干果图像数据集用于机器学习应用程序
相关实验视频
Updated: Jul 23, 2025
13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
9.2K
Vishal Meshram1, Chetan Choudhary1, Atharva Kale1
1Vishwakarma Institute of Information Technology, Pune, India.
Data in brief
|July 13, 2023
概括
这项研究引入了11500多张干果图像的综合数据集,有助于精确分类和识别杏仁,果仁,葡萄和干无花果等品种,用于机器学习应用.
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:
- 干果提供显著的健康益处,包括改善营养和降低疾病风险,使它们成为一种有价值的饮食成分.
- 全球干果市场规模庞大,预计将继续增长,这凸显了这些商品的经济重要性.
- 干果的准确质量评估通常依赖于视觉外观,需要高质量,标签良好的图像.
研究的目的:
- 为机器学习开发一个全面的,高质量的干果图像数据集.
- 为了促进各种干果类型和亚型的分类和识别.
- 支持与干果相关的研究,教育和潜在的医疗应用.
主要方法:
- 收集和处理超过11,500张干果的高质量图像.
- 将图像分为12个不同的类别,包括四种主要类型 (杏仁,果仁,葡萄,干无花果) 和三个子类型.
- 确保图像代表了强大的模型培训的各种条件.
主要成果:
- 这一数据集包括12个不同的干果类别的11,500多张处理过的图像.
- 杏仁,果仁,葡萄和干无花果及其子类型的详细表示.
- 该数据集为开发准确的干果分类模型提供了基础.
结论:
- 创建的数据集是机器学习模型的宝贵资源,旨在分类和识别干果.
- 该资源可以促进干果分析领域的研究,教育和应用.
- 该数据集支持干果行业越来越需要自动化质量评估和识别.
相关概念视频
01:58Fruit Development, Structure, and Function
22.5K
Fruits form from a mature flower ovary. As seeds develop from the ovules contained within, the ovary wall undergoes a series of complex changes to form fruit. In some fruits, such as soybeans, the ovary wall dries; in other fruits, such as grapes, it remains fleshy. In some cases, organs other than the ovary contribute to fruit formation; such fruits are called accessory fruits.
22.5K
00:56Extraction: Advanced Methods
493
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
493