Paygude Priyanka, Thite Sandip, Kumar Ajay, Bhosle Amol, Pawar Rajendra, Mane Renuka, Joshi Rahul, Kasar Manisha, Chavan Prashant, Gayakwad Milind
Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India.
Vishwakarma University, Pune, India.
Data Brief. 2024 Oct 16;57:111024. doi: 10.1016/j.dib.2024.111024. eCollection 2024 Dec.
Indian bay leaf is a crucial spice in Indian cuisine. However, its quality and authenticity are often compromised. To address this, we introduce The Digital Indian Bay leaf dataset, a comprehensive collection of high-resolution 5696 images capturing diverse bay leaf samples under controlled conditions. The dataset encompasses variations in leaf conditions such as fresh leaf, dried leaf and diseased prone leaf. The dataset is meticulously curated to support research in condition analysis and machine learning applications for leaf quality assessment. To ensure data diversity, each category includes a wide range of images captured under controlled conditions with varying lighting, background, and leaf orientation. By providing a standardized and accessible resource, this dataset aims to accelerate research in this domain and contribute to the improvement of the Indian spice industry.
印度月桂叶是印度菜肴中的一种关键香料。然而，其质量和真伪常常受到影响。为了解决这个问题，我们推出了数字印度月桂叶数据集，这是一个全面的高分辨率5696张图像的集合，在可控条件下捕捉了各种月桂叶样本。该数据集涵盖了叶片状况的变化，如新鲜叶片、干燥叶片和易患病叶片。该数据集经过精心策划，以支持叶片质量评估的状况分析和机器学习应用研究。为确保数据多样性，每个类别都包括在不同光照、背景和叶片方向的可控条件下拍摄的大量图像。通过提供一个标准化且可访问的资源，该数据集旨在加速该领域的研究，并为印度香料行业的改进做出贡献。
