IBIA: Indian Biological Images Archive
Image Data Submission Report
Generated on: 25 May 2026
| Project Accession: | IBIAP_1000000018 |
| Title: | Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications |
| Representative Image: |  |
| Description: | Sugarcane, a vital crop for the global sugar industry, is susceptible to various diseases that significantly impact its yield and quality. Accurate and timely disease detection is crucial for effective management and prevention strategies. We persent the “Sugarcane Leaf Dataset" consisting of 6748 high-resolution leaf images classified into nine disease categories, a healthy leaves category, and a dried leaves category. The dataset covers diseases such as smut, yellow leaf disease, pokkah boeng, mosale, grassy shoot, brown spot, brown rust, banded cholorsis, and sett rot. The dataset's potential for reuse is significant. The provided dataset serves as a valuable resource for researchers and practitioners interested in developing machine learning algorithms for disease detection and classification in sugarcane leaves. By leveraging this dataset, various machine learning techniques can be applied, including deep learning, feature extraction, and pattern recognition, to enhance the accuracy and efficiency of automated sugarcane disease identification systems. The open availability of this dataset encourages collaboration within the scientific community, expediting research on disease control strategies and improving sugarcane production. By leveraging the “Sugarcane Leaf Dataset,” we can advance disease detection, monitoring, and management in sugarcane cultivation, leading to enhanced agricultural practices and higher crop yields. |
| Publications: | https://doi.org/10.1016/j.dib.2024.110268 |
| Associated Codes (URL only): | N/A |
| Funding agency: | N/A |
| Grant Number: | N/A |
| Ethics Statement: | Download |
| Any Other Information : | The original version of the dataset is available at Mendeley Data (https://data.mendeley.com/datasets/355y629ynj/1). The Mendeley Data citation is: Thite, Sandip; Suryawanshi, Yogesh; PATIL, Kailas; chumchu, prawit (2023), “Sugarcane Leaf Dataset”, Mendeley Data, V1, doi: 10.17632/355y629ynj.1. |
| 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 | Sandip | Thite | sandip.thite@vupune.ac.in | Vishwakarma University, Pune, India | Unspecified |
| 2 | Yogesh | Suryawansh | N/A | Vishwakarma University, Pune, India | Unspecified |
| 3 | Kailas | Patil | kailas.patil@vupune.ac.in | Vishwakarma University, Pune, India | Principal Investigator |
| 4 | Prawit | Chumchu | prawit@eng.src.ku.ac.th | Kasetsart University, Sriracha, Thailand | Principal Investigator |
| Study Accession: | PPS_1000000023 |
| Title: | Sugarcane leaf dataset |
| Imaging Type: | Plant Photography (PP) |
| Imaging Sub-type: | Not Applicable |
| Summary: | This Sugarcane Leaf Dataset contains a diverse collection of 6748 high-resolution images of sugarcane leaves. The images are stored in JPEG format and have dimensions of 768 Ă— 1024 pixels. The dataset is categorized into 11 distinct classes, including nine disease categories, a healthy leaves category, and a dried leaves category. The disease categories cover a range of common sugarcane leaf diseases, such as smut, yellow leaf disease, pokkah boeng, mosale, grassy shoot, brown spot, brown rust, banded cholorsis, and sett rot. Each category is labelled and organized in separate folders, ensuring easy access and identification of specific disease samples. The images were collected through extensive field surveys conducted in sugarcane-growing regions. The data collection process involved using quality cameras to capture images from various angles, including both sides of the leaves. Images were taken in the field and by cutting/separating individual leaves, capturing different stages and manifestations of the diseases. This approach ensures a comprehensive representation of the visual characteristics of sugarcane leaf diseases within the dataset. The dataset's images are of high quality, with a resolution set at 72 dots per inch (dpi), ensuring clear and detailed visual representation of the sugarcane leaf samples. |
| Keywords: | Classification; Dataset; Deep learning; Disease detection; Image analysis; Leaf diseases; Machine learning; Sugarcane |
| Additional / Any Other Information: | N/A |
| Release Date: | July 16, 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: 6748
| Sample Type ID | Organism | Taxon ID | Biological Entity | Laterality | Source Tissue | Source Cell/Cell-line | Cell Organelle |
|---|---|---|---|---|---|---|---|
| PPSMT_10000000051 | Saccharum officinarum | 4547 | 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_10000000021 | Camera | Smart Phone | Samsung | Galaxy F 23 5 G (SM-E236B) |
| Experimental Design Summary (PPET_10000000021) |
|---|
| The Sugarcane dataset was generated through the acquistion of images using high resolution rear cameras of Samsung F23 5 G Mobile. The summary of the data acquisition steps undertaken for the project includes two steps viz. Step 1: Image Acquisition (Duration: April to June): During this period, field/farm visits were conducted during daytime to capture images. The data collection process involved capturing images under diverse scenarios, encompassing leaves within their natural habitat as well as leaves that had been detached or severed from the plant, all from a distance of 30–50 cm. This deliberate approach aimed to provide a comprehensive and varied representation of sugarcane leaf diseases under different environmental conditions. The objective was to gather a collection of images related to sugarcane leaf diseases. Step 2: Image Pre-processing (Duration: June): In this step, the gathered images were reviewed, and the appropriate images for the dataset were selected. These selected images then underwent pre-processing, which may have included resizing, cropping, and enhancing the images as necessary. The data acquisition process involved capturing images during field visits and subsequently preparing the images for inclusion in the dataset through pre-processing. |
| Acquired Images Annotation Description (PPET_10000000021) |
|---|
| To ensure accurate disease identification, the collected images were forwarded to the Botany Department of Rashtrapita Mahatma Gandhi Arts and Science College in Nagbhid, Chandrapur, India. The department's expertise was leveraged to confirm the disease categories present in the images. Subsequently, the captured images underwent a pre-processing phase, which involved resizing and renaming, facilitated by the utilization of IrfanView software. The resized and renamed images were systematically organized into folders corresponding to their respective disease categories. This curation process enhances the dataset's suitability for scientific analysis and research on sugarcane leaf diseases. |