Cacao Fruit Images for Moniliasis Detection Using Deep Learning and YOLOv8 in Bagua, Peru

A dataset for detecting Moniliasis disease in cacao fruits, caused by Moniliophthora roreri. It likely contains annotated images for training a YOLOv8 model. The dataset was created by Josep Haamil Gonzalez Herrera and last updated on 2026-05-05.

Use Cases

• Training object detection models to identify Moniliasis-infected cacao fruits based on image annotations.
• Benchmarking deep learning architectures for plant disease recognition in agricultural settings.
• Developing automated monitoring tools for cacao crop health based on visual symptoms described.
• Studying the application of YOLOv8 in a specific agricultural case study in northern Peru.

Strengths

• Focuses on a significant disease (Moniliasis) causing major losses in tropical cacao production.
• Describes a specific methodology (deep learning and YOLOv8) for a concrete case study in Bagua, Peru.
• Has a clear open license (CC-BY-4.0) facilitating reuse.

Limitations

• Dataset size is listed as 0.0 B, indicating the actual data files may not be available or are extremely limited.
• Column-level documentation and sample data are unavailable, making data structure and quality uncertain.
• Row count is unknown, which limits suitability assessment for machine learning tasks.

Provenance

Source

figshare

Collection Method

Likely gathered through field imaging of cacao fruits in Bagua province, Peru.

Freshness

Last updated 2026-05-05 19:14:53; freshness should be verified.

Geography

Bagua province, northern Peru.