Non-destructive detection of blueberry skin pigments and intrinsic fruit qualities based on deep learning.
Journal:
Journal of the science of food and agriculture
PMID:
33211339
Abstract
BACKGROUND: This paper proposes a novel method to improve accuracy and efficiency in detecting the quality of blueberry fruit, taking advantage of deep learning in classification tasks. We first collected 'Tifblue' blueberries at seven different stages of maturity (10-70 days after full bloom) and measured the pigments of the blueberry skin and the total sugar and the total acid of the pulp. We then established a skin pigment contents prediction network (SPCPN), based on the correlation between the pigments and blueberry pictures, and also a fruit intrinsic qualities prediction network (FIQPN), based on the correlation between the pigments and fruit qualities. Finally, the SPCPN and FIQPN were consolidated into the blueberry quality parameters prediction network (BQPPN).