| Summary: | Rapid economic growth and increasing urban population have led to a significant increase in waste production, raising serious concerns for countries worldwide. As the population expands, the increase in waste generation poses numerous environmental and public health challenges. This study focuses on educating children about recyclable waste to promote early awareness and proper waste classification habits. Specifically, this study investigates the performance of the YOLOv8 model to embed it into a waste recognition system tailored for children's waste management education. Datasets were obtained from Kaggle and underwent preprocessing. The findings show that a model with 100 epochs, an SGD optimizer, and a batch size of 25 achieved the best performance, with an accuracy of over 94% and a low loss of 0.367. This model demonstrated competitive accuracy in detecting and classifying waste images, highlighting its potential as an effective tool in educational programs aimed at teaching children the importance of waste management and promoting sustainable practices from an early age. © by the authors.
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