Wang Yufan, Lu Fuhao, Huo Changfu
School of Computer Science and Technology, Tongji University, Shanghai 201804, China.
CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China.
Sensors (Basel). 2025 Aug 11;25(16):4956. doi: 10.3390/s25164956.
Accurate segmentation of fine roots in field rhizotron imagery is essential for high-throughput root system analysis but remains challenging due to limitations of traditional methods. Traditional methods for root quantification (e.g., soil coring, manual counting) are labor-intensive, subjective, and low-throughput. These limitations are exacerbated in in situ rhizotron imaging, where variable field conditions introduce noise and complex soil backgrounds. To address these challenges, this study develops an advanced deep learning framework for automated segmentation. We propose an improved U-shaped Convolutional Neural Network (U-Net) architecture optimized for segmenting larch () fine roots under heterogeneous field conditions, integrating both in situ rhizotron imagery and open-source multi-species minirhizotron datasets. Our approach integrates (1) a Convolutional Block Attention Module (CBAM) to enhance feature representation for fine-root detection; (2) an additive feature fusion strategy (UpAdd) during decoding to preserve morphological details, particularly in low-contrast regions; and (3) a transfer learning protocol to enable robust cross-species generalization. Our model achieves state-of-the-art performance with a mean intersection over union (mIoU) of 70.18%, mean Recall of 86.72%, and mean Precision of 75.89%-significantly outperforming PSPNet, SegNet, and DeepLabV3+ by 13.61%, 13.96%, and 13.27% in mIoU, respectively. Transfer learning further elevates root-specific metrics, yielding absolute gains of +0.47% IoU, +0.59% Precision, and +0.35% F1-score. The improved U-Net segmentation demonstrated strong agreement with the manual method for quantifying fine-root length, particularly for third-order roots, though optimization of lower-order root identification is required to enhance overall accuracy. This work provides a scalable approach for advancing automated root phenotyping and belowground ecological research.
在田间根窗图像中准确分割细根对于高通量根系分析至关重要，但由于传统方法的局限性，这仍然具有挑战性。传统的根系量化方法（如土壤取芯、人工计数）劳动强度大、主观性强且通量低。在原位根窗成像中，这些局限性更加突出，因为可变的田间条件会引入噪声和复杂的土壤背景。为了应对这些挑战，本研究开发了一种先进的深度学习框架用于自动分割。我们提出了一种改进的U型卷积神经网络（U-Net）架构，该架构针对在异质田间条件下分割落叶松细根进行了优化，整合了原位根窗图像和开源多物种微型根窗数据集。我们的方法整合了：（1）卷积块注意力模块（CBAM）以增强细根检测的特征表示；（2）在解码过程中采用加法特征融合策略（UpAdd）以保留形态细节，特别是在低对比度区域；（3）一种迁移学习协议以实现强大的跨物种泛化。我们的模型实现了70.18%的平均交并比（mIoU）、86.72%的平均召回率和75.89%的平均精度，达到了当前的最佳性能——在mIoU方面分别比PSPNet、SegNet和DeepLabV3+显著高出13.61%、13.96%和13.27%。迁移学习进一步提升了根系特定指标，IoU绝对增益为+0.47%，精度为+0.59%，F1分数为+0.35%。改进后的U-Net分割在细根长度量化方面与人工方法显示出很强的一致性，特别是对于三阶根，不过需要优化低阶根识别以提高整体准确性。这项工作为推进自动根系表型分析和地下生态研究提供了一种可扩展的方法。
