Bibliographic citations
Ruiz, E., Ortiz, M. (2024). Desarrollo de un análisis comparativo de desempeño computacional entre Nvidia Jetson Nano y el Raspberry Pi CM4, en el contexto de clasificación de frutas mediante algoritmos basados en Máquinas de Soporte Vectorial (SVM) y Redes Neuronales Convolucionales (CNN) [Trabajo de suficiencia profesional, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/683333
Ruiz, E., Ortiz, M. Desarrollo de un análisis comparativo de desempeño computacional entre Nvidia Jetson Nano y el Raspberry Pi CM4, en el contexto de clasificación de frutas mediante algoritmos basados en Máquinas de Soporte Vectorial (SVM) y Redes Neuronales Convolucionales (CNN) [Trabajo de suficiencia profesional]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2024. http://hdl.handle.net/10757/683333
@misc{renati/1300398,
title = "Desarrollo de un análisis comparativo de desempeño computacional entre Nvidia Jetson Nano y el Raspberry Pi CM4, en el contexto de clasificación de frutas mediante algoritmos basados en Máquinas de Soporte Vectorial (SVM) y Redes Neuronales Convolucionales (CNN)",
author = "Ortiz Rojas, Manuel Enrique",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2024"
}
This project presents a computational comparative analysis of two Single Board Computers (SBCs), the Nvidia Jetson Nano and the Raspberry Pi CM4, in the context of fruit classification using algorithms based on SVM and CNN. Given the growing demand for classification systems in the agro-industrial sector, these machine learning algorithms were implemented to evaluate the capabilities of each device in image classification, specifically for oranges, bananas, and apples, in terms of processing speed and computational capacity measured in FLOPs. The results indicate that the Nvidia Jetson Nano achieves higher performance using the CNN-based algorithm, with faster processing times and greater FLOPs. On the other hand, the Raspberry Pi CM4 performs better with the SVM-based algorithm, especially in tasks with lower computational load, allowing it to manage scenarios with high volumes of images classified per hour.
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