Optimizing AlexNet for accurate tree species classification via multi-branch architecture and mixed-domain attention

Accurate identification of tree species is essential for effective forestry management and conservation. Simple deep-learning models, such as AlexNet and VGG16, often struggle with fine-grained texture extraction and feature distinction, especially in complex environments. While more advanced models, such as ResNet34 and deeper architectures, offer superior feature extraction capabilities, they come with the trade-off of significantly longer training times and higher computational costs. To address these challenges in tree species classification, an optimized AlexNet architecture, MMCAlexNet, is proposed to address these challenges for tree species classification. The model integrates a multi-branch convolutional module, a mixeddomain attention module, and a joint loss function to improve feature extraction and class separation. The multi-branch convolutional module extracts diverse features by processing input with branches of different kernel sizes, capturing both fine and global details. The mixeddomain attention module enhances feature representation by focusing on critical spatial and channel-wise features. The joint loss function ensures better class separation and prevents overfitting by balancing classification accuracy and feature consistency. Experimental results demonstrate that MMCAlexNet outperforms the baseline AlexNet by 7.61%–9.69% in classification accuracy. Although the introduction of optimized structures increases computational complexity, it reduces the model size by 12MB. Furthermore, MMCAlexNet decreases the model size by 306.24MB and improves accuracy by 3.94%–6.35% compared to VGG16, demonstrating a balance between improved accuracy and computational efficiency.