IBIA: Indian Biological Images Archive
Image Data Submission Report
Generated on: 26 May 2026
| Project Accession: | IBIAP_1000000026 |
| Title: | Dataset of Centella Asiatica leaves for quality assessment and machine learning applications |
| Representative Image: |  |
| Description: | Centella asiatica is a significant medicinal herb extensively used in traditional oriental medicine and gaining global popularity. The primary constituents of Centella asiatica leaves are triterpenoid saponins, which are predominantly believed to be responsible for its therapeutic properties. Ensuring the use of high-quality leaves in herbal medicine preparation is crucial across all medicinal practices. To address this quality control issue using machine learning applications, we have developed an image dataset of Centella asiatica leaves. The images were captured using Samsung Galaxy M21 mobile phones and depict the leaves in “Dried,” “Healthy,” and “Unhealthy” states. These states are further divided into “Single” and “Multiple” leaves categories, with “Single” leaves being further classified into “Front” and “Back” views to facilitate a comprehensive study. The images were pre-processed and standardized to 1024 × 768 dimensions, resulting in a dataset comprising a total of 9094 images. This dataset is instrumental in the development and evaluation of image recognition algorithms, serving as a foundational resource for computer vision research. Moreover, it provides a valuable platform for testing and validating algorithms in areas such as image categorization and object detection. For researchers exploring the medicinal potential of Centella asiatica in traditional medicine, this dataset offers critical information on the plantʼs health, thereby advancing research in herbal medicine and ethnopharmacology. |
| Publications: | https://doi.org/10.1016/j.dib.2024.111150 |
| Associated Codes (URL only): | N/A |
| Funding agency: | N/A |
| Grant Number: | N/A |
| Ethics Statement: | Download |
| Any Other Information : | The dataset is collected from a specific region, potentially limiting its applicability to other geographical areas with different disease prevalence or manifestations. |
| Additional File: | N/A |
| Acknowledgments: | We are grateful to Kasetsart University Sriracha Campus, Thailand and Vishwakarma University, Pune for their support and provision of necessary resources during this research endeavour. |
| Sr.No | First name | Last name | Organization | Designation | |
|---|---|---|---|---|---|
| 1 | Rohini | Jadhav | N/A | Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India | Unspecified |
| 2 | Mayuri | Molawade | N/A | Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India | Unspecified |
| 3 | Amol | Bhosle | N/A | MIT Art, Design and Technology University, Pune, India | Unspecified |
| 4 | Yogesh | Suryawanshi | N/A | Vishwakarma University, Pune, India | Unspecified |
| 5 | Kailas | Patil | kailas.patil@vupune.ac.in | Vishwakarma University, Pune, India | Principal Investigator |
| 6 | Prawit | Chumchu | prawit@eng.src.ku.ac.th | Kasetsart University, Sriracha, Thailand | Principal Investigator |
| Study Accession: | PPS_1000000031 |
| Title: | Image Dataset of Centella Asiatica for quality assessment and machine learning applications |
| Imaging Type: | Plant Photography (PP) |
| Imaging Sub-type: | Not Applicable |
| Summary: | This Centella asiatica dataset is structured into three main folders representing various states of leaf health: Dried (2996 images), which are organized into Single (2018 images) and Multiple (985 images) subfolders. Unlike the healthy and unhealthy categories, the Single images of dried leaves are not divided into front and back views due to the shriveled nature of dried leaves, which obscures these distinctions., Healthy (3048 images) and is further divided into Single (2044 images) and Multiple (1004 images) subfolders. The Single subfolder contains images of individual leaves and is further categorized by leaf orientation into Front (1014 images) and Back (1030 images) views , and Unhealthy (3050 images), this folder is similarly organized into Single (2024 images) and Multiple (1016 images) subfolders. The Single images are further divided into Front (1023 images) and Back (1011 images) views. This organization enables machine learning models to classify leaves based on health status, facilitating plant quality assessment and real-time health monitoring in agricultural systems. Each main folder is further divided to