Research on bamboo strip density control technology based on deep learning.

This study proposes a deep learning-based method for the automated density detection of bamboo strips, a critical factor in quality control. Addressing the limitations of traditional inspection methods, our research focuses on quantifying bamboo density by analyzing the distribution of vascular bundles, its primary determinant. We utilized a dataset of bamboo strip cross-sectional images to train and evaluate eleven mainstream deep learning models, including both Convolutional Neural Network (CNN) and Transformer-based architectures, for the task of vascular bundle density classification. The experimental results demonstrate that the ConvNeXt model achieved superior performance, attaining a classification accuracy of 99%. This research provides an effective and automated technological solution for bamboo strip density control and highlights the significant potential of applying deep learning to enhance the precision of bamboo quality assessment.