edcelbogs/cassava-7-dataset

🌿 Cassava-Dataset (YOLOv8 Format)

📌 Overview

The Cassava-Dataset is a computer vision dataset designed for plant disease detection in cassava leaves.
It is formatted for YOLOv8 object detection models, making it suitable for training deep learning systems for agricultural disease identification.

This dataset was prepared for research and development in smart agriculture, IoT farming systems, and AI-based plant disease detection.

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🎯 Objective

To enable automated detection and classification of cassava leaf diseases using deep learning models such as:

• YOLOv8 Nano (yolov8n)
• YOLOv8 Small/Medium
• Other object detection architectures

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🧠 Classes

The dataset contains the following classes:

names:

• CBB
• CBSD
• CGM
• CMD
• HEALTHY

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📁 Dataset Structure

cassava-7/ ├── train/ │ ├── images/ │ └── labels/ ├── valid/ │ ├── images/ │ └── labels/ ├── test/ │ ├── images/ │ └── labels/ └── data.yaml

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⚙️ YOLOv8 Configuration

Example data.yaml:

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

names:
 names:
- CBB
- CBSD
- CGM
- CMD
- HEALTHY

🚀 Usage (YOLOv8 Training)

Install dependencies

pip install ultralytics

from ultralytics import YOLO

model = YOLO("yolov8n.pt")

model.train(
    data="data.yaml",
    epochs=50,
    imgsz=640,
    batch=16,
    device=0
)