JujubeBruiseNet:一个高分辨率的图像数据集用于检测毛里求斯的伤
Md Arham Tabib1, Sumyia Sabrin Liza1, Md Mizanur Rahman1
1Department of Computer Science and Engineering, Faculty of Science & Information Technology, Daffodil International University, Birulia, Dhaka 1216, Bangladesh.
Data in brief
|October 27, 2025
概括
一个新的数据集,JujubeBruiseNet,有助于检测朱果上的伤. 本资源支持自动化质量评估,减少人工劳动和提高农业效率.
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:
- 在Ziziphus mauritiana (jujube) 中检测伤对于收获后的质量控制至关重要.
- 手动伤评估是劳动密集型的,容易出现错误.
- 需要自动化系统来提高水果加工的效率和准确性.
研究的目的:
- 介绍JujubeBruiseNet,这是一个新的高分辨率图像数据集,用于检测朱果中的伤.
- 为开发用于农业质量评估的先进计算机视觉模型提供资源.
- 促进自动化水果分类和分类方面的研究.
主要方法:
- 收藏了1464个Ziziphus mauritiana水果的原始图像.
- 将图像分类为"健康"和"受伤"等级.
- 在受控的照明条件下捕捉到的图像,以确保统一性.
主要成果:
- 朱珠布鲁斯网数据集包括1464个标记图像.
- 该数据集旨在训练机器学习模型,包括卷积神经网络 (CNN).
- 它可以开发精确的伤识别系统.
结论:
- 朱布鲁斯网是农业研发的宝贵资源.
- 这一数据集可以显著改善朱果的自动伤检测.
- 它支持计算机视觉的进步,用于水果质量评估和分类.
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