IBIA: Indian Biological Images Archive
Image Data Submission Report
Generated on: 25 May 2026
| Project Accession: | IBIAP_1000000017 |
| Title: | Enhanced deep learning technique for sugarcane leaf disease classification and mobile application integration |
| Representative Image: |  |
| Description: | With an emphasis on classifying diseases of sugarcane leaves, this research suggests an attention-based multilevel deep learning architecture for reliably classifying plant diseases. The suggested architecture comprises spatial and channel attention for saliency detection and blends features from lower to higher levels. On a self-created database, the model outperformed cutting-edge models like VGG19, ResNet50, XceptionNet, and EfficientNet_B7 with an accuracy of 86.53%. The findings show how essential all-level characteristics are for categorizing images and how they can improve efficiency even with tiny databases. The suggested architecture has the potential to support the early detection and diagnosis of plant diseases, enabling fast crop damage mitigation. Additionally, the implementation of the proposed AMRCNN model in the Android phone-based application gives an opportunity for the widespread use of mobile phones in the classification of sugarcane diseases. |
| Publications: | https://doi.org/10.1016/j.heliyon.2024.e29438 |
| Associated Codes (URL only): | N/A |
| Funding agency: | This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. |
| Grant Number: | N/A |
| Ethics Statement: | N/A |
| Any Other Information : | The original version of the dataset is available at Mendeley Data (https://data.mendeley.com/datasets/9424skmnrk/1). The Mendeley Data citation is: Daphal, Swapnil; Koli, Sanjay (2022), “Sugarcane Leaf Disease Dataset”, Mendeley Data, V1, doi: 10.17632/9424skmnrk.1. Please note: At the time of data curation from the Mendeley Data, a total of 2521 images were retrieved by the IBIA data curation team, whereas, the total number of images described in the article are 2569. As suggested by the author, the remaining 48 images will be made available by the corresponding author upon reasonable request. |
| Additional File: | N/A |
| Acknowledgments: | N/A |
| Sr.No | First name | Last name | Organization | Designation | |
|---|---|---|---|---|---|
| 1 | Swapnil | Daphal | daphalsd01@gmail.com | Department of E&TC Engineering, G. H. Raisoni College of Engineering & Management, Wagholi, Pune 412207, Maharashtra, India | Principal Investigator |
| 2 | Sanjay | Koli | N/A | Department of E&TC Engineering, Ajeenkya DY Patil School of Engineering, Charholi Bk., Pune 412105, Maharashtra, India | Co-Investigator |
| Study Accession: | PPS_1000000022 |
| Title: | Sugarcane leaf disease dataset |
| Imaging Type: | Plant Photography (PP) |
| Imaging Sub-type: | Not Applicable |
| Summary: | Manually collected image dataset of sugarcane leaf disease. It has mainly five categories in it. Healthy, Mosaic, Redrot, Rust and Yellow disease. The dataset has been captured with smart phones of various configuration to maintain the diversity. It contains total 2569 images including all categories. This database has been collected in Maharashtra, India. The database is balanced and contains good variety. The image sizes are not constant as it originates form various capturing devices. All images are in RGB format. |
| Keywords: | Deep learning; Disease classification; Agriculture; Sugarcane database |
| Additional / Any Other Information: | N/A |
| Release Date: | June 24, 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: 2521
| Sample Type ID | Organism | Taxon ID | Biological Entity | Laterality | Source Tissue | Source Cell/Cell-line | Cell Organelle |
|---|---|---|---|---|---|---|---|
| PPSMT_10000000050 | 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_10000000020 | Camera | Smart Phone | N/A | N/A |
| Experimental Design Summary (PPET_10000000020) |
|---|
| The study gathered photos of sugarcane leaves in various cultivated fields with various weather conditions. Five classes, including four classes of sick sugarcane leaves and one class of healthy sugarcane leaf samples, made up the initial dataset for sugarcane leaf disease. There were roughly 500 photos in each class, which is a small number. The original photos were downsized to 256x256 to aid computations. The database created in controlled environment fare well in terms of laboratory statistics but fails majorly in real time use cases. To address these challenges database having real time field images have been included in this study. Attempts have been made to maintain variety in terms of orientation, distance, illumination and other environmental condition while collecting the images. Thus, the samples were collected with a preferable degree of variety in terms of rotation, varying light conditions, and even with different capturing devices. Smartphone cameras with different image resolutions were used for the database collection. All images are in .jpg format. |
| Acquired Images Annotation Description (PPET_10000000020) |
|---|
| During the database creation, the labels were assigned by a team of 5 people, including 3 expert farmers with over 10 years of experience in sugarcane cultivation, and 2 agriculture experts holding certified degrees in agricultural sciences plus having over 5 years of experience in plant pathology and relevant areas. |