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遥感图像数据分析使用海洋捕食者算法与深度学习用于粮食作物分类.

Ahmed S Almasoud1, Hanan Abdullah Mengash2, Muhammad Kashif Saeed3

  • 1Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.

Biomimetics (Basel, Switzerland)
|November 24, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种使用海洋捕食者算法与深度学习的新方法,用于从遥感图像中对粮食作物进行分类. 该方法提高了识别作物类型的准确性,优于现有的深度学习技术.

关键词:
计算机视觉 计算机视觉农作物分类的作物分类方法深度学习是一种深度学习.机器学习是机器学习.遥感图像来自远程传感.

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科学领域:

  • 农业遥感 农业遥感
  • 人工智能在农业中的应用
  • 机器学习用于作物分类.

背景情况:

  • 来自无人机和卫星的遥感 (RS) 数据越来越多地用于农业应用,如作物分类.
  • 传统的分类方法与异质作物种植作斗争,需要先进的AI技术.
  • 深度学习 (DL) 为有效的作物类型检测提供了卓越的特征提取.

研究的目的:

  • 开发一种新的遥感图像数据分析技术,用于粮食作物分类.
  • 提高作物分类模型的准确性和概括性.
  • 调查海洋捕食者算法 (MPA) 与DL相结合的有效性.

主要方法:

  • 设计了远程传感海洋捕食者算法与深度学习食品作物分类 (RSMPA-DLFCC) 技术.
  • 使用SimAM-EfficientNet模型从RS图像中提取特征.
  • 使用MPA进行最佳的超参数选择,以增强SimAM-EfficientNet架构,并使用极端学习机器 (ELM)进行分类.

主要成果:

  • 该RSMPA-DLFCC技术有效地分析RS数据以确定粮食作物品种.
  • 优化MPA显著提高了SimAM-EfficientNet模型的分类准确性和概括性.
  • 在基准数据集上的模拟分析表明,与现有的DL技术相比,性能优越.

结论:

  • 拟议的RSMPA-DLFCC技术为使用遥感数据进行粮食作物分类提供了强大而准确的解决方案.
  • 将MPA与DL模型集成为优化农业分类任务提供了有效的策略.
  • 这种方法具有改善作物监测,产量估计和土地管理的巨大潜力.