Revisión sistemática sobre la clasificación de orquídeas mediante redes neuronales convolucionales: arquitecturas viables para Moyobamba
DOI:
https://doi.org/10.36825/RITI.14.34.004Palabras clave:
Redes Neuronales Convolucionales, Clasificación de Imágenes, Orquídeas, Revisión Sistemática, BiodiversidadResumen
El presente estudio aborda la limitada identificación de especies de orquídeas en contextos de alta biodiversidad, particularmente en Moyobamba, donde esta situación dificulta su conservación y aprovechamiento en ámbitos turísticos y educativos; por ello, el objetivo es analizar el estado del arte en la clasificación automática de orquídeas mediante redes neuronales convolucionales (CNN). La investigación se desarrolló mediante una revisión sistemática de la literatura basada en la metodología de revisión sistemática de Kitchenham, estructurada en fases de planificación, conducción y reporte, aplicando criterios de inclusión y exclusión a publicaciones entre 2024 y 2026 (con fecha de corte de búsqueda en enero de 2026), lo que permitió seleccionar 22 estudios primarios a partir de 371 registros iniciales. Los resultados evidencian un predominio de arquitecturas como ResNet, DenseNet y MobileNet, así como el uso frecuente de técnicas como Transfer Learning y Data Augmentation, que mejoran significativamente el rendimiento de los modelos. Se identifican limitaciones relacionadas con la escasez de datasets específicos de orquídeas y su implementación en entornos reales. Se concluye que las CNN constituyen una herramienta prometedora para la identificación automática de orquídeas, con potencial aplicación en contextos de biodiversidad tropical como el del Bosque de Protección Alto Mayo.
Citas
Akbar, F. A., Sari, C. A. (2025). Orchid species classification using the DenseNet121 deep learning model with a data imbalance handling approach. Journal of Applied Informatics and Computing, 9 (6), 3118-3129. https://doi.org/10.30871/jaic.v9i6.11458
Alvian Ideastari, N., Atika Sari, C., Faisal, E., Arifin, Z., Danang Krismawan, A., Muslih, M. (2024). An optimum hyperparameters of ResNet-50 for orchid classification based on convolutional neural network. Journal of Soft Computing Exploration, 5 (1), 55-66. https://doi.org/10.52465/joscex.v5i1.297
Apriyanti, D. H., Spreeuwers, L. J., Lucas, P. J. F. (2025). Explainable automated wild-orchid identification combining deep neural networks and Bayesian networks. Engineering Applications of Artificial Intelligence, 161, 1-17. https://doi.org/10.1016/j.engappai.2025.111961
Bouakkaz, H., Bouakkaz, M., Kerrache, C. A., Dhelim, S. (2025). Enhanced classification of medicinal plants using deep learning and optimized CNN architectures. Heliyon, 11 (3), 1-11. https://doi.org/10.1016/j.heliyon.2025.e42385
Shareena, E. M., Chandy, D. A., Shemi, P. M., Poulose, A. (2025). A hybrid deep learning model for aromatic and medicinal plant species classification using a curated leaf image dataset. AgriEngineering, 7 (8), 243. https://doi.org/10.3390/agriengineering7080243
Ganesh, C., Harshavardhan, G., Sri Keerthi, N. R., Yabaji, R. V., Rajveer Yabaji, M. S. (2025). Enhancing plant leaf classification with deep learning: Automating feature extraction for accurate species identification. SCT Proceedings in Interdisciplinary Insights and Innovations, 3, 1-13. https://doi.org/10.56294/piii2025513
Kutyrev, A., Andriyanov, N. (2024). Apple flower recognition using convolutional neural networks with transfer learning and data augmentation technique. E3S Web of Conferences, 493, 1-8. https://doi.org/10.1051/e3sconf/202449301006
Meneses Claudio, B. A. (2024). Development of an image recognition system based on neural networks for the classification of plant species in the Amazon rainforest, Peru, 2024. LatIA, 2, 1-13. https://doi.org/10.62486/latia202415
Pathan, A. I., Pandey, S. (2025). Fruit plant recognition and classification from plant leaves using deep learning, CNN models. International Journal of Recent Technology and Engineering (IJRTE), 14 (4), 16-25. https://doi.org/10.35940/ijrte.D8303.14041125