capture detailed leaf views, thereby supporting complex image recognition and classification tasks. This comprehensive dataset not only facilitates Centella asiatica identification but also supports a range of machine learning applications in plant health assessment, quality control, and agricultural decision-making. It is poised to aid researchers in developing machine learning models for the detection of adulteration in herbal products, a critical need in ensuring the authenticity of herbal medicine. Additionally, the dataset's structured diversity in health states and orientations makes it an invaluable resource for computer vision researchers focused on fine-grained plant image classification and the development of robust, application-ready models. By offering categorized, high-quality images, this dataset provides a foundation for advancing both applied research and the broader fields of ethnobotany and agricultural informatics. |
| Keywords: | Artificial intelligence; Brahmi; Medicinal plant; Quality assessment; Machine learning |
| Additional / Any Other Information: | N/A |
| Release Date: | Nov. 14, 2025 |
| Access Licence Type: | Open Access |
Table 1. The sample types registered under this study are as follows:
The total number of samples registered under this study is: 9094
| Sample Type ID | Organism | Taxon ID | Biological Entity | Laterality | Source Tissue | Source Cell/Cell-line | Cell Organelle |
|---|---|---|---|---|---|---|---|
| PPSMT_10000000062 | Centella asiatica | N/A | Leaf | Not Applicable | N/A | N/A | N/A |
Table 3. The experiment types registered under this study are as follows:
| Experiment Type ID | Instrument Name | Instrument Type | Manufacturer | Model |
|---|---|---|---|---|
| PPET_10000000028 | Camera | Smart Phone | Samsung | Galaxy M21 |
| Experimental Design Summary (PPET_10000000028) |
|---|
| Leaves from Centella asiatica 50 plants were gathered from the herbal garden located at Vishwakarma University Pune (coordinates: 18°27′34.8″N 73°53′01.1″E) between January 2024 and March 2024. A total of 50 individual plants were specifically chosen for leaf collection to compile the dataset. Approximately 400 individual leaves were selected for each category (Healthy, Unhealthy, Dried). Healthy leaves with a moisture content below 5 % were classified as ‘dried’, with moisture levels determined using the oven-drying method. Leaves of Centella asiatica exhibiting any visual discoloration, aside from the green hue, were categorized as unhealthy. Conversely, leaves that were uniformly green, without any discoloration, were classified as healthy. The distinction between healthy and unhealthy leaves was based solely on visual appearance. Images of leaves from each selected plant were taken under varying backgrounds, angles, and lighting conditions. To ensure image uniformity, pre-processing was conducted using FastStone Photo Resizer software. This tool was employed to systematically adjust the dimensions of the input images according to specified resolution parameters. This process standardized the resolution across all images in the dataset, promoting consistency and uniformity. Such pre-processing is crucial in data preparation as it enhances image quality and ensures compatibility for subsequent analyses and applications. We reduced the image resolution from 4000 × 3000 pixels to 1024 × 768 pixels to enhance computational efficiency, reduce model training time, and ensure consistency for machine learning applications. The resultant images were saved in JPG format and resized to a resolution of 1024 × 768 pixels, with all files renamed using numerical identifiers only. |
| Acquired Images Annotation Description (PPET_10000000028) |
|---|
| The leaves from 50 plants of Centella asiatica were collected from the Vishwakarma University campus in Pune. Three types of leaf conditions were considered for image creation: Dried, Healthy, and Unhealthy. Total 9094 high-resolution images captured using a Samsung Galaxy M21 Android phone with a 12-megapixel sensor, 4.6 mm focal length, F2 aperture, and 4000 × 3000 resolution, featuring leaf images taken under different backgrounds, angles, and lighting conditions. These images were divided into “Single” and “Multiple” leaf categories, with “Single” leaves being further classified into "Front" and “Back” views to enable a comprehensive study. The images were pre-processed and standardized to 1024 × 768 dimensions, resulting in a dataset comprising a total of 9094 images in 1024 × 768 dimensions. |