植物疾病识别的本地和全球特征感知双分支网络
Jianwu Lin1,2,3,4, Xin Zhang1,2,3,4, Yongbin Qin1,2
1Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.
Plant phenomics (Washington, D.C.)
|August 12, 2024
概括
本研究介绍了LGNet,这是一个新的双分支网络,将卷积神经网络 (CNN) 和视觉转换器 (VT) 结合起来,用于高级植物疾病识别. LGNet有效地融合了本地和全球特征,在识别植物疾病方面实现了最先进的准确性.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 准确的植物疾病鉴定对于农业安全至关重要.
- 卷积神经网络 (CNN) 在本地特征提取方面表现出色,而视觉转换器 (VTs) 在全球特征提取方面表现强.
- 现有的模型很难有效地将本地和全球感知结合起来,以识别植物疾病.
研究的目的:
- 开发一种先进的深度学习模型,用于准确识别植物疾病.
- 克服个人CNN和VTs在捕捉本地和全球图像特征方面的局限性.
- 提高植物疾病识别系统的性能和稳定性.
主要方法:
- 提出了一个双分支网络 (LGNet),将CNN集成为本地功能和VTs集成为全球功能.
- 引入了一个自适应特征融合 (AFF) 模块,用于对本地和全球特征进行动态加权.
- 开发了一种层次混合尺度单元引导特征融合 (HMUFF) 模块,以增强多尺度感知.
主要成果:
- 在AI挑战者2018数据集上,LGNet以88.74%的准确度实现了最先进的性能.
- 在自收集的玉米病 (SCD) 数据集上,LGNet表现出卓越的识别准确性,达到99.08%.
- 拟议的融合模块有效地提高了模型在多个尺度上感知和整合特征的能力.
结论:
- 拟议的LGNet通过整合它们的互补优势,有效地解决了CNN和VT的局限性.
- 对于智能植物疾病识别,LGNet提供了一个强大而准确的解决方案.
- 该模型增强的多尺度感知能力是其在各种数据集中的高性能的关键.
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