Synthetic Plant Co-Infection Images for Disease Classification

Synthetic images of plant leaves with co-infections. The dataset is designed for training YOLOv11 object detection models for disease classification tasks. The author, organization, and dataset size are unknown.

Use Cases

• Train YOLOv11 object detection models based on the described synthetic co-infected leaf images.
• Benchmark disease classification algorithms on synthetic data based on the described co-infection scenarios.
• Augment real-world plant disease datasets with synthetic examples based on the described co-infected leaf images.
• Study model performance on multi-disease (co-infection) detection tasks based on the described synthetic data.

Strengths

• Data is specifically designed for a modern object detection framework (YOLOv11).
• Focuses on the complex scenario of co-infections, which may be underrepresented in real-world datasets.

Limitations

• Dataset size and number of images are unknown, which limits suitability assessment.
• Column-level documentation and sample data are unavailable; field semantics must be inferred after download.
• Data is synthetic, which may not fully capture the visual characteristics of real-world plant diseases.

Provenance

Source

Kaggle

Collection Method

Synthetically generated.