Wu Qiangjia, Chen Meixiang, Shi Hao, Yi Tongchuan, Xu Gang, Wang Weijia, Zhang Ruirui
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
College of Information Engineering, Northwest A&F University, Yangling, 712100, China.
Plant Methods. 2025 May 23;21(1):68. doi: 10.1186/s13007-025-01385-6.
Pine wood nematode (PWN), a major international quarantined forest pest, has resulted in significant loss to the pine forest resources, posing a serious threat to global forest ecosystems. Quick and accurate identification of trees infected by PWN can lead to earlier intervention in their spread, thereby significantly reducing losses. However, there is a scarcity of algorithm that are both swift and precise. To achieve more rapid and precise segmentation of trees affected by PWN, we proposed a novel lightweight model termed Refined and Deformable Carafe Attention Net (RCANet). The RCANet excels in both accuracy and real-time performance. It has achieved segmentation accuracy that surpasses mainstream segmentation networks, including DeepLabv3 + , Segformer, PSPNet, HrNet, and UNet. The number of parameters in RCANet is only 5.373 million, the segmentation speed reached 83.14 fps. Compared to the baseline model UNet, the IoU of the affected trees class is improved by 5.6%, and the segmentation speed is accelerated by about 90%. A straightforward yet highly effective lightweight structure was proposed, termed Refined VGG. Additionally, we validate the efficacy of several network modules for this task. RCANet addressed the challenges of low accuracy and inadequate real-time capabilities in the identification of PWN-affected pine trees within intricate forest landscapes. which is expected to be deployed on UAVs in the future for real-time recognition to accelerate the identification and localization of affected trees. This work could facilitate the management of PWN.
松材线虫（PWN）是一种主要的国际检疫性森林害虫，已给松林资源造成重大损失，对全球森林生态系统构成严重威胁。快速准确地识别受松材线虫感染的树木可以更早地干预其传播，从而显著减少损失。然而，缺乏既快速又精确的算法。为了更快速、精确地分割受松材线虫影响的树木，我们提出了一种新颖的轻量级模型，称为精炼可变形卡拉菲注意力网络（RCANet）。RCANet在准确性和实时性能方面都表现出色。它实现的分割精度超过了主流分割网络，包括DeepLabv3 +、Segformer、PSPNet、HrNet和UNet。RCANet的参数数量仅为537.3万，分割速度达到83.14帧/秒。与基线模型UNet相比，受影响树木类别的交并比（IoU）提高了5.6%，分割速度加快了约90%。我们提出了一种简单而高效的轻量级结构，称为精炼VGG。此外，我们验证了几个网络模块在此任务中的有效性。RCANet解决了在复杂森林景观中识别受松材线虫影响的松树时准确性低和实时能力不足的挑战，有望未来部署在无人机上进行实时识别，以加速受影响树木的识别和定位。这项工作有助于松材线虫的管理。
