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无症状花生叶病的光谱图像分类基于深度学习算法

Laixiang Xu¹, Xinjia Chen¹, Peng Xu²

¹School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan, 467036, China.

|December 21, 2025

概括

这项研究引入了一种新的多谱成像系统和深度学习模型,用于早期检测花生叶病. 该系统在识别无症状疾病时达到98.45%的准确性,提高了作物产量和质量.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
植物病理学植物病理学

背景情况:

花生叶病显著降低作物产量和质量.
早期和准确的诊断对于有效的疾病管理至关重要.
现有的诊断方法可能缺乏速度和精度.

研究的目的:

开发一种先进的系统,用于早期诊断花生叶病.
将多谱成像与深度学习相结合,以提高检测能力.
为了提高花生疾病分类的准确性和稳定性.

主要方法:

设计了一种硬件系统,用于花生叶的多光谱反射和光成像.
收集了无症状花生叶病的多谱图像 (,烧斑,炭).
开发了一个深度学习模型,使用卷积神经网络,适应性道注意力和稀疏的二阶注意力机制进行分类.

主要成果:

在无症状的花生叶病中达到98.45%的高分类准确度.
证明光谱图像信息增强了模型对数据转换的稳定性.
拟议的系统在检测能力和分类准确性方面明显优于传统方法.

结论:

综合多谱成像和深度学习方法为精确地诊断花生疾病提供了强大的工具.
这项技术可以帮助植物学家更快,更准确地进行诊断.
这些发现有助于确保高质量的花生生产和产量.

关键词:

无症状的花生叶没有症状.深度学习是一种深度学习.多光谱反射和光图像的多光谱反射和光图像.频谱传感感应的使用.

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