Song, X. (2025). Exploration and research of convolutional neural networks in image recognition. Applied and Computational Engineering, 121. https://doi.org/10.54254/2755-2721/121/2025.19558
Suliva, L. B., Bustillo, R. G. (2026). Flora Folium: Plant leaf identification using convolutional neural networks (CNN). Buana Information Technology and Computer Sciences (BIT and CS), 7 (1), 1-8. https://doi.org/10.36805/cgwb2584
Luna Bojórquez, J. L., Ramírez Noriega, A. D., Martínez Ramírez, Y. (2025). Clasificación de imágenes del dataset FBSI por medio de redes neuronales. Revista de Investigación en Tecnologías de la Información (RITI), 13 (32 Especial), 60-73. https://doi.org/10.36825/RITI.13.32.006
Andrade Carrera, H., Sinche Maita, S., Hidalgo Lascano, P. (2021). Modelo para detectar el uso correcto de mascarillas en tiempo real utilizando redes neuronales convolucionales. Revista de Investigación en Tecnologías de la Información (RITI), 9 (17 Especial), 111-120. https://doi.org/10.36825/RITI.09.17.011
Kitchenham, B., Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report No. EBSE-2007-01. Keele University & Durham University.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Patel, H. N., Modi, N., Bokhani, N. (2024). Efficient Indian flowers recognition model using deep convolutional neural networks. En V. Goar, A. Sharma, J. Shin, M. F. Mridha (Eds.) Deep Learning and Visual Artificial Intelligence (pp. 333-341). Springer. https://doi.org/10.1007/978-981-97-4533-3_25
Chetia, D., Kalita, S. K., Baruah, P. P. P., Dutta, D., Akhter, T. (2025). Identification of traditional medicinal plant leaves using an effective deep learning model and self-curated dataset. En A. Verma, P. Verma, K. K. Pattanaik, R. Buyya, D. Dasgupta (Eds.) Advanced Network Technologies and Intelligent Computing (pp. 342-356). https://doi.org/10.1007/978-3-031-83793-7_22
Juárez-Flores, M., Olguín-Rojas, J. C., Prado-Hernández, J. V., Loaeza-Joachin, J. (2025). Intelligent classification of three floral classes (Bellis perennis, Lavandula angustifolia, and Helianthus annuus) utilizing convolutional neural networks. En L. Martínez-Villaseñor, R. A. Vázquez, G. Ochoa-Ruiz (Eds.) Advances in Soft Computing (pp. 117-140). https://doi.org/10.1007/978-3-032-08704-1_10
Peng, Y., Xu, X., Zhang, L., Zhang, J., Wang, Y., Chen, M. (2024). Orchid2024: A cultivar-level dataset and methodology for fine-grained classification of Chinese Cymbidium orchids. Plant Methods, 20, 1-15. https://doi.org/10.1186/s13007-024-01252-w
Lee, S. H., Ku, H. C., Zhang, Y. C. (2024). Few-shot learning based on dual-attention mechanism for orchid species recognition. International Journal of Data Science and Analytics, 20 (4), 3439-3452. https://doi.org/10.1007/s41060-024-00671-1
Liu, S., Liu, H., Li, J., Wang, Y. (2025). Artificial and algorithmic screening of infrared spectral feature bands of Gastrodia elata to achieve rapid identification of its species. Journal of Chemometrics, 39 (1). https://doi.org/10.1002/cem.3641
Liu, Q., Hu, Y., Liu, H. (2025). Multitarget recognition of flower images based on lightweight deep neural network and transfer learning. Advanced Intelligent Systems, 8 (2), 1-20. https://doi.org/10.1002/aisy.202500540
Hu, W. C., Chen, L. B., Huang, X. R., Huang, G. Z. (2025). OrchidNet: A self-supervised learning-based efficient multiscale feature fusion convolutional neural network with a lightweight architecture for orchid classification. IEEE Internet of Things Journal, 12 (5), 5859-5875. https://doi.org/10.1109/JIOT.2024.3488736
Chen, L. B., Huang, X. R., Huang, G. Z., Kuo, S. Y. (2024). An orchid classification scheme using deep learning for automated packaging in production lines. IEEE Consumer Electronics Magazine, 14 (1), 65-76. https://doi.org/10.1109/MCE.2024.3434910
Suvarna Vani, K., Kalakota, H. R., Velisala, V., Sree Vijaya Lakshmi, K. (2024). Medicinal flower detection using CNN algorithm. 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud). Kirtipur, Nepal.. https://doi.org/10.1109/I-SMAC61858.2024.10714599
