改进的U形卷积神经网络,具有卷积块注意模块和特征融合,用于现场子图像中的细根的自动细分
Yufan Wang1, Fuhao Lu2,3, Changfu Huo2
1School of Computer Science and Technology, Tongji University, Shanghai 201804, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了一种先进的深度学习模型,用于在具有挑战性的现场条件下精确细分根,改进高吞吐量根系分析和地下生态研究.
科学领域:
- 植物科学
- 计算机科学
- 生态学
背景情况:
- 传统的根量化方法是劳动密集型和主观的.
- 由于噪音和复杂的背景,在现场基质子成像存在挑战.
- 精确的根细分对于高通量根系统分析至关重要.
研究的目的:
- 开发一个先进的深度学习框架,用于现场根基图像中的细根的自动细分.
- 提高根系分析的准确性和效率.
- 能够进行强大的跨物种概括,用于根系表型.
主要方法:
- 提出了改进的U形卷积神经网络 (U-Net) 架构.
- 集成了一个卷积块注意模块 (CBAM) 以增强特征表示.
- 采用添加特征融合策略 (UpAdd) 和转移学习.
主要成果:
- 最先进的性能达到70.18%,回忆率为86.72%,精度为75.89%.
- 在细分精度方面超过了PSPNet,SegNet和DeepLabV3+等现有模型.
- 转移学习表现出强大的跨物种概括和改善根特异性指标.
结论:
- 开发的深度学习框架提供了一种可扩展的自动根类型化方法.
- 该模型与手工量化有很强的一致性,特别是对于第三阶根.
- 这项工作促进了地下生态研究和高通量表型.
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