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对于叶病分类的含糊性意识的半监督学习
Tri-Cong Pham1,2, Tien-Nam Nguyen3, Van-Duy Nguyen4,5
1Thuyloi University, 175 Tay Son, Dong Da, Hanoi, 10000, Vietnam. phtcong@tlu.edu.vn.
Scientific reports
|April 24, 2025
概括
本研究介绍了一种含糊性意识的半监督学习方法,用于叶病的分类. 它通过拒绝不正确的伪标签来提高准确性,以更少的标签数据实现高精度.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 半监督学习 (SSL) 通过使用标记和未标记数据来增强神经网络训练.
- SSL模型为未标记的数据生成伪标签,但错误可能会降低准确性.
- 叶病分类从准确的模型中受益,但标记的数据往往很少.
研究的目的:
- 开发一种含糊性意识的半监督学习方法,用于精确的叶病分类.
- 通过实施每种疾病的模糊性拒绝算法来提高伪标签质量.
- 减少对植物病理学中大型,完全标记的数据集的依赖.
主要方法:
- 提出了一种含糊性意识的半监督学习方法,用于叶病的分类.
- 开发了一种针对疾病的模糊性拒绝算法,以改进伪标签.
- 在各种数据场景下评估了咖啡和香叶疾病数据集的方法.
主要成果:
- 模糊性拒绝算法显著提高了伪标签的精度.
- 半监督方法实现了与完全监督模型相比的高精度,仅使用50%的标记数据.
- 咖啡的分类精度为99.46%,香叶病的分类精度为100.0%.
结论:
- 拟议的方法有效地减少了在叶病分类中需要广泛的标记数据的需求.
- 拒绝模糊性对于提高半监督学习在这个领域的表现至关重要.
- 这种方法提供了一个可行的解决方案,以有限的数据准确识别植物疾病.
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