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HCAR-AM地坚果叶网:基于混合卷积的自适应ResNet,具有检测地坚果叶病的注意力机制,具有自适应细分的注意力机制

Annamalai Thiruvengadam Madhavi¹, Kamal Basha Rahimunnisa¹

¹Department of ECE, Easwari Engineering College, Chennai, India.

Network (Bristol, England)

|November 18, 2024

概括

一个新的深度学习模型有效地通过先进的特征提取和混合优化来识别花生叶病. 这种方法提高了大型数据集的系统功能,提高了疾病检测的准确性.

科学领域:

农业科学农业科学
计算机科学计算机科学
人工智能的人工智能

背景情况:

从大型数据集中估计最佳答案在计算上是昂贵的,并且可以降低系统的功能.
准确识别地叶疾病对于作物管理和产量优化至关重要.

研究的目的:

开发和实施一种新的深度学习模型,以有效地识别花生叶乱.
解决计算成本和系统功能在植物病理学大规模图像分析的挑战.

主要方法:

一个两阶段的特征提取过程,涉及自适应的TransResunet++细分和Kaze特征点与二进制描述符.
两个不同的特征集的连接,以增强分类模型的输入.
使用基于混合卷积的自适应复网与注意力机制 (HCAR-AM) 进行疾病检测,参数通过白和鱼 (HP-BWCF) 的混合位置算法进行优化.

主要成果:

拟议的模型在检测花生叶病方面表现出高效率.
实验分析显示,与现有的地面花生叶病检测方法相比,其性能具有竞争力.
混合优化技术 (HP-BWCF) 有效地调整HCAR-AM模型的参数.

结论:

开发的深度学习模型提供了有效的解决方案,用于识别花生叶乱,即使使用大型数据集.
先进的细分,特征提取和混合优化的结合显著提高了检测准确性.
这项研究为农业中自动化植物疾病诊断提供了强大的方法.

关键词:

检测地坚果叶病检测地坚果叶病适应式的TranResunet++可以调整.功能提取特性提取基于混合卷积的自适应重置与注意力机制.白和鱼的混合位置.预处理预处理

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