Yuan Xiaoli, Zhou Guoxiong, Yan Yongming, Yan Xuewu
Institute of Artificial Intelligence Application, Central South University of Forestry and Technology, Changsha 410004, China.
Hunan Academy of Forestry, Changsha 410018, China.
Plants (Basel). 2025 Aug 11;14(16):2490. doi: 10.3390/plants14162490.
The pine wood nematode is responsible for pine wilt disease, which poses a significant threat to forest ecosystems worldwide. If not quickly detected and removed, the disease spreads rapidly. Advancements in UAV and image detection technologies are crucial for disease monitoring, enabling efficient and automated identification of pine wilt disease. However, challenges persist in the detection of pine wilt disease, including complex UAV imagery backgrounds, difficulty extracting subtle features, and prediction frame bias. In this study, we develop a specialized UAV remote sensing pine forest ARen dataset and introduce a novel pine wilt disease detection model, SLMW-Net. Firstly, the Self-Learning Feature Extraction Module (SFEM) is proposed, combining a convolutional operation and a learnable normalization layer, which effectively solves the problem of difficult feature extraction from pine trees in complex backgrounds and reduces the interference of irrelevant regions. Secondly, the MicroFeature Attention Mechanism (MFAM) is designed to enhance the capture of tiny features of pine trees infected by initial nematode diseases by combining Grouped Attention and Gated Feed-Forward. Then, Weighted and Linearly Scaled IoU Loss (WLIoU Loss) is introduced, which combines weight adjustment and linear stretch truncation to improve the learning strategy, enhance the model performance and generalization ability. SLMW-Net is trained on the self-built ARen dataset and compared with seven existing methods. The experimental results show that SLMW-Net outperforms all other methods, achieving an mAP@0.5 of 86.7% and an mAP@0.5:0.95 of 40.1%. Compared to the backbone model, the mAP@0.5 increased from 83.9% to 86.7%. Therefore, the proposed SLMW-Net has demonstrated strong capabilities to address three major challenges related to pine wilt disease detection, helping to protect forest health and maintain ecological balance.
松材线虫是导致松材线虫病的病原体，对全球森林生态系统构成重大威胁。如果不迅速检测和清除，该病会迅速传播。无人机和图像检测技术的进步对于疾病监测至关重要，能够高效、自动地识别松材线虫病。然而，松材线虫病的检测仍存在挑战，包括无人机图像背景复杂、难以提取细微特征以及预测框偏差等问题。在本研究中，我们开发了一个专门的无人机遥感松林ARen数据集，并引入了一种新颖的松材线虫病检测模型SLMW-Net。首先，提出了自学习特征提取模块（SFEM），它结合了卷积操作和可学习的归一化层，有效解决了在复杂背景下从松树中提取特征困难的问题，并减少了无关区域的干扰。其次，设计了微特征注意力机制（MFAM），通过结合分组注意力和门控前馈来增强对初始线虫病害感染松树微小特征的捕捉。然后，引入了加权和线性缩放交并比损失（WLIoU Loss），它结合了权重调整和线性拉伸截断来改进学习策略，提高模型性能和泛化能力。SLMW-Net在自建的ARen数据集上进行训练，并与七种现有方法进行比较。实验结果表明，SLMW-Net优于所有其他方法，mAP@0.5达到86.7%，mAP@0.5:0.95达到40.1%。与骨干模型相比，mAP@0.5从83.9%提高到86.7%。因此，所提出的SLMW-Net已展示出强大的能力来应对与松材线虫病检测相关的三大挑战，有助于保护森林健康和维持生态平衡。
