Food defect detection technologies based on deep learning and prospects in detection of unsound wheat kernels

Rong Wang

doi:10.1016/j.foodchem.2025.146910

Food defect detection technologies based on deep learning and prospects in detection of unsound wheat kernels

2025-11-02

0

PRE

AI

R

Rong Wang

Z

Zhiyao Zhao *

Y

Ying Sun *

Y

Yongbiao Ni

J

Jin Ye

M

Min Zhang

DOI：10.1016/j.foodchem.2025.146910

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Abstract

En 中文

• Proposes a strategy to develop automated, AI-driven food quality detection systems. • AI-assisted cycle to monitor, assess, optimize, and sustain food quality detection. • Reviews deep learning-enhanced food quality sensing across multiple product types. • Highlights the role of machine vision in rapid, non-destructive defect detection • Analyzes challenges in identifying subtle and mixed defects in unsound wheat kernels.

Food defect detection technologies based on deep learning and prospects in detection of unsound wheat kernels

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