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Bishal17/
plant_care

Name: plant_care
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🥦 Cauliflower Disease Detection — YOLOv8 Model

📌 Overview

This repository hosts a trained YOLOv8 object detection model for identifying cauliflower leaf diseases and stress conditions from images.

The model was trained on a curated, multi-class agricultural dataset and is suitable for:

Automated crop disease detection
Smart agriculture & decision support systems
Mobile and edge inference
Research in plant pathology & computer vision

The model detects five distinct disease categories from field images and outputs bounding boxes and class labels.

🧠 Model Summary

Attribute	Value
Task	Object Detection
Architecture	YOLOv8-Small
Framework	PyTorch (Ultralytics YOLOv8)
Input Size	640 × 640
Classes	5
Training Epochs	200
Precision	FP32
Export Format	TorchScript

🏷️ Supported Classes

Class ID	Disease / Condition
0	alternaria_leaf_spot
1	black_rot
2	downey_mildew
3	insect_infested
4	nutrient_deficiency

⚙️ Training Configuration

The model was trained using Ultralytics YOLOv8. The exact training arguments used are listed below:

yaml

# Key Training Parameters
task: detect
model: yolov8s.pt
data: cauli_disease-3/data.yaml

# Training Setup
epochs: 200
batch: 32
imgsz: 640
optimizer: auto
pretrained: true
amp: true
seed: 0
deterministic: true

# Optimization
lr0: 0.01
momentum: 0.937
weight_decay: 0.0005
warmup_epochs: 3.0

# Validation & Inference
iou: 0.7
max_det: 300
split: val

# Augmentation
mosaic: 1.0
fliplr: 0.5
auto_augment: randaugment

# Tracking & Output
tracker: botsort.yaml
save_dir: runs/detect/train2

Training outputs and intermediate artifacts were saved under runs/detect/train2/.

📊 Dataset

Dataset: Cauliflower Disease Detection Dataset
Annotation Format: YOLO
Classes: 5 (see Supported Classes)
License: CC BY 4.0

The dataset contains real-field cauliflower leaf images annotated with bounding boxes for disease localization. The dataset used during training is referenced in data above (cauli_disease-3/data.yaml).

🚀 Inference Usage

Using Ultralytics CLI

bash

yolo detect predict \
  model=weights/best.pt \
  source=your_image.jpg \
  imgsz=640

Python Inference Example

python

from ultralytics import YOLO

model = YOLO("weights/best.pt")
results = model("image.jpg", conf=0.25)

# Display or save results
results[0].show()
results[0].save(save_dir='inference_results')

Notes:

Set conf (confidence) threshold to trade off precision vs. recall.
For batch or directory inference, pass a folder path to source.

📦 Repository Contents (suggested)

.
├── best.pt                # Best trained weights (model for inference)
├── last.pt                # Final checkpoint from training
├── args.yaml              # Training configuration 
├── results.png            # Training curves / metrics plot
├── README.md              # This documentation file

📈 Performance Notes

Trained with data augmentation options enabled during training.
Early stopping monitored with patience = 100.
IoU threshold set to 0.7 during validation.

Exact evaluation metrics (mAP@0.5, precision, recall) should be calculated on a held-out test set and can be added to this README once available.

🌍 Deployment Use Cases

Smart farming dashboards
Mobile crop monitoring applications
Drone-based field inspection
Agricultural advisory systems
Edge AI inference pipelines (ONNX / TensorRT conversions possible)

📜 License

This model and its training outputs are released under the MIT license. Attribution is required for use in publications or products.

📖 Citation

If you use this model in academic or applied work, please cite the model and dataset:

bibtex

@model{cauliflower_yolov8_detector,
  title={YOLO based Cauliflower Disease Detection Model},
  author={Indra Prasad Sapkota},
  year={2025},
  url={https://huggingface.co/Bishal17/plant_care}
}

🤝 Acknowledgements

Model trained using Ultralytics YOLOv8.
Dataset annotation and preprocessing assisted by Roboflow or similar annotation tools.
Model hosting by Hugging Face (recommended).

📬 Contact & Contributions

Contributions are welcome (issues / PRs). Possible contributions:

Performance benchmarks and evaluation reports
Edge optimization (quantized, ONNX, TensorRT)
Additional datasets and annotated examples

For contributions or questions, open an issue on the repository or contact the maintainer listed on the Hugging Face project page.