柳13-3634: 一个新的柳图像数据集和分类方法的评估方法
Shaoye Luo1,2, Hanling Zheng1, Ziyang Lin1
1College of Computer and Data Science, Putian University, Putian, Fujian, China.
PloS one
|October 23, 2025
概括
为了解决缺少基准数据的问题,创建了一个包含13个品种的新李子图像数据集 (Lychee13-3634). 使用此数据集,EfficientNetv2在枝分类中实现了99.90%的准确性.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 精确的枝品种分类对于生产效率和市场供应至关重要.
- 现有的数据集缺乏准确的李分类模型所需的多样性和全面性.
- 李子品种之间较小的类间差异对自动分类构成了挑战.
研究的目的:
- 为基准培训构建一个全面和多样化的柳图像数据集 (Lychee13-3634).
- 为了评估深度学习模型在李奇图像分类上的表现.
- 提供对数据集平衡及其对分类准确性的影响的见解.
主要方法:
- 立花13-3634数据集的构建,包括13种立花品种的3634张图像.
- 20个基于深度学习的高级分类模型的应用和评估.
- 分析数据集平衡及其与模型性能相关性的分析.
主要成果:
- 李子13-3634数据集有效地突出了李子品种之间的微妙差异.
- EfficientNetv2表现出卓越的性能,在枝分类中达到99.90%的准确性.
- 发现数据集平衡对模型分类性能产生了积极的影响.
结论:
- 莱奇13-3634数据集是莱奇图像分类研究的宝贵基准.
- 深度学习模型,特别是EfficientNetv2,对于枝品种识别是有效的.
- 这项研究为未来农产品图像识别研究提供了基础.
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