Comparison of deep learning approach for water bottle waste Classification

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Thiraphat Chorakhe
Sangdaow Noppitak


An used plastic water bottle is an important problem affect to the ecosystem.  The aim of this research is to compare the efficiency in classification from datasets of the used plastic water bottle using Convolution Neural Network (CNN). The water bottle waste dataset consisted of 6 classes with a total of 841 images. First, a performance comparison was performed to find the best model from the three CNNs (EfficientNetV2B0, EfficientNetV2B1 and Efficient NetV2B3). EfficientNetV2B1 architecture has the highest accuracy of 98.30%. Then, it improves the three architectures’ performance using adding layers to the fully connected layer. The experimental results showed that the error of all three CNNs decreased compared to the method without adding layers. The highest accuracy achieves 98.59% from EfficientNetV2B3. Furthermore, EfficientNetV2B3 that the best model from the last experiment test on the testing dataset. The result of the highest accuracy was 97.16%, and the error was 0.08


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Chorakhe, T. ., & Noppitak, S. (2023). Comparison of deep learning approach for water bottle waste Classification . Kalasin University Journal of Science Technology and Innovation, 2(2), 53–65.
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