SE-VisionTransformer:基于注意力机制的混合网络用于诊断甘叶病
Cuimin Sun1, Xingzhi Zhou1, Menghua Zhang1
1School of Computer and Electronic Information Engineering, Guangxi University, Nanning 530004, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究介绍了SE-VIT混合网络用于甘疾病的识别,使用先进的注意力机制提高了5%以上的准确性. 这种人工智能模型为管理植物疾病提供了一种新的方法,尤其是在有限的数据的情况下.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 生物技术是生物技术.
背景情况:
- 甘疾病对国民经济构成重大威胁.
- 准确和早期识别甘叶病对于有效管理至关重要.
研究的目的:
- 开发一种先进的混合神经网络,用于准确识别甘叶病.
- 为了比较不同的图像细分算法来提取叶子病变.
主要方法:
- 支持矢量机 (SVM) 用于精确的叶子病变细分.
- SE注意模块被集成到ResNet-18 (CNN) 中,以增强特征学习.
- 多头自我注意 (MHSA) 和2D相对位置编码被纳入了SE-VIT混合网络.
主要成果:
- 在PlantVillage数据集上,SE-VIT混合网络实现了97.26%的准确性.
- 观察到精度 (5.1%),精度 (3.23%) 和回忆 (5.17%) 的显著改善.
- SE-VIT的性能优于四种经典的神经网络模型,在比较测试中达到89.57%的准确性.
结论:
- 拟议的SE-VIT模型为智能甘种植园管理提供了一个强大的解决方案.
- 这项研究为应用人工智能用于植物疾病检测提供了有价值的见解,特别是在有限的数据集下.
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