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玉米种子的品种识别基于高光谱成像和残留mamba 1D CNN

Guoqing Qi¹, Chengzhong Liu¹, Junying Han¹

¹College of Information Sciences and Technology, Gansu Agricultural University, Lanzhou 730070, China.

Foods (Basel, Switzerland)

|October 29, 2025

概括

这项研究引入了一种新的AI模型,用于使用高光谱成像识别玉米种子品种. 剩余马巴一维卷积神经网络 (RM1DNet) 实现了高精度,改善了作物育种和质量控制.

科学领域:

农业科学农业科学
计算机科学计算机科学
数据科学数据科学数据科学

背景情况:

准确的玉米种子品种识别对于农业,育种和质量控制至关重要.
传统的种子识别方法在速度和准确性方面存在局限性.
超光谱成像 (HSI) 为非破坏性分析提供丰富的光谱信息.

研究的目的:

开发一种用于玉米种子品种识别的非破坏性方法.
将高光谱成像 (HSI) 与先进的深度学习技术相结合.
提出和评估一种新的深度学习模型,以增强功能学习.

主要方法:

开发了一个残余的Mamba一维卷积神经网络 (RM1DNet).
RM1DNet模型集成了残留和Mamba模块,以改进特征提取.
20种玉米种子品种的高光谱图像用于培训和测试.

主要成果:

在20种玉米种子品种中,RM1DNet实现了94.85%的分类准确度.
拟议的RM1DNet模型的表现优于传统的分类器和基线深度学习模型.
该研究证明了该模型在种子识别方面的稳定性和效率.

结论:

开发的RM1DNet模型显示了智能玉米种子品种识别的巨大潜力.
这种方法为传统方法提供了一个非破坏性的,准确和高效的替代方案.
综合HSI和RM1DNet将推动农业数据分析和质量保证领域的发展.

关键词:

种植作物的表型化.非破坏性测试是指非破坏性测试.剩余的神经网络剩余的神经网络种子的分类种子的分类.频谱分析是一种分析.

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