Prototipo de inteligencia artificial para el análisis de suelos: caso de estudio plantas de maíz. - De La Cruz Medina, Kevin Fernando (2021)
The present study focuses on the development of an artificial intelligence prototype for soil analysis, which has a sensor to measure the pH level in the ranges of 1 - 14, which are the standard ranges worldwide, where , the lower the rank, the more acidic it is considered, and the higher the rank, the more alkaline it is considered, on the other hand, if the rank is intermediate with a rank of 7 or approximately, it is considered neutral. And by using and programming an artificial neural network developed in Python, predict the amount of element that exists in the soils of corn plantations. As mentioned before, farmers in the province of Cotopaxi, Latacunga city, Ignacio Flores parish, Santan Grande neighborhood, via la vicentina do not have adequate knowledge about the pH level and amounts of elements that their corn plantation soils have, which It is the product that is most sown and harvested in the locality, causing a poor development of the corn and, as a consequence, a loss of money and time. The development of this artificial intelligence prototype is to provide farmers in the province of Cotopaxi, Latacunga city, Ignacio Flores parish, Santan Grande neighborhood, via the Vicentina information on the pH level and amount of existing element in their plantation soils. corn. The technological and social impact of the project is to generate a solution to a problem that very few people are aware of, through the use of technological tools such as: development IDE, programming languages and artificial neural networks. The development team has adequate knowledge of software management such as: Java, Python, MySQL database manager. For the development of the interface, the Netbeans development IDE was used, for the coding of the Arduino board the Arduino IDE was used and for the prediction of the element the Spyder IDE that works with the Python programming language. The development of this research is important because it allows farmers to generate knowledge about their soils in corn plantations with the help of technology.