结合卷积和视觉变压器的双分支模型用于作物疾病分类
Qingduan Meng1, Jiadong Guo1, Hui Zhang1
1College of Information Engineering, Henan University of Science and Technology, Luoyang, Henan, China.
PloS one
|April 24, 2025
概括
本研究引入了一种结合卷积神经网络 (CNN) 和视觉转换器 (ViT) 的新型双分支模型,用于准确的作物疾病分类. 轻量级模型在较少的参数下实现了高精度,为植物疾病识别提供了有效的解决方案.
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:
- 农作物疾病分类对于粮食安全至关重要,但由于复杂的视觉特征而具有挑战性.
- 现有的计算机视觉模型经常与植物疾病的复杂纹理和形状作斗争.
- 卷积神经网络 (CNN) 和视觉转换器 (ViT) 的集成为改进分类提供了一个有希望的途径.
研究的目的:
- 开发一种轻量级且高度准确的双分支模型用于作物疾病分类.
- 有效地将本地特征提取 (CNN) 与全球特征理解 (ViT) 结合起来.
- 提高农业应用的变压器模型的表示能力.
主要方法:
- 一个双分支架构,将CNN集成为本地功能和ViT集成为全球功能.
- 引入聚合局部感知料前向层 (ALP-FFN) 以增强变压器的局部性.
- 开发一个轻量级的变压器块与ALP-FFN和线性自我注意力减少复杂性.
主要成果:
- 在仅有490万个参数的PlantVillage数据集上实现了99.71%的准确性,超过了最先进的模型.
- 在土豆叶数据集上获得了98.78%的准确性,超过了ResNet-18.
- 在显著降低参数和计算成本的情况下,证明了卓越的性能.
结论:
- 拟议的双分支CNN-ViT模型有效地利用本地和全球特征来精确识别作物疾病.
- 采用ALP-FFN的轻量级设计为现实世界农业应用提供了高效的解决方案.
- 这种方法为自动作物疾病诊断提供了一种强大而可扩展的方法.
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