Chauhan, S. (2024). Optimizing flower classification with EfficientNetB3: Insights and results from a high-performance model. International Conference on Innovation and Novelty in Engineering and Technology (INNOVA). Vijayapura, India. https://doi.org/10.1109/INNOVA63080.2024.10847051
Kant, V., Kumar, K. S. (2025). Automated flower recognition using fine-tuned ResNet: A solution for efficient botanical classification. 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. Raigarh, India. https://doi.org/10.1109/OTCON65728.2025.11070970
Bhoomika, Manchanda, R., Badhani, R., Kharola, A., Singh, R., Gupta, S. (2025). Deep neural network for flower type detection: A fine-tuned EfficientNetB3 approach. International Conference on Electrical, Electronics, and Computer Science with Advance Power Technologies - A Future Trends (ICE2CPT). Jamshedpur, India. https://doi.org/10.1109/ICE2CPT66440.2025.11340009
Verma, G., Prasad, C., Mahajan, S. (2024). Deep convolutional networks for flower species recognition: A ResNet study. 2nd International Conference on Computational and Characterization Techniques in Engineering and Sciences (IC3TES). Lucknow, India. https://doi.org/10.1109/IC3TES62412.2024.10877502
Han, J., Hu, Q., Wang, Y. (2025). Rapid and accurate identification of Dendrobium species using FT-IR, FT-NIR, and data fusion with machine learning. Industrial Crops and Products, 234, 1-14. https://doi.org/10.1016/j.indcrop.2025.121637
Xu, D., Huang, X., Zhao, Z., Zhao, Z., Hu, D., Yuan, C. (2026). Pha-YOLO: A multi-view Phalaenopsis flower detection and counting method based on improved YOLO11 with dynamic reference selection and adaptive thresholding. Computers and Electronics in Agriculture, 243. https://doi.org/10.1016/j.compag.2026.111446
Karimi, Z., Karamad, M., Rahmany, S. (2025). Enhanced flower image classification: A multimodal approach leveraging Persian texts and deep learning. Engineering Applications of Artificial Intelligence, 159. https://doi.org/10.1016/j.engappai.2025.111642
Jiang, J., Yang, X., Yan, H., Liu, J., Chen, Y., Mao, Z., Wang, T. (2026). Chrysanthemum classification method via multi-stream deep color space feature fusion. Computers and Electronics in Agriculture, 244. https://doi.org/10.1016/j.compag.2026.111455
Li, G., Li, J., Liu, H., Wang, Y. (2025). Variant identification of Gastrodia elata Bl. using deep learning assisted FTIR. Industrial Crops and Products, 237, 1-14. https://doi.org/10.1016/j.indcrop.2025.122313
Guo, T., Li, Q., Wang, C., Liu, M., Ge, F., Zhou, X., Zhang, X., Ma, J. (2025). Rapid identification of Gastrodia elata Blume hybrids using near-infrared spectroscopy combined with lightweight depthwise separable convolutional neural network. Microchemical Journal, 212. https://doi.org/10.1016/j.microc.2025.113273
Xu, Y., Li, L., Wang, Y., Hu, Q. (2025). Novel non-destructive authentication of nine Dendrobium species using residual convolutional neural network relying on plant images and FT-NIR spectral information. Smart Agricultural Technology, 11, 1-13. https://doi.org/10.1016/j.atech.2025.101027
Sangiovanni, M., Starace, L. L. L., Di Pierro, D., Cozzolino, S., Addonizio, E. (2025). OrchID: Explainable deep learning for Ophrys orchid biodiversity monitoring. Array, 27, 1-13. https://doi.org/10.1016/j.array.2025.100480
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2026 Revista de Investigación en Tecnologías de la Información
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Esta revista proporciona un acceso abierto a su contenido, basado en el principio de que ofrecer al público un acceso libre a las investigaciones ayuda a un mayor intercambio global del conocimiento.
El texto publicado en la Revista de Investigación en Tecnologías de la Información (RITI) se distribuye bajo la licencia Creative Commons (CC BY-NC
), que permite a terceros utilizar lo publicado citando a los autores del trabajo y a RITI, pero sin hacer uso del material con propósitos comerciales.
