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基于注意力的卷积神经网络使用挤压和激发机制进行了增强的植物疾病分类

S Karthikeyan¹, R Charan¹, Sathiya Narayanan¹

¹School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.

Frontiers in artificial intelligence

|August 28, 2025

概括

新的深度学习模型CNN-SEEIB使用注意力机制准确地分类植物疾病. 这种高效的模型适用于边缘设备的实时农业应用.

科学领域:

农业技术
深度学习
计算机视觉

背景情况:

智能设备和边缘计算对现代农业至关重要,通过自动化和实时分析提高生产力.
目前的深度学习模型在农业多标签分类任务的准确性,规模和速度方面面临挑战.

研究的目的:

为多标签植物疾病分类提出一个高效的深度学习架构.
使用注意力机制提高分类性能和计算效率.

主要方法:

开发了一种混合卷积神经网络,使用挤压和激发启用身份块 (CNN-SEEIB).
将注意力机制纳入身份块以增强视觉注意力.
使用PlantVillage数据集 (54,305张图像,38个类) 进行培训和验证.

主要成果:

在PlantVillage数据集上获得了99.79%的准确性,0.9970的精度,0.9972的回忆力和0.9971的F1得分.
每张图像的推断时间为64毫秒,证明其适合实时部署.
在土豆叶病数据集上验证,达到97.77%的准确性.

结论:

在多标签植物疾病分类中,CNN-SEEIB模型具有很高的准确性和效率.
注意力机制提高了性能和计算效率,使其成为农业边缘计算的理想选择.
该模型的性能验证了其在智能农业应用中实时检测疾病的潜力.

关键词:

注意力机制卷积神经网络深度学习边缘计算设备植物疾病分类

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