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Updated: Sep 13, 2025

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使用可解释的深度内在学习模型预测有毒植物.

Ahmed S Maklad1,2, Ashraf Alyanbaawi1, Mohammed Farsi1

  • 1College of Computer Science and Engineering, Taibah University, Yanbu 966144, Saudi Arabia.

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概括
此摘要是机器生成的。

本研究引入了一种可解释的深度内在学习方法,用于准确的植物物种分类和毒性状况预测. 该方法提高了对人工智能的信任,用于识别有害植物,减少中毒事件.

关键词:
在XAI,XAI就是XAI.深度内在的学习是内在的.解释技巧 解释技巧图像的分类图像的分类.可以解释的解释性.

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 植物学 植物学

背景情况:

  • 全球植物物种的发现在区分有益和有毒品种方面带来了挑战.
  • 目前用于植物分类和毒性预测的计算机视觉方法受到数据可用性的限制.
  • 缺乏全面的数据集阻碍了对有毒植物的准确识别和预测.

研究的目的:

  • 为植物物种分类和毒性状况预测提出一种可解释的深度内在学习方法.
  • 提高AI模型在识别有毒植物方面的准确性和可解释性.
  • 建立对人工智能驱动的植物识别和安全系统的信任.

主要方法:

  • 在深度内在学习框架内利用先进的计算机视觉技术.
  • 雇佣可解释的人工智能 (XAI) 用于本地和全球决策过程的澄清.
  • 通过使用阿拉伯半岛50种植物物种的2500张图像数据集与元数据验证了这一方法.

主要成果:

  • 该XAI模型实现了高性能指标:0.94准确度,0.96精度,0.96回忆和0.97F1-Score.
  • 证明有效的植物物种分类和毒性状况预测.
  • 提供视觉信息,以增强用户对AI模型预测的信心.

结论:

  • 拟议的可解释深层内在学习方法对于植物分类和毒性预测是有效的.
  • 通过XAI增强的解释性促进了对人工智能的更大的信任,用于识别有害植物.
  • 这项研究有助于减少有害植物中毒事件,有利于公共卫生.