| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: objects |
| struct: |
| - name: bbox |
| list: |
| list: float64 |
| - name: categories |
| list: |
| class_label: |
| names: |
| '0': Healthy |
| '1': Mites |
| '2': Virus |
| - name: split |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 89322357 |
| num_examples: 1419 |
| download_size: 92141473 |
| dataset_size: 89322357 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: apache-2.0 |
| task_categories: |
| - object-detection |
| size_categories: |
| - 1K<n<10K |
| --- |
| # Chili Leaf Curl Detection |
|
|
| The dataset comprises real RGB images of chili leaves captured in a mixed agricultural environment at IARI, New Delhi, India, using a handheld smartphone. It depicts natural field conditions for object detection tasks focused on identifying disease symptoms related to chili leaf curl complex. The dataset contains 1,419 images with 12,205 bounding box annotations across 3 categories. |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| The original train/test/val split has been preserved in the `split` column. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{paul2025sca, |
| title={SCA-MobiPlant: smartphone-deployed multistage attention fusion model for accurate field detection of chili leaf curl complex}, |
| author={Paul, Samrat and Emmadi, Venu and Sarkar, Mehulee and Das, Shubhajyoti and Roy, Anirban and Sinha, Parimal}, |
| journal={Plant Methods}, |
| volume={21}, |
| pages={138}, |
| year={2025}, |
| publisher={BioMed Central} |
| } |
| ``` |
|
|
| https://github.com/SamratPaul16/YOLO_DyE |
| |
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
