Comparison of Vertex AI and Convolutional Neural Networks for Automatic Waste Sorting

Jhonny Darwin Ortiz-Mata (), Xiomara Jael Oleas-Vélez, Norma Alexandra Valencia-Castillo, Mónica del Rocío Villamar-Aveiga and David Elías Dáger-López
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Jhonny Darwin Ortiz-Mata: Facultad de Ciencias e Ingeniería, Universidad Estatal de Milagro (UNEMI), Milagro 091050, Ecuador
Xiomara Jael Oleas-Vélez: Facultad de Ciencias e Ingeniería, Universidad Estatal de Milagro (UNEMI), Milagro 091050, Ecuador
Norma Alexandra Valencia-Castillo: Facultad de Ciencias e Ingeniería, Universidad Estatal de Milagro (UNEMI), Milagro 091050, Ecuador
Mónica del Rocío Villamar-Aveiga: Facultad de Ciencias e Ingeniería, Universidad Estatal de Milagro (UNEMI), Milagro 091050, Ecuador
David Elías Dáger-López: Facultad de Ciencias e Ingeniería, Universidad Estatal de Milagro (UNEMI), Milagro 091050, Ecuador

Sustainability, 2025, vol. 17, issue 4, 1-23

Abstract: This study discusses the optimization of municipal solid waste management through the implementation of automated waste sorting systems, comparing two advanced artificial intelligence methodologies: Vertex AI and convolutional neural network (CNN) architectures, developed using TensorFlow. Automated solid waste classification is presented as an innovative technological approach that leverages advanced algorithms to accurately identify and segregate materials, addressing the inherent limitations of conventional sorting methods, such as high labor dependency, inaccuracies in material separation, and constrained scalability for processing large waste volumes. A system was designed for the classification of paper, plastic, and metal waste, integrating an Arduino Uno microcontroller, a Raspberry Pi, a high-resolution camera, and a robotic manipulator. The system was evaluated based on performance metrics including classification accuracy, response time, scalability, and implementation cost. The findings revealed that Xception achieved a flawless classification accuracy of 100% with an average processing time of 0.25 s, whereas Vertex AI, with an accuracy of 90% and a response time of 2 s, exceled in cloud scalability, making it ideal for resource-constrained environments. The findings highlight Xception’s superiority in high-precision applications and Vertex AI’s adaptability in scenarios demanding flexible deployment, advancing efficient and sustainable waste management solutions.

Keywords: automatic waste classification; Vertex AI; CNN; Xception; InceptionV3; ResNet50V2 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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