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基于改进的EfficientNet轻量级神经网络的玉米品种识别

Jinpu Xu^1,2, Jinhao Lan³, Guangjie Lv⁴

¹College of Animation and Communication, Qingdao Agricultural University, Qingdao, China.

Frontiers in plant science

|July 4, 2025

概括

这项研究介绍了SCD_EFTNet,这是一种用于识别玉米耳品种的AI模型. 这种深度学习方法增强了种子真实性选,提高了作物产量和市场价值.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
机器学习机器学习

背景情况:

玉米种子的真实性对于作物产量和经济价值至关重要.
对玉米耳进行有效选对于种子加工和知识产权保护至关重要.
目前用于玉米耳识别的方法可能缺乏效率和准确性.

研究的目的:

开发一种智能系统,使用深度学习来对玉米耳品种进行分类和识别.
提出一个改进的EfficientNet轻量化模型,以进行增强的玉米耳图像分析.
通过准确的识别来保护玉米品种的知识产权.

主要方法:

收集了来自五种品种的6529张玉米耳RGB图像的数据集.
通过修改EfficientNetB0开发了SCD_EFTNet模型,将CBAM注意力和扩展卷积纳入其中.
使用Swish激活功能来提高梯度转移稳定性.

主要成果:

在SCD_EFTNet模型中,SCD_EFTNet模型在召回,精度和mAP中表现出优于主流模型的性能.
在玉米耳的分类中获得了98.11%的平均平均精度 (mAP).
与现有模型相比,推断时间显著改善.

结论:

玉米耳的表型特征对于分类和识别不同品种是有效的.
拟议的SCD_EFTNet模型为智能玉米分类提供了可靠和高效的解决方案.
这项研究为自动化农业过程和质量控制提供了宝贵的参考资料.

关键词:

这就是为什么CBAM是CBAM.有效网B0 有效网B0 这是分类分类的分类.一个玉米耳.扩张的卷积扩张的卷积.品种识别品种识别

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