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相关实验视频

Updated: Sep 17, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

使用综合CNN-ViT模型进行了增强的玉米叶病检测和分类.

Gunjan Shandilya¹, Sheifali Gupta¹, Heba G Mohamed²

¹Chitkara University Institute of Engineering and Technology Chitkara University Punjab India.

Food science & nutrition

|July 2, 2025

概括

此摘要是机器生成的。

结合卷积神经网络 (CNN) 和视觉转换器 (ViT) 的新混合深度学习模型可以准确检测玉米叶病. 这种先进的方法通过提供早期和可靠的疾病识别来改善作物管理.

关键词:

在CD&S数据集中,卷积神经网络 (CNN) 是一种神经网络.深度学习 (DL) 是指深度学习.这是一种混合CNN-ViT.玉米叶病是一种玉米叶病.植物疾病分类植物疾病分类植物村数据集植物村数据集视觉变压器 (ViT) 是一个视觉变压器.

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相关实验视频

Last Updated: Sep 17, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

Author Spotlight: Investigating Fungal Pathogenicity Mechanisms in Maize

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Published on: September 15, 2023

Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

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Published on: September 22, 2023

科学领域:

农业科学农业科学
计算机科学计算机科学
植物病理学植物病理学

背景情况:

玉米作物的生产力受到叶病的威胁,需要有效的检测方法.
传统的分类技术与受疾病影响的叶子图像的复杂性作斗争.
自动检测疾病对于及时干预和优化作物管理至关重要.

研究的目的:

开发一个强大的混合深度学习框架,用于增强玉米叶病症分类.
克服传统方法在捕捉复杂的视觉模式的局限性.
提高自动化玉米疾病检测的准确性和可靠性.

主要方法:

一个混合深度学习框架,集成卷积神经网络 (CNN) 进行局部特征提取和视觉转换器 (ViT) 进行上下文依赖.
在ViT模块中利用自我注意机制来捕捉远程关系.
连接CNN和ViT模块的特征,然后是完全连接的分类层.

主要成果:

拟议的混合CNN-ViT模型实现了99.15%的验证准确度,精度,回忆和F1得分为99.13%.
五倍交叉验证证明了高平均准确率:Kaggle + Mendeley数据集的平均准确率为99.06%,玉米疾病和严重性 (CD&S) 数据集的平均准确率为95.93%.
混合型号的表现优于独立的CNN,表明性能优越和通用化能力优越.

结论:

混合CNN-ViT模型为玉米叶病的识别提供了可靠和高度准确的解决方案.
该框架在多个数据集中的有效性突出显示了其对现实世界农业应用的潜力.
由于失学规范化和RAdam优化器,稳定性和性能得到了改善.