RoCoLe: A Robusta Coffee Leaf Images Dataset

The RoCoLe (Robusta Coffee Leaf) Dataset is a publicly available dataset designed for plant disease detection and classification in Robusta coffee leaves. It contains 1,560 high-quality images captured under real-world conditions using a smartphone camera, making it ideal for machine learning, deep learning, and computer vision applications in agriculture and plant health monitoring.

Key Features of the Dataset

✔ 1,560 Labeled Coffee Leaf Images – Divided into three key categories:

• Healthy Leaves – Coffee leaves with no disease or damage.
• Coffee Rust Disease – Leaves infected by the coffee rust fungus.
• Spider Mite Infestation – Leaves affected by spider mites, a common pest.

✔ Captured Under Real-World Conditions – Includes natural lighting variations, different backgrounds, and temperature fluctuations.
✔ High-Resolution & Preprocessed – Optimized for machine learning models, ensuring clear visual representation.
✔ Well-Structured Dataset – Images are classified into respective folders for easy access and implementation.
✔ Potential Annotations – Some versions may include leaf object annotations, disease severity levels, and classification metadata.

 Applications of the RoCoLe Dataset

🔹 AI-Powered Plant Disease Detection
🔹 Machine Learning & Deep Learning in Agriculture
🔹 Automated Coffee Leaf Disease Classification
🔹 Precision Farming & Crop Health Monitoring
🔹 Smartphone-Based Disease Recognition Models