Food Science and Engineering
机器学习在采后水果品质评估中的应用
Machine Learning for Postharvest Fruit Quality AssessmentA data-driven approach utilizing multi-source sensing data and machine learning algorithms to detect and analyze postharvest damage and storage deterioration in apples. / 基于多源传感数据和机器学习算法，用于检测和分析苹果采后损伤及贮藏劣变的数据驱动方法。
- Knowledge type
- 实际应用
- First proposed or discovered
- Proposer
- Turkey Farm member
- Creator
- 农场主
- Last updated
- 2026-10-08 07:54:04
Sub-knowledge points
Nondestructive perceptionPostharvest damage detectionStorage deterioration analysis
Higher-order knowledge points
Food Quality ControlSmart Agriculture
Main parameters and performance indicators
sensing data: multi-source optical and environmental dataalgorithm: machine learning modelstarget: apples
Open problems
No unresolved problem recorded yet
Related literature
ranked by community starsNondestructive perception of postharvest damage and storage deterioration analysis of apples based on multi-source sensing data and machine learningOpen source
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1 recordsRecord title: Machine Learning for Postharvest Fruit Quality Assessment.
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