Research on eucalyptus individual tree segmentation and age estimation utilizing improved Mask R-CNN algorithm based on UAV stereo images

作者：Jirong Ding, Liyang You, Yehua Liang, Juncheng Huang, Zhiyong Wu, Runlian Huang, Jianjun Chen, Haotian You · 发表于：Industrial Crops and Products · 年份：2025 · DOI：10.1016/j.indcrop.2025.121073 · 被引用次数：10 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing and Land Use、Remote Sensing in Agriculture

Eucalyptus, as one of the world's three major fast-growing tree species, not only provides timber for various industries but also makes significant contributions to carbon sequestration and mitigating climate change. However, due to the rapid growth and diverse variations in eucalyptus, accurate individual tree segmentation poses certain challenges. Additionally, achieving individual tree segmentation while extracting the age of eucalyptus plantations presents greater challenges to segmentation algorithms. Based on this, this study utilizes the Mask R-CNN algorithm framework. Firstly, Transnext is employed to enhance the backbone's performance in extracting image texture and spatial information. Secondly, CARAFEFPN replaces the original FPN structure to increase the receptive field, thereby improving the model's detection performance in dense eucalyptus plantations. Subsequently, Seesawloss is used as the loss function, and SABLHead serves as the detection head to address sample imbalance and enhance model robustness. Finally, the improved Mask R-CNN algorithm is utilized for individual tree segmentation and stand age extraction in eucalyptus plantations. The results indicate that the improved Mask R-CNN model achieves precise individual tree segmentation in different stages of eucalyptus plantations, achieving MAP50 of 80.6 %, MAP75 of 54.2 %, and MAP0.5:0.95 of 48.9 %, all surpassing the Mask R-CNN model. Based on the improved Mask R-CNN model, the SHAI algorithm enables ac...