Diseño de plataforma tecnológica basada en machine learning para controlar los factores que intervienen en la producción de cultivos aeropónicos
Fecha
2021
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Universidad de Guayaquil. Facultad de Ciencias Matemáticas y Físicas. Carrera de Ingeniería en Networking y Telecomunicaciones
Resumen
El sector agrícola busca aumentar los procesos de producción en cultivos aeropónicos
disminuyendo el impacto ambiental debido a esto se ha vinculado una tecnología que
permite la evolución de estos procesos, formando un sistema automatizado, amigable y
que potencialice sus cultivos. Entre los hallazgos investigados en la cotidianidad de los
procesos de producción de cultivos, se encontró la necesidad de una plataforma que
permite el monitoreo en los factores principales que intervienen en el desarrollo de
cultivos aeropónicos. Factores que pueden ser almacenados e interpretados para su
posterior análisis y mejora. Encontrando que una buena manera de automatizar un
sistema es que él aprenda de la información que se le proporcione y pueda adaptarse a os cambios que surgen siendo el Machine Learning (Inteligencia Artificial) una tecnología
que proporciona todas las características para la innovación de cultivos aeropónicos.
Para el presente proyecto integrador se realizó el diseño de un prototipo basado en
machine learning para el monitoreo de cultivos aeropónicos. Se implementó un sistema
aeropónico con rábanos del cual se obtuvo datos a diario tomados de un proceso
automatizado con sensores de temperatura (ambiental y agua), bomba de agua
temporizada, sensor de calidad de aire y medidor de Ph, que entregan información a un
módulo esp32 el cual codifica los datos y los envía a una tarjeta Raspberry Pi 4 vía WiFi.
Adicionalmente las variables se almacenan en una base de datos que permite alimentar
a la plataforma que muestra las estadísticas de crecimiento de las plantas y alimenta el
algoritmo de machine learning para mejoramiento de condiciones del cultivo.
The agricultural sector seeks to increase the production processes in aeroponic crops decreasing the environmental impact due to this, a technology has been linked that allows the evolution of these processes, forming an automated system, friendly and that potentiates their crops. Among the findings investigated in the daily processes of crop production, it was found the need for a platform that allows the monitoring of the main factors involved in the development of aeroponic crops. Factors that can be stored and interpreted for further analysis and improvement. Finding that a good way to automate a system is that it learns from the information provided and can adapt to the changes that arise being the Machine Learning (Artificial Intelligence) a technology that provides all the features for the innovation of aeroponic crops. For this integrative project, the design of a prototype based on machine learning for the monitoring of aeroponic crops was carried out. An aeroponic system with radishes was implemented and daily data was obtained from an automated process with temperature sensors (environmental and water), timed water pump, air quality sensor and Ph meter, which deliver information to an esp32 module which encodes the data and sends it to a Raspberry Pi 4 card via WiFi. Additionally, the variables are stored in a database that allows feeding the platform that shows the statistics of plant growth and feeds the machine learning algorithm for improving crop conditions.
The agricultural sector seeks to increase the production processes in aeroponic crops decreasing the environmental impact due to this, a technology has been linked that allows the evolution of these processes, forming an automated system, friendly and that potentiates their crops. Among the findings investigated in the daily processes of crop production, it was found the need for a platform that allows the monitoring of the main factors involved in the development of aeroponic crops. Factors that can be stored and interpreted for further analysis and improvement. Finding that a good way to automate a system is that it learns from the information provided and can adapt to the changes that arise being the Machine Learning (Artificial Intelligence) a technology that provides all the features for the innovation of aeroponic crops. For this integrative project, the design of a prototype based on machine learning for the monitoring of aeroponic crops was carried out. An aeroponic system with radishes was implemented and daily data was obtained from an automated process with temperature sensors (environmental and water), timed water pump, air quality sensor and Ph meter, which deliver information to an esp32 module which encodes the data and sends it to a Raspberry Pi 4 card via WiFi. Additionally, the variables are stored in a database that allows feeding the platform that shows the statistics of plant growth and feeds the machine learning algorithm for improving crop conditions.
Descripción
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Palabras clave
Machine Learning, Raspberry pi, Sensor, Base de Datos