Revisión sistemática de literatura sobre soluciones computacionales aplicadas a la agricultura: diagnóstico visual de enfermedades, clasificación poscosecha y monitoreo inteligente mediante IoT

Agriculture faces increasing challenges related to timely disease diagnosis, postharvest quality assessment, and efficient farm management. In this context, computational solutions based on artificial intelligence, computer vision, and IoT technologies have emerged as key tools to support agricultural decision-making. This paper presents a systematic literature review (SLR) aimed at analyzing and synthesizing recent scientific evidence on computational approaches applied to (1) automatic disease detection in agricultural leaves and fruits, (2) surface classification and postharvest quality assessment of grains, and (3) farm monitoring and management through web platforms and IoT. The SLR followed the Kitchenham guidelines and the PRISMA protocol, applying explicit criteria for study identification, selection, quality assessment, and synthesis. A total of 263 initial records were identified; after screening and quality assessment, 16 primary studies were included: 6 on visual disease diagnosis (Axis A), 3 on postharvest grain classification (Axis B), and 7 on IoT-based farm monitoring (Axis C). The results indicate that deep learning (convolutional neural networks, attention mechanisms, and pretrained models) dominates visual diagnosis and postharvest classification. In addition, IoT-based infrastructures are widely adopted for agricultural monitoring, although usability evaluation and systematic application of standards such as ISO 9241 remain limited. Unlike previous reviews focused on generic technologies or specific crops, this SLR structures evidence by concrete agricultural problems, integrating visual diagnosis, postharvest classification, and IoT monitoring into a unified comparative analysis. Finally, the review identifies research gaps related to model generalization, dataset standardization, and validation under real-field conditions, which define future research directions in computational agriculture.

https://doi.org/10.61854/rccedia.v1n1.007

PDF (ES) (Español (España))

PDF

References

Abinaya, S., Kumar, K. U., & Alphonse, A. S. (2023). Cascading autoencoder with attention residual u-net for multi-class plant leaf disease segmentation and classification. IEEE Access, 11, 98153-98170. https://doi.org/10.1109/ACCESS.2023.3312718

Aliparo, J., Santos, C. D., Sese, C. M., Trinidad, J. ver, Beano, M. G., Capuno, M. E. A., Medina, O., Sigue, A., & Soriano, A. (2022). IoT-based assessment and monitoring of NPK content and fertility condition of soil. En TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON) (pp. 1-6). https://doi.org/10.1109/TENCON55691.2022.9978040

Archambault, P. (2024). Co-simulation and crop representation for digital twins of controlled environment agriculture systems. En Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems (pp. 196-199). https://doi.org/10.1145/3652620.3676879

Bhadra, T., Mujakkir-Ul-Islam, & Alam, N. (2025). IoT-based real-time data monitoring of a green house farm with offline monitoring capability. En 2025 4th International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) (pp. 111-116). https://doi.org/10.1109/ICREST63960.2025.10914444

Borromeo, K. O., Bonotan, M. R., Llagas, A. P., Juan, J. M. O., Sanchez, P. D. C., & Berro, N. K. A. (2025). Classification of cacao pods ripeness using convolutional neural networks. Procedia Computer Science, 257, 1197-1204. https://doi.org/10.1016/j.procs.2025.03.160

Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., & Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain. Journal of Systems and Software, 80(4), 571-583. https://doi.org/10.1016/j.jss.2006.07.009

Faisal, M., Leu, J.-S., & Darmawan, J. T. (2023). Model Selection of Hybrid Feature Fusion for Coffee Leaf Disease Classification. IEEE Access, 11, 62281-62291. https://doi.org/10.1109/ACCESS.2023.3286935

GAIDeT. (2025). Generative Artificial Intelligence Disclosure Taxonomy (GAIDeT). https://panbibliotekar.github.io/gaidet-declaration/

Garg, D., & Gill, K. S. (2024). Revolutionizing rice quality control: MobileNetV2 for grain classification. En 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) (pp. 809-812). https://doi.org/10.1109/ICAICCIT64383.2024.10912366

Hewage, P., Anderson, M., & Fang, H. (2017). An agile farm management information system framework for precision agriculture. En Proceedings of the 9th International Conference on Information Management and Engineering (pp. 75-80). https://doi.org/10.1145/3149572.3149583

Kant, V. (2024). Efficient rice grain classification using MobileNetV2: A deep learning approach. En 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS) (pp. 989-993). https://doi.org/10.1109/ICTACS62700.2024.10840597

Kitchenham, B., Pretorius, R., Budgen, D., Pearl Brereton, O., Turner, M., Niazi, M., & Linkman, S. (2010). Systematic literature reviews in software engineering – A tertiary study. Information and Software Technology, 52(8), 792-805. https://doi.org/10.1016/j.infsof.2010.03.006

Konakalla, S., Kunchala, L. V., & Yellampalli, S. S. (2021). Smart Agriculture using IoT & Filtering Technology. En 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-5). https://doi.org/10.1109/ICCCNT51525.2021.9579730

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310

Muthusamy, S., & Ramu, S. P. (2025). A dynamic attention-guided deep learning framework (d-pan) with reduced complexity for fine grained severity-aware classification of micronutrient deficiency and disease detection in banana and coffee leaves. IEEE Access, 13, 153474-153497. https://doi.org/10.1109/ACCESS.2025.3599359

Noon, S. K., Amjad, M., Qureshi, M. A., & Mannan, A. (2022). Handling Severity Levels of Multiple Co-Occurring Cotton Plant Diseases Using Improved YOLOX Model. IEEE Access, 10, 134811-134825. https://doi.org/10.1109/ACCESS.2022.3232751

Nugroho, L. E., Pratama, A. G. H., Mustika, I. W., & Ferdiana, R. (2017). Development of monitoring system for smart farming using Progressive Web App. En 2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 1-5). https://doi.org/10.1109/ICITEED.2017.8250513

Ouamane, A., Chouchane, A., Himeur, Y., Miniaoui, S., Atalla, S., Mansoor, W., & Al-Ahmad, H. (2025). Optimized vision transformers for superior plant disease detection. IEEE Access, 13, 48552-48570. https://doi.org/10.1109/ACCESS.2025.3547416

Pasaribu, F. B., Dewi, L. J. E., Aryanto, K. Y. E., & Varnakovida, P. (2025). Effect of synthetic data augmentation on plant classification accuracy using MobileNetV2, EfficientNet-B0, and ResNet-18. En 2025 IEEE 5th International Conference on Software Engineering and Artificial Intelligence (SEAI) (pp. 1-6). https://doi.org/10.1109/SEAI65851.2025.11108883

Prakasa, E., Prajitno, D. R., Nur, A., Sulistyo, K. A., & Rachmawati, E. (2021). Quality categorisation of corn (Zea mays) seed using feature-based classifier and deep learning on digital images. En 2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) (pp. 498-505). https://doi.org/10.1109/3ICT53449.2021.9581464

Rahman, Z. U., Asaari, M. S. M., Ibrahim, H., Abidin, I. S. Z., & Ishak, M. K. (2024). Generative Adversarial Networks (GANs) for image augmentation in farming: A review. IEEE Access, 12, 179912-179943. https://doi.org/10.1109/ACCESS.2024.3505989

Rao, A., Shao, H., & Yang, X. (2019). The design and implementation of smart agricultural management platform based on UAV and wireless sensor network. En 2019 IEEE 2nd International Conference on Electronics Technology (ICET) (pp. 248-252). https://doi.org/10.1109/ELTECH.2019.8839480

Yadav, S., T S, D., & Kokil, P. (2026). An IoT-enabled smart sensor system for real-time monitoring of soil and air conditions with integrated web-based farming guidance. IEEE Sensors Journal, 26(1), 1267-1275. https://doi.org/10.1109/JSEN.2025.3619663

Yeong, D. J., Panduru, K., & Walsh, J. (2024). Smart agriculture: Software platform for telematics monitoring in farm machinery. En 2024 35th Irish Signals and Systems Conference (ISSC) (pp. 1-6). https://doi.org/10.1109/ISSC61953.2024.10602840

Yue, X., Qi, K., Na, X., Liu, Y., Yang, F., & Wang, W. (2025). Deep learning for recognition and detection of plant diseases and pests. Neural Computing and Applications, 37(17), 11265-11310. https://doi.org/10.1007/s00521-025-11125-5

This work is licensed under a Creative Commons Attribution 4.0 International License.