Treffer: Analysis of tools for developing an environmental support system to identify solid waste ; Análisis de herramientas para desarrollar un sistema de apoyo ambiental para identificar residuos sólidos

This paper presents a detailed methodology for the development of an environmental support system for solid waste identification. The methodology is divided into three fundamental stages: hardware selection, software selection, and preliminary algorithm design. In the first stage, a selection matrix is used to evaluate and compare several key hardware aspects, such as processors, RAM, programming languages, graphics processing, price and market availability. The weighted results indicate that the Raspberry Pi 4 and the Jetson Nano are the most suitable options, based on the specific needs of the project. In the second stage, a similar selection matrix is used to evaluate critical aspects of the software, such as licensing cost, library availability, integration with development environments, connectivity with office software and support for artificial intelligence. Python emerges as the ideal programming language, with a weight of 52.35%, due to its versatility and processing capacity. In addition, a proposed algorithm is presented that addresses data preparation, including image resizing, training and validation data labeling, grayscale image conversion, and data normalization. Taken together, this methodology provides a sound guide for the development of a system that can significantly contribute to environmental management and sustainability by effectively identifying solid waste. The proposed approach can have a positive impact on urban and rural environments by improving the efficiency of waste management. ; Este artículo presenta una metodología detallada para el desarrollo de un sistema de apoyo ambiental destinado a la identificación de residuos sólidos. La metodología se divide en tres etapas fundamentales: selección de hardware, selección de software y diseño preliminar del algoritmo. En la primera etapa, se utiliza una matriz de selección para evaluar y comparar diversos aspectos clave del hardware, como procesadores, memoria RAM, lenguajes de programación, procesamiento gráfico, precio y ...