Aplicación móvil agrícola con redes neuronales convolucionales para control de producción de hongos comestibles
Agricultural mobile application based on convolutional neural networks for the control of edible mushroom productionBarra lateral del artículo
Contenido principal del artículo
La producción de hongos comestibles es un sector agrícola en crecimiento que requiere control preciso de variables ambientales. Este estudio tuvo por objetivo desarrollar una aplicación móvil agrícola integrada con redes neuronales convolucionales para mejorar el control de información en la producción de hongos comestibles en NEOS, Bolivia. Se aplicó un enfoque mixto con metodología Scrum para el desarrollo del sistema BPM, implementando sensores IoT y algoritmos CNN. La muestra incluyó 50 productores (10 de NEOS y 40 de Chuquisaca). Los resultados mostraron una mejora del 25% en la detección temprana de contaminaciones y un incremento del 18% en la eficiencia de los procesos de colonización y fructificación. La aplicación permitió monitoreo remoto en tiempo real y generación automática de reportes, contribuyendo a la sostenibilidad y eficiencia productiva. Se concluye que la integración de tecnologías emergentes mejora sustancialmente la gestión en la producción de hongos comestibles.
Edible mushroom production is an expanding agricultural sector that demands precise control of environmental variables. This study aimed to develop an agricultural mobile application integrated with convolutional neural networks (CNNs) to enhance information management and production control of edible mushrooms in NEOS, Bolivia. A mixed-methods approach was applied, employing the Scrum framework for the development of a Business Process Management (BPM) system and implementing Internet of Things (IoT) sensors in conjunction with CNN algorithms. The study sample consisted of 50 producers (10 from NEOS and 40 from Chuquisaca). The results demonstrated a 25% improvement in early contamination detection and an 18% increase in the efficiency of colonization and fruiting processes. The application enabled real-time remote monitoring and automatic report generation, contributing to greater sustainability and production efficiency. These findings indicate that integrating emerging technologies can significantly enhance management and decision-making in edible mushroom production systems.
Detalles del artículo
Badoni, A., & Siddiqui, M. H. (2025a). Nutritional, medicinal, and economic potential of edible mushrooms: A global perspective. Journal of Agricultural Science, 15(2), 123-135. https://doi.org/10.3390/plants14152364
Badoni, A., y Siddiqui, M. H. (2025b). Metamorphosis of mushroom production from tradition to automation. Discover Applied Sciences, 7(974). https://doi.org/10.1007/s42452-025-07517-w
Balan, V., Zhu, W., Krishnamoorthy, H., Benhaddou, D., Mowrer, J., Husain, H., & Eskandari, A. (2022). Challenges and opportunities in producing high-quality edible mushrooms from lignocellulosic biomass in a small scale. Applied Microbiology and Biotechnology, 106(4), 1355-1374. https://doi.org/10.1007/s00253-021-11749-2
Bwambale, E., Abagale, F. K., & Anornu, G. K. (2022). Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review. Agricultural Water Management, 260, 107324. https://doi.org/10.1016/j.agwat.2021.107324
Elewi, A., Al-Jubouri, M., y Hassan, R. (2024). Design and implementation of a cost-aware and smart oyster mushroom farming system based on IoT and machine learning. Smart Agricultural Technology, 8, 100443. https://doi.org/10.1016/j.atech.2024.100443
Getahun, S., Kefale, H., & Gelaye, Y. (2024). Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic review. The Scientific World Journal, 2024(1), 2126734. https://doi.org/10.1155/2024/2126734
Gupta, A., & Singh, A. (2022). An intelligent healthcare cyber physical framework for encephalitis diagnosis based on information fusion and soft-computing techniques. New Generation Computing, 40(4), 1093-1123. https://doi.org/10.1007/s00354-022-00175-1
Kewessa, G., Dejene, T., Alem, D., Tolera, M., & Martín-Pinto, P. (2022). Forest type and site conditions influence the diversity and biomass of edible macrofungal species in Ethiopia. Journal of Fungi, 8(10), 1023. https://doi.org/10.3390/jof8101023
Mandal, S., Yadav, A., Panme, F. A., Devi, K., y Kumar, S. (2024). Adaption of smart applications in agriculture to enhance production. Smart Agricultural Technology, 7, 100431. https://doi.org/10.1016/j.atech.2024.100431
Mansoor, S., Rahman, A., y Li, J. (2025). Integration of smart sensors and IoT in precision agriculture. Frontiers in Plant Science, 16, 1587869. https://doi.org/10.3389/fpls.2025.1587869
Mendoza-Bernal, J., González-Vidal, A., & Skarmeta, A. F. (2024). A convolutional neural network approach for image-based anomaly detection in smart agriculture. Expert Systems with Applications, 247, 123210. https://doi.org/10.1016/j.eswa.2024.123210
Miller, T., Mikiciuk, G., Durlik, I., Mikiciuk, M., Łobodzińska, A., & Śnieg, M. (2025). The IoT and AI in Agriculture: The Time Is Now—A Systematic Review of Smart Sensing Technologies. Sensors (Basel, Switzerland), 25(12), 3583. https://doi.org/10.3390/s25123583
Mowla, M. N., Mowla, N., Shah, A. S., Rabie, K. M., & Shongwe, T. (2023). Internet of Things and wireless sensor networks for smart agriculture applications: A survey. IEEe Access, 11, 145813-145852. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10371307
Padhiary, M., Kumar, A., & Sethi, L. N. (2025). Emerging technologies for smart and sustainable precision agriculture. Discover Robotics, 1(1), 6. https://doi.org/10.1007/s44430-025-00006-0
Postolache, S., Sebastião, P., Viegas, V., Postolache, O., & Cercas, F. (2022). IoT-based systems for soil nutrients assessment in horticulture. Sensors, 23(1), 403. https://doi.org/10.3390/s23010403
Quy, V. K., Hau, N. V., Anh, D. V., Quy, N. M., Ban, N. T., Lanza, S., ... & Muzirafuti, A. (2022). IoT-enabled smart agriculture: architecture, applications, and challenges. Applied Sciences, 12(7), 3396. https://doi.org/10.3390/app12073396
Rejeb, A., Keogh, J. G., Zailani, S., Treiblmaier, H., & Rejeb, K. (2020). Blockchain technology in the food industry: A review of potentials, challenges and future research directions. Logistics, 4(4), 27. https://doi.org/10.3390/logistics4040027
Singh, K., Yadav, M., Singh, Y., & Moreira, F. (2025). Techniques in reliability of internet of things (IoT). Procedia Computer Science, 256, 55-62. https://doi.org/10.1016/j.procs.2025.02.095
Sofiev, M., Buters, J., Tummon, F., Fatahi, Y., Sozinova, O., Adams-Groom, B., ... & Clot, B. (2023). Designing an automatic pollen monitoring network for direct usage of observations to reconstruct the concentration fields. Science of the Total Environment, 900, 165800. https://doi.org/10.1016/j.scitotenv.2023.165800
Yang, Z. Y., Xia, W. K., Chu, H. Q., Su, W. H., Wang, R. F., & Wang, H. (2025). A comprehensive review of deep learning applications in cotton industry: From field monitoring to smart processing. Plants, 14(10), 1481. https://doi.org/10.3390/plants14101481