Sistema IOT para el monitoreo y control de un cultivo hidropónico de lechuga con análisis mediante machine learning
Este proyecto de tesis de grado propone el desarrollo e implementación de un sistema basado en IoT para la monitorización y el control de un cultivo hidropónico doméstico de lechuga, utilizando la Técnica de Película Nutritiva (NFT) y análisis mediante aprendizaje automático. El proyecto aborda los...
- Autores:
-
Becerra Velásquez, José Javier
Uribe Trujillo, Juan Fernando
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2025
- Institución:
- Universidad de San Buenaventura
- Repositorio:
- Repositorio USB
- Idioma:
- spa
- OAI Identifier:
- oai:bibliotecadigital.usb.edu.co:10819/26492
- Acceso en línea:
- https://hdl.handle.net/10819/26492
- Palabra clave:
- Dispositivos móviles
Cultivo hidropónico
Machine learning
620 - Ingeniería y operaciones afines
IoT
Machine learning
Edge computing
ThingSpeak
Hidroponía
Cultivo hidropónico
Monitoreo ambiental
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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Sistema IOT para el monitoreo y control de un cultivo hidropónico de lechuga con análisis mediante machine learning |
| title |
Sistema IOT para el monitoreo y control de un cultivo hidropónico de lechuga con análisis mediante machine learning |
| spellingShingle |
Sistema IOT para el monitoreo y control de un cultivo hidropónico de lechuga con análisis mediante machine learning Dispositivos móviles Cultivo hidropónico Machine learning 620 - Ingeniería y operaciones afines IoT Machine learning Edge computing ThingSpeak Hidroponía Cultivo hidropónico Monitoreo ambiental |
| title_short |
Sistema IOT para el monitoreo y control de un cultivo hidropónico de lechuga con análisis mediante machine learning |
| title_full |
Sistema IOT para el monitoreo y control de un cultivo hidropónico de lechuga con análisis mediante machine learning |
| title_fullStr |
Sistema IOT para el monitoreo y control de un cultivo hidropónico de lechuga con análisis mediante machine learning |
| title_full_unstemmed |
Sistema IOT para el monitoreo y control de un cultivo hidropónico de lechuga con análisis mediante machine learning |
| title_sort |
Sistema IOT para el monitoreo y control de un cultivo hidropónico de lechuga con análisis mediante machine learning |
| dc.creator.fl_str_mv |
Becerra Velásquez, José Javier Uribe Trujillo, Juan Fernando |
| dc.contributor.advisor.none.fl_str_mv |
Pincay Lozada, Jorge Luis |
| dc.contributor.author.none.fl_str_mv |
Becerra Velásquez, José Javier Uribe Trujillo, Juan Fernando |
| dc.subject.armarc.none.fl_str_mv |
Dispositivos móviles Cultivo hidropónico Machine learning |
| topic |
Dispositivos móviles Cultivo hidropónico Machine learning 620 - Ingeniería y operaciones afines IoT Machine learning Edge computing ThingSpeak Hidroponía Cultivo hidropónico Monitoreo ambiental |
| dc.subject.ddc.none.fl_str_mv |
620 - Ingeniería y operaciones afines |
| dc.subject.proposal.eng.fl_str_mv |
IoT Machine learning Edge computing ThingSpeak |
| dc.subject.proposal.spa.fl_str_mv |
Hidroponía Cultivo hidropónico Monitoreo ambiental |
| description |
Este proyecto de tesis de grado propone el desarrollo e implementación de un sistema basado en IoT para la monitorización y el control de un cultivo hidropónico doméstico de lechuga, utilizando la Técnica de Película Nutritiva (NFT) y análisis mediante aprendizaje automático. El proyecto aborda los retos actuales de la sostenibilidad alimentaria urbana promoviendo soluciones accesibles, automatizadas y eficientes para la agricultura a pequeña escala. El objetivo principal es implementar un sistema capaz de monitorizar variables críticas del cultivo, como la temperatura, la humedad y el pH del agua, en tiempo real, utilizando un microcontrolador ESP32 y sensores especializados conectados a la plataforma ThingSpeak®. La metodología sigue el enfoque CDIO (Concebir, Diseñar, Implementar y Operar), que guía el proyecto desde la identificación del problema hasta la validación práctica. Se diseñó y construyó un invernadero a escala, integrando sensores, actuadores y un modelo de clasificación basado en árboles de decisión para evaluar la salud del cultivo. Este modelo se entrena e implementa mediante Edge Computing, lo que reduce la latencia y la dependencia de los servicios en la nube. |
| publishDate |
2025 |
| dc.date.accessioned.none.fl_str_mv |
2025-08-04T21:57:17Z |
| dc.date.available.none.fl_str_mv |
2025-08-04T21:57:17Z |
| dc.date.issued.none.fl_str_mv |
2025 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Pregrado |
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http://purl.org/coar/resource_type/c_7a1f |
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Text |
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info:eu-repo/semantics/bachelorThesis |
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http://purl.org/redcol/resource_type/TP |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_7a1f |
| status_str |
