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使用狮子优化算法的深度功能识别麦昆虫.

M A Elmagzoub1, Wahidur Rahman2,3, Kaniz Roksana2

  • 1Department of Network and Communication Engineering, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia.

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概括
此摘要是机器生成的。

使用先进的人工智能早期检测水昆虫可以防止大米收获的重大损失. 这项研究将深度学习和机器学习与功能优化集成在一起,以准确识别害虫.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.线性差异分析线性差异分析狮子优化算法 狮子优化算法机器学习 机器学习病虫害鉴定 病虫害鉴定 病虫害鉴定主要组件分析的主要组件分析.

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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:

  • 病虫害导致全球大米产量大幅下降,估计为20%.
  • 早期发现米昆虫对于减轻这些经济影响至关重要.
  • 现有的昆虫识别系统缺乏集成的功能优化与深度学习和机器学习.

研究的目的:

  • 开发一个框架,使用先进的人工智能技术快速检测和分类大米昆虫.
  • 通过预处理和特征选择来增强田昆虫图像数据集.
  • 为了提高在农业领域的水昆虫诊断的准确性和效率.

主要方法:

  • 收集和分类一个米昆虫图像数据集.
  • 应用预处理技术,如增强和图像过.
  • 使用5个预训练的卷积神经网络模型来提取特征.
  • 实施特征选择方法:主要组件分析 (PCA),递归特征消除 (RFE),线性差异分析 (LDA) 和狮子优化.
  • 采用7个机器学习算法来识别昆虫.

主要成果:

  • 拟议的框架成功地从图像中检测和分类田昆虫.
  • 使用ResNet50结合后勤回归和PCA提取的特征向量实现了最高的准确率99.28%.
  • 功能优化技术的整合显著改善了诊断能力.

结论:

  • 开发的AI框架为米昆虫诊断提供了高度准确和高效的解决方案.
  • 这种方法有可能显著减少在米种植中的作物损失.
  • 这项研究强调了将深度学习,机器学习和功能优化结合起来对于农业害虫管理的重要性.