An Application of Deep Learning Models for the Detection of Cocoa Pods at Different Ripening Stages: An Approach with Faster R-CNN and Mask R-CNN

An Application of Deep Learning Models for the Detection of Cocoa Pods at Different Ripening Stages: An Approach with Faster R-CNN and Mask R-CNN

The accurate classification of cocoa pod ripeness is critical for optimizing harvest timing, improving post-harvest processing, and ensuring consistent quality in chocolate production. Traditional ripeness assessment methods are often subjective, labor-intensive, or destructive, highlighting the nee...

Full description

Saved in:
Bibliographic Details
Main Authors: Juan Felipe Restrepo-Arias, María José Montoya-Castaño, María Fernanda Moreno-De La Espriella, John W. Branch-Bedoya
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Computation
Subjects:
cocoa crop
ripeness detection
smart agriculture
precision agriculture
R-CNN model
artificial intelligence
Online Access:https://www.mdpi.com/2079-3197/13/7/159
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2079-3197/13/7/159

Similar Items