AI-powered detection and quantification of post-harvest physiological deterioration (PPD) in cassava using YOLO foundation models and K-means clustering

AI-powered detection and quantification of post-harvest physiological deterioration (PPD) in cassava using YOLO foundation models and K-means clustering

Abstract Background Post-harvest physiological deterioration (PPD) poses a significant challenge to the cassava industry, leading to substantial economic losses. This study aims to address this issue by developing a comprehensive framework in collaboration with cassava breeders. Results Advanced dee...

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Bibliographic Details
Main Authors: Daniela Gómez Ayalde, Juan Camilo Giraldo Londoño, Audberto Quiroga Mosquera, Jorge Luis Luna Melendez, Winnie Gimode, Thierry Tran, Xiaofei Zhang, Michael Gomez Selvaraj
Format: Article
Language:English
Published: BMC 2024-11-01
Series:Plant Methods
Subjects:
Post-harvest physiological deterioration (PPD)
Cassava
Deep learning
Segment Anything Model (SAM)
YOLO models
Image processing
Online Access:https://doi.org/10.1186/s13007-024-01309-w
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https://doi.org/10.1186/s13007-024-01309-w

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