyn, M. (2021). AgriFusion: An Architecture for IoT and Emerging Technologies Based on a Precision Agriculture Survey. IEEE Access, 9, 136253-136283. https://doi.org/10.1109/ACCESS.2021.3116814
Talaviya, T., Shah, D., Patel, N., Yagnik, H., & Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58-73. https://doi.org/10.1016/j.aiia.2020.04.002
Yang, L., Cui, B., Wu, J., Xiao, X., Luo, Y., Peng, Q., & Zhang, Y. (2024). Automatic Detection of Banana Maturity—Application of Image Recognition in Agricultural Production. Processes, 12(4), 799. https://doi.org/10.3390/pr12040799
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2025 Cristhel Valeria Romero-García, Carmen Marlene Saraguro-Reyes, Bertha Eugenia Mazon-Olivo, Rodrigo Fernando Morocho-Román
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
CC BY-NC-SA : Esta licencia permite a los reutilizadores distribuir, remezclar, adaptar y construir sobre el material en cualquier medio o formato solo con fines no comerciales, y solo siempre y cuando se dé la atribución al creador. Si remezcla, adapta o construye sobre el material, debe licenciar el material modificado bajo términos idénticos.
OAI-PMH URL: https://fundacionkoinonia.com.ve/ojs/index.php/ingeniumetpotentia/oai
