ConvTransNet-S:用于复杂的现场环境的CNN-变压器混合疾病识别模型
Shangyun Jia1, Guanping Wang1, Hongling Li1
1College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China.
Plants (Basel, Switzerland)
|August 14, 2025
概括
一个新的混合模型,ConvTransNet-S,通过使用集成的卷积神经网络 (CNN) 和变压器,提高了作物疾病识别的准确性. 该模型在复杂的野外环境中增强了特征提取和强度,为智能农业提供了显著的价值.
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:
- 在现实世界现场条件下,作物疾病识别面临着低准确度和高模型复杂性的挑战.
- 现有的模型难以平衡详细的特征提取与全球上下文理解,特别是在环境变化 (如照明和遮蔽) 下.
研究的目的:
- 开发一种新的混合模型,ConvTransNet-S,用于准确有效地识别作物疾病.
- 提高复杂的田间环境中的作物疾病识别模型的稳定性和降低复杂性.
主要方法:
- 提出了ConvTransNet-S,这是一个融合卷积神经网络 (CNN) 和变压器的混合模型.
- 引入了局部感知单元 (LPU) 用于细粒度的细节和轻量级多头自我注意 (LMHSA) 用于全球依赖.
- 采用反向剩余输送网络 (IRFFN) 优化特征传播和阶段架构,用于多尺度特征融合.
主要成果:
- 在2514万个参数的PlantVillage数据集上实现了98.85%的准确性.
- 在一个自建的现场数据集上,ConvTransNet-S达到88.53%的准确率,超过了EfficientNetV2,Vision Transformer和Swin Transformer.
- 与其他模型相比,在复杂的背景条件下显著改善 (高达14.22%),参数数量减少了46.8%.
结论:
- ConvTransNet-S有效地平衡了本地纹理感知和全球上下文建模,克服了独立CNN或变压器的局限性.
- 该模型的多尺度特征机制成功地将疾病与复杂农业场景中的背景特征区分开来.
- ConvTransNet-S为疾病诊断提供了有前途的技术方法,显示出智能农业管理的实质性应用价值.
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