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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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PriBeL-Net:扩展贝特叶数据集,使用基于CNN的图像分类.

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概括

对精准农业的深度学习框架进行了评估. 在现实现场条件下,DenseNet121表现最好,使其成为农业应用的可靠选择.

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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
  • 机器学习 机器学习

背景情况:

  • 深度学习对于推进精准农业至关重要.
  • 评估深度学习框架对于优化农业技术至关重要.

研究的目的:

  • 为了比较四个深度学习框架 (MobileNetV2,EfficientNetB0,ResNet50V2,DenseNet121) 的性能.
  • 确定在受控和现场条件下最适合精密农业应用的框架.

主要方法:

  • 一个定制的数据集被用来评估MobileNetV2,EfficientNetB0,ResNet50V2和DenseNet121.一个定制的数据集.
  • 框架在受控的实验室环境和现实世界的现场环境中进行了测试.

主要成果:

  • 移动NetV2和ResNet50V2在受控环境中表现最好,显示出对变化的稳定性.
  • 在现场环境中,DenseNet121实现了卓越的准确性和F1得分.
  • 效率NetB0表现不佳,突出了噪音数据集中的轻量级模型的局限性.

结论:

  • DenseNet121被认为是农业应用中最可靠的深度学习模型.
  • 未来的工作重点是调整DenseNet121以在各种农业条件下提高性能.