indra17/plant_care

🥦 Cauliflower Disease Detection Dataset

A curated computer vision dataset for automatic detection and classification of cauliflower leaf diseases, designed for training and evaluating deep learning models in agricultural and plant pathology applications.

This dataset is suitable for image classification, object detection, and transfer learning workflows and is provided in YOLO-compatible format.

📌 Dataset Overview

Cauliflower crops are highly susceptible to various fungal diseases, insect infestations, and nutrient deficiencies, which significantly impact yield and quality. This dataset aims to support research and development of AI-driven disease detection systems for precision agriculture.

Key features:

• —Real-world field images
• —Multiple disease classes
• —YOLO-style annotations
• —Ready for training with modern detection frameworks

🧪 Classes

The dataset contains 5 distinct classes:

nc: 5
names:
  - alternaria_leaf_spot
  - black_rot
  - downey_mildew
  - insect_infested
  - nutrient_deficiency

📂 Dataset Structure

The dataset follows the YOLO directory format:

cauli_disease/
├── train/
│   ├── images/
│   └── labels/
├── valid/
│   ├── images/
│   └── labels/
├── test/
│   └── images/
└── data.yaml

data.yaml

train: ../train/images
val: ../valid/images
test: ../test/images

🧾 Annotation Format

• —YOLO annotation format
• —Each image has a corresponding .txt file
• —Label format:

  <class_id> <x_center> <y_center> <width> <height>

• —All coordinates are normalized between 0 and 1

🏷️ Source & Licensing

• —Original Dataset Source: Roboflow Universe
• —Project Name: Cauli Disease
• —Version: 4
• —License: CC BY 4.0

🔗 Source URL: https://universe.roboflow.com/computervisionprojects-apdge/cauli_disease/dataset/4

You are free to use, modify, and distribute this dataset, provided appropriate credit is given to the original creators.

🚀 Use Cases

This dataset can be used for:

• —🌱 Plant disease detection
• —📸 Agricultural image classification
• —🤖 YOLO / Faster R-CNN / SSD training
• —🧠 Transfer learning experiments
• —📊 Benchmarking agricultural vision models

🛠️ Example: Training with YOLOv8

yolo detect train \
  data=data.yaml \
  model=yolov8n.pt \
  epochs=100 \
  imgsz=640

📚 Citation

If you use this dataset in academic work, please cite:

  @dataset{cauli_disease,
  	title={Cauliflower Disease Detection Dataset},
 	author={Indra Prasad Sapkota},
	year={2024},
  	url={https://huggingface.co/datasets/indra17/plant_care}
} 

If you use the methodology or benchmark results, please also cite:

@INPROCEEDINGS{11485071,
  author={Sapkota, Indra Prasad and Chhetri, Sneha and Sigdel, Ashish and Paudel, Sijan and Lamichhane, Nabin and Nakarmi, Pranaya},
  booktitle={2026 International Conference on ICT and Photonics (ICTP)},
  title={Cauliflower Disease Detection Using YOLO Models},
  year={2026},
  volume={1},
  pages={1-6},
  doi={10.1109/ICTP67998.2026.11485071}
}

License: MIT ---

🙌 Acknowledgements

• —Roboflow Universe for dataset hosting and annotation tools
• —Contributors and annotators involved in data collection and labeling
• —Open-source community supporting agricultural AI research