豆类图像数据集用于分类
相关实验视频
Updated: Jul 25, 2025

13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
9.2K
Wei Lin1,2, Youhao Fu1, Peiquan Xu1,3
1Nanjing Agricultural University, Nanjing, China.
Data in brief
|June 29, 2023
概括
现在可以获得一个新数据集,包含5000多张大豆种子图像,分为五个质量级别. 该资源有助于开发大豆分类和质量评估的自动化系统.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 数据科学数据科学数据科学
背景情况:
- 对农业经济来说,大豆质量评估至关重要.
- 自动化分类系统需要大量,多样化的数据集.
- 现有的数据集可能缺乏足够多样化的种子质量缺陷.
研究的目的:
- 为了引入单个大豆种子图像的综合数据集.
- 促进自动化大豆分类和质量评估方面的研究.
- 为农业中的图像处理算法提供一个基准.
主要方法:
- 收集并策划了超过5000张大豆种子图像的数据集.
- 种子分为五类:完整的,不成熟的,皮肤受损的,斑点的和破碎的.
- 利用图像处理算法对单个种子进行细分,精度>98%.
主要成果:
- 一个包含每类别1000多张图像的数据集,涵盖五个不同的大豆种子质量等级.
- 从较大的图像 (3072x2048像素) 中成功分割单个大豆种子 (227x227像素).
- 对于单个大豆种子,已经证明了高细分精度 (>98%).
结论:
- 这一数据集对于训练用于大豆质量评估的机器学习模型非常有价值.
- 这种资源可以促进自动化农业检查系统的发展.
- 该数据集支持进一步研究用于作物质量评估的图像分析.
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