Open AccessNeural network for crop rotation and soil analysis in a Greenhouse
Author(s) -
Eva Rafael-Pérez,
Yeimi Yanet Montero-Cortés,
Alan Eduardo Ruiz-Ramírez,
Maricela Morales-Hernández
Publication year - 2021
Publication title -
ecorfan
Language(s) - English
Resource type - Journals
ISSN - 2414-4924
DOI - 10.35429/ejdrc.2020.12.7.29.41
Subject(s) - artificial neural network , greenhouse , backpropagation , crop rotation , multilayer perceptron , computer science , agricultural engineering , java , artificial intelligence , rotation (mathematics) , data collection , process (computing) , perceptron , agriculture , machine learning , remote sensing , engineering , mathematics , statistics , agronomy , geography , programming language , archaeology , biology , operating system
Currently, Artificial intelligence (AI) is a very important area, the way in which it has revolutionized has allowed it to be an essential part of technological evolution in different sectors of society such as agriculture, it is a fundamental activity in the development of our country, and one of the developing areas is implementation of greenhouse crop. This article describes the use of artificial intelligence for a greenhouse through an Artificial Neural Network (ANN) of the multilayer perceptron type using the BackPropagation algorithm. The main aim is obtain the most optimal type of crop to be sown by means of the crop rotation, which, supported by a data acquisition device through sensors, obtains the values of temperature and humidity of the environment and soil pH, with those data the ANN makes the soil analysis. Through the interfaces of the data analysis module and the measurement module, the data collection process, the calculation and the results produced by the artificial neural network are shown. For this project, the Prototype model was used using the Java programming language.