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图:使用AFNO的混合变压器-CNN架构,用于准确的植物叶病图像识别

Bo Hu¹, Wenqian Jiang², Juan Zeng³

¹School of Information Engineering, Nanchang University, Nanchang, China.

Frontiers in plant science

|September 29, 2023

概括

这项研究介绍了FOTCA,一种用于识别植物叶病的混合AI模型. FOTCA实现了高精度 (99.8%) 和F1得分 (0.9931),改进了传统方法和变压器模型以有效识别疾病.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

植物病严重影响作物产量和环境.
手动识别疾病是劳动密集型的,不准确的,昂贵的.
计算机视觉为植物疾病检测提供了一个高效和准确的替代方案.

研究的目的:

开发一种先进的AI模型,用于准确识别植物叶病.
在较小的数据集上解决现有变压器模型的局限性.
提高植物疾病识别系统的效率和准确性.

主要方法:

提出了一种新的混合架构,FOTCA,它结合了适应福里埃神经运算符 (AFNO) 进行全球特征提取和卷积内核进行本地特征提取.
利用转移学习来增强对分布外 (OOD) 样本的模型概括.
集成的焦点损失,以提高模型的融合和识别准确性.

主要成果:

该FOTCA模型实现了99.8%的平均识别精度和0.9931.1的F1得分.
与传统模型相比,废弃实验显示了与传统模型相比,收速度和精度的显著改善.
该模型在OOD样本上显示出强大的概括能力.

结论:

福特卡模型在植物叶病的图像识别方面表现出卓越的性能.
混合架构和转移学习方法有效地克服了标准变压器模型的局限性.
高精度和高效率使FOTCA适合在农业中实际部署.

关键词:

适应性里埃神经运算子深度学习是一种深度学习.混合架构架构是混合架构的架构.植物叶病图像识别图像识别基于变压器的模型

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