TY - JOUR
T1 - Artificial Intelligence Based System for Sorting and Detection of Organic and Inorganic Waste
AU - Meza, Angel Jair Castañeda
AU - Haro, Nicol’s Alexander Lopez
AU - Flores-Castañeda, Rosalynn Ornella
N1 - Publisher Copyright:
© (2025), (Science and Information Organization). All rights reserved.
PY - 2025
Y1 - 2025
N2 - Solid waste management has become a global challenge today due to its constant increase in waste and inadequate classification, which leads to serious environmental problems. The research objective is to develop a system based on artificial intelligence (AI) for the classification and detection of organic and inorganic waste. In terms of its approach, it is quantitative with a pre-experimental and applied design. The population was made up of 1,298 images as a data collection technique for observation. Furthermore, the implementation of this system has shown significant improvements in its key indicators: precision, detection speed, and reduction of errors in the tests carried out, obtaining an increase in precision of 11.52%, 23.61% in detection speed and a reduction in 24.13% error rate. Finally, this research highlights the importance of AI in environmental sustainability by promoting much more efficient waste management and thus promoting ecological awareness in educational environments and for students to value the importance of recycling and sustainability. Finally, this research concludes that AI-based systems are a viable and scalable solution to address all the challenges associated with waste management.
AB - Solid waste management has become a global challenge today due to its constant increase in waste and inadequate classification, which leads to serious environmental problems. The research objective is to develop a system based on artificial intelligence (AI) for the classification and detection of organic and inorganic waste. In terms of its approach, it is quantitative with a pre-experimental and applied design. The population was made up of 1,298 images as a data collection technique for observation. Furthermore, the implementation of this system has shown significant improvements in its key indicators: precision, detection speed, and reduction of errors in the tests carried out, obtaining an increase in precision of 11.52%, 23.61% in detection speed and a reduction in 24.13% error rate. Finally, this research highlights the importance of AI in environmental sustainability by promoting much more efficient waste management and thus promoting ecological awareness in educational environments and for students to value the importance of recycling and sustainability. Finally, this research concludes that AI-based systems are a viable and scalable solution to address all the challenges associated with waste management.
KW - Artificial intelligence (AI)
KW - environmental sustainability
KW - inorganic waste
KW - organic waste
KW - waste classification
UR - https://www.scopus.com/pages/publications/105008389657
U2 - 10.14569/IJACSA.2025.0160509
DO - 10.14569/IJACSA.2025.0160509
M3 - Artículo
AN - SCOPUS:105008389657
SN - 2158-107X
VL - 16
SP - 87
EP - 94
JO - International Journal of Advanced Computer Science and Applications
JF - International Journal of Advanced Computer Science and Applications
IS - 5
ER -