Desarrollo de una aplicación web y móvil basada en machine learning, para monitoreo, registro y control de temperatura y humedad en el cultivo de la pitahaya.

Abstract

The present research work consists in the development and implementation of a technological tool with artificial intelligence based on machine learning, which is a web and mobile application that is under the android operating system. This implementation about machine learning and deep learning will be executed with Tensor Flow to access the graphic processing of the information. The main objective of this application is to increase the yield and quality of the pitahaya crop. Through monitoring, recording and controlling physical variables such as temperature and humidity in each of the phases of the crop and through the implementation of several devices such as: arduinos, microcontrollers, sensors and actuators that allow to reveal the respective plantations levels, and at the same time to identify which are the optimum levels of the crop in its best scenery taking into account its interaction with the environment. From these measurements, big data control is generated during the time of its implementation for its later analysis, which is considered in the decision making about the management strategies of the resources supplied in the production process. This transition seeks to leave behind conventional agriculture and make way for precision agriculture. For purposes of study, mixed methodology is used since it takes into account the qualitative and quantitative approach in this research that is exploratory and descriptive. Taking as research tools a survey and an interview based on a heuristic analysis for usability tests. As a final result, a versatile and intuitive low cost system that allows the monitoring, registration and control of the variables of a crop through an operator or user of Elisa application.