A tree species classification model based on improved YOLOv7 for shelterbelts - Liu, Yihao; Zhao, Qingzhan; Wang, Xuewen; Sheng, Yuhao; Tian, Wenzhong; Ren, Yuanyuan (Frontiers in Plant Science, 2024)
Tree species classification within shelterbelts is crucial for shelterbelt management. The large-scale satellite-based and low-altitude drone-based approaches serve as powerful tools for forest monitoring, especially in tree species classification. However, these methods face challenges in distinguishing individual tree species within complex backgrounds. Additionally, the mixed growth of trees within protective forest suffers from similar crown size among different tree species. The complex background of the shelterbelts negatively impacts the accuracy of tree species classification. The You Only Look Once (YOLO) algorithm is widely used in the field of agriculture and forestry, ie., plant and fruit identification, pest and disease detection, and tree species classification in forestry. We proposed a YOLOv7-Kmeans++_CoordConv_CBAM (YOLOv7-KCC) model for tree species classification based on drone RGB remote sensing images. Firstly, we constructed a dataset for tree species in shelterbelts and adopted data augmentation methods to mitigate overfitting due to limited training data. Secondly, the K-means++ algorithm was employed to cluster anchor boxes in the dataset. Furthermore, to enhance the YOLOv7 backbone network’s Efficient Layer Aggregation Network (ELAN) module, we used Coordinate Convolution (CoordConv) replaced the ordinary 1×1 convolution. The Convolutional Block Attention Module (CBAM) was integrated into the Path Aggregation Network (PANet) structure to facilitate multiscale feature extraction and fusion, allowing the network to better capture and utilize crucial feature information. Experimental results showed that the YOLOv7-KCC model achieves a mean average precision@0.5 of 98.91%, outperforming the Faster RCNN-VGG16, Faster RCNN-Resnet50, SSD, YOLOv4, and YOLOv7 models by 5.71%, 11.75%, 5.97%, 7.86%, and 3.69%, respectively. The GFlops and Parameter values of the YOLOv7-KCC model stand at 105.07G and 143.7MB, representing an almost 5.6% increase in F1 metrics compared to YOLOv7. Therefore, the proposed YOLOv7-KCC model can effectively classify shelterbelt tree species, providing a scientific theoretical basis for shelterbelt management in Northwest China focusing on Xinjiang.