Accurate roundwood sorting is critical for improving resource efficiency in the timber industry. However, in many small sawmills, sorting is performed manually through visual inspection, often resulting in inconsistencies and misclassifications. Sorting wood based on macroscopic images using convolutional neural networks (CNN) is a cost-effective and efficient approach. However, current models focus primarily on cross-sectional images, limiting their ability to detect important features like knots and beetle infestations. We present an industrial dataset of 5,200 samples, each including both a trunk and a cross-sectional image. Using this dataset, we developed two models: a stem quality classification model and a beetle-indicator detection model. The first model uses only trunk images to extract stem information, supporting existing cross-sectional models. The beetle-indicator detection model utilizes both trunk and cross-sectional images, introducing a new detection capability to computer vision-based roundwood sorting. To ensure transparency and interpretability, we applied Explainable Artificial Intelligence (XAI) techniques to highlight key regions within the images that influenced the models’ predictions. The stem quality model achieves 80% accuracy in trunk feature analysis, and the beetle-indicator model reaches 89% accuracy. Together, these models improve automation based on convolutional neural networks by enabling detailed trunk analysis and early beetle infestation detection.