A dataset revolutionizing Indian bay leaf analysis
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.
Authors
- Milind Gayakwad (ORCID: https://orcid.org/0000-0002-0430-2626)
- Rajendra P. Pawar (ORCID: https://orcid.org/0000-0002-5861-2080)
- Rahul Joshi (ORCID: https://orcid.org/0000-0002-5871-890X)
- Sandip Thite (ORCID: https://orcid.org/0000-0003-4459-8238)
- Prashant Chavan (ORCID: https://orcid.org/0000-0003-4258-3921)
- Amol A. Bhosle (ORCID: https://orcid.org/0000-0003-4548-7193)
- Priyanka Paygude (ORCID: https://orcid.org/0000-0002-3162-8453)
- Manisha Kasar
- R Mane
- Ajay Kumar
Institutions
- Manipal Academy of Higher Education (IN)
- Symbiosis International University (IN)
- MIT World Peace University (IN)
- MIT Art, Design and Technology University (IN)
- Vishwakarma University
- Bharati Vidyapeeth (Deemed to be University) (IN)
Publication Details
- Journal
- Data in Brief
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1016/j.dib.2024.111024
- Citations
- 6
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 3.60