Accurate and Efficient Solid Waste Recognition: A Novel Approach Using Google Teachable Machine Based on Convolutional Neural Network (CNN)
László Duma, Marcos Daniel Cortijo Mendoza · 2025
This paper explores the use of Convolutional Neural Networks (CNNs) for solid waste recognition, using Google Teachable Machine. A CNN model was trained on 13,745 images from Kaggle datasets to classify waste into four categories: Dangerous, Recyclable, Organic, and Non-Recyclable. Evaluation on 80 test images showed an overall accuracy of 82.5%, with the highest performance in the Dangerous, Organic and Non-Recyclable categories, while Recyclable waste had the highest misclassification rate. A confusion matrix analysis revealed that Recyclable waste was often misidentified as other categories. A comparison between manual testing and Google Teachable Machine’s accuracy reports showed consistent classification trends, reinforcing the importance of real-world validation. Accuracy and loss per epoch graphs confirmed stable training. The findings highlight the potential of AI in waste management and suggest improvements such as dataset expansion and real-time image augmentation.