acceptedVersion |
| dc.identifier.citation.none.fl_str_mv |
Uribe, Becerra, “Sistema IOT para el monitoreo y control de un cultivo hidropónico de lechuga con análisis mediante machine learning.”, Trabajo de grado profesional, Ingeniería Electrónica, Universidad de San Buenaventura Cali (Valle del Cauca), 2025 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/10819/26492 |
| identifier_str_mv |
Uribe, Becerra, “Sistema IOT para el monitoreo y control de un cultivo hidropónico de lechuga con análisis mediante machine learning.”, Trabajo de grado profesional, Ingeniería Electrónica, Universidad de San Buenaventura Cali (Valle del Cauca), 2025 |
| url |
https://hdl.handle.net/10819/26492 |
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spa |
| language |
spa |
| dc.relation.references.none.fl_str_mv |
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Pincay Lozada, Jorge Luisvirtual::4560-1Becerra Velásquez, José JavierUribe Trujillo, Juan Fernando2025-08-04T21:57:17Z2025-08-04T21:57:17Z2025Este proyecto de tesis de grado propone el desarrollo e implementación de un sistema basado en IoT para la monitorización y el control de un cultivo hidropónico doméstico de lechuga, utilizando la Técnica de Película Nutritiva (NFT) y análisis mediante aprendizaje automático. El proyecto aborda los retos actuales de la sostenibilidad alimentaria urbana promoviendo soluciones accesibles, automatizadas y eficientes para la agricultura a pequeña escala. El objetivo principal es implementar un sistema capaz de monitorizar variables críticas del cultivo, como la temperatura, la humedad y el pH del agua, en tiempo real, utilizando un microcontrolador ESP32 y sensores especializados conectados a la plataforma ThingSpeak®. La metodología sigue el enfoque CDIO (Concebir, Diseñar, Implementar y Operar), que guía el proyecto desde la identificación del problema hasta la validación práctica. Se diseñó y construyó un invernadero a escala, integrando sensores, actuadores y un modelo de clasificación basado en árboles de decisión para evaluar la salud del cultivo. Este modelo se entrena e implementa mediante Edge Computing, lo que reduce la latencia y la dependencia de los servicios en la nube.This undergraduate thesis project proposes the development and implementation of an IoT-based system for monitoring and controlling a domestic hydroponic lettuce crop, using the Nutrient Film Technique (NFT) and analysis through machine learning. The project addresses current challenges in urban food sustainability by promoting accessible, automated, and efficient solutions for small-scale agriculture. The main objective is to implement a system capable of monitoring critical crop variables—such as temperature, humidity, and water pH—in real time, using an ESP32 microcontroller and specialized sensors connected to the ThingSpeak® platform. The methodology follows the CDIO (Conceive, Design, Implement, and Operate) approach, guiding the project from problem identification to practical validation. A scaled greenhouse was designed and built, integrating sensors, actuators, and a decision tree-based classification model to assess crop health. This model is trained and deployed through Edge computing, reducing latency and dependence on cloud services.PregradoIngeniero Electrónico174 páginasapplication/pdfUribe, Becerra, “Sistema IOT para el monitoreo y control de un cultivo hidropónico de lechuga con análisis mediante machine learning.”, Trabajo de grado profesional, Ingeniería Electrónica, Universidad de San Buenaventura Cali (Valle del Cauca), 2025https://hdl.handle.net/10819/26492spaUniversidad de San Buenaventura - CaliCaliFacultad de IngenieríaCaliIngeniería ElectrónicaUnited Nations Department of Economic and Social Affairs, “68% of the world population projected to live in urban areas by 2050, says UN,” UN DESA, Nov. 30, 2024. [Online]. Available: https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.htmlK. 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Available: https://hdl.handle.net/20.500.14138/405. Accessed: Nov. 30, 2024.Programar Fácil, “Cómo utilizar el DHT11 para medir la temperatura y humedad con Arduino,” [Online]. Available: https://programarfacil.com/blog/arduino-blog/sensor-dht11-temperatura-humedad-arduino/. Accessed: Dec. 1, 2024.Programar Fácil, “ESP32 Wifi + Bluetooth en un solo lugar,” [Online]. Available: https://programarfacil.com/esp8266/esp32/. Accessed: Dec. 1, 2024.ThingSpeak, “IoT: El Internet de las cosas en las empresas Cordobesas,” [Online]. Available: https://sites.google.com/ieslafuensanta.es/iot/proceso/estudio-plataformas-iot/software/thingspeak. Accessed: Dec. 1, 2024.L. S. Kondaka, R. Iyer, S. Jaiswal, and A. Ali, “A Smart Hydroponic Farming System Using Machine Learning,” in Proc. ICIITCEE 2023, pp. 357–362, 2023, doi: 10.1109/IITCEE57236.2023.10090860.D. Adidrana and N. Surantha, “Hydroponic Nutrient Control System based on Internet of Things and K-Nearest Neighbors,” in Proc